Mistral Release Notes

Leanstral 1.5

Leanstral 1.5, a free Apache-2.0 licensed model with 6B active parameters, delivers a major performance upgrade in formal verification, saturating miniF2F, solving 587/672 PutnamBench problems, and achieving state-of-the-art results on FATE-H (87%) and FATE-X (34%). Trained through mid-training, supervised fine-tuning, and reinforcement learning with CISPO, it excels in agentic proof engineering and real-world code verification, uncovering 5 previously unknown bugs across 57 repositories tested. Fully open-sourced and available via Hugging Face and a free API, Leanstral 1.5 is now accessible for practical proof engineering in Lean 4.

Since its launch, Leanstral has offered an open, practical approach to proof engineering in Lean 4. Today, we are releasing Leanstral 1.5, a free Apache-2.0 licensed model with 119B total and only 6B active parameters, delivering a performance upgrade that makes formal verification more powerful and accessible than ever.

Leanstral 1.5 saturates miniF2F, solves 587/672 PutnamBench problems, and achieves a new state-of-the-art of 87% on FATE-H and 34% on FATE-X. Beyond benchmarks, it verifies complex code properties and uncovers previously unknown bugs in open-source repositories—proving that rigorous formal methods can be both effective and practical for real-world use.

Training Leanstral

Leanstral 1.5 goes through a three-stage process: mid-training, supervised fine-tuning, and reinforcement learning with CISPO. Leanstral 1.5 leverages extensive training on two RL environments:

In the multiturn environment, the model is given a theorem statement and must either prove or disprove it. The model submits a proof, receives Lean compiler feedback, and refines its approach with each attempt. If the proof compiles it succeeds; otherwise the loop continues until the model either solves the problem or exhausts its budget.

In the code agent environment, Leanstral operates like a developer in a raw filesystem: it edits files, runs bash commands, and uses the Lean language server to inspect goals, errors, and type information in real time. This allows it to tackle long-horizon tasks like completing partial proofs in a repository, building auxiliary lemmas, and persisting through multiple rounds of context compaction. The model learns to navigate the full proof-engineering workflow and is finally verified by our fork of SafeVerify for correctness given a list of target theorems.

Evaluation

We evaluate Leanstral on the following benchmarks:

• miniF2F is a cross-system benchmark for formal mathematics, ranging from elementary problems to IMO-level challenges, testing diverse proof abilities across algebra, combinatorics, and number theory.
• PutnamBench consists of 672 problems from the Putnam Mathematical Competition, requiring deep reasoning and long proof chains to solve challenging mathematical problems.
• FATE-H and FATE-X are abstract algebra benchmarks for graduate and PhD-level problems, respectively, testing advanced reasoning in areas like group theory, ring theory, and module theory.
• FLTEval is based on real pull requests from the Fermat’s Last Theorem repository, testing practical proof engineering with real-world complexity.

We saturate miniF2F completely, reaching 100% on both the validation and test sets. On PutnamBench and FATE-H/X, we compare Leanstral 1.5 against Goedel-Architect without natural-language guidance, Seed-Prover 1.5 at its high setting, and AxProverBase. Leanstral reaches a new state-of-the-art on FATE-H/X, solving 87 and 34 problems respectively. On PutnamBench, it edges out Seed-Prover 1.5 high by 7 problems at far lower cost: about $4 per problem, against an estimated $300 or more for Seed-Prover, whose high setting runs with a budget of 10 H20-days per problem. The only provers ranked higher operate under different conditions—some receive natural-language proof guidance, others cost far more to run, like Aleph Prover at $54–68 per problem.

Leanstral 1.5 shows the strongest test-time scaling we have seen from a formal-reasoning model. The figure below tracks Pass@8 on PutnamBench as we raise the token budget per attempt from 25k to 4M: performance climbs smoothly and monotonically the whole way, from 44 problems solved at 50k to 244 at 200k, 493 at 1M, and 587 at 4M. Rather than giving up when a proof runs long, Leanstral keeps reasoning, editing files, and revising across millions of tokens, turning that budget directly into solved problems—the same behavior behind the AVL-tree proof below, which ran for over 2.7 million tokens across 22 compactions.

With this release, we also fully open source FLTEval. Leanstral 1.5 lifts pass@1 on the benchmark from 21.9 to 28.9 and pass@8 from 31.9 to 43.2, surpassing Opus 4.6's 39.6 at one-seventh the cost. It also widens its lead over open-source models 3–10× larger, as shown in the figure below.

Code Verification Case Studies

While being primarily trained for mathematics, Leanstral 1.5 exhibits strong abilities in code verification. We present 2 critical case studies to demonstrate its impact.

AVL Trees: Proving Time Complexity

AVL trees are self-balancing binary search trees that maintain O(log n) height through rebalancing during insertions and deletions. Leanstral 1.5 proved these time complexity guarantees for a real implementation—a task that required structural induction to mirror the tree’s recursive structure, careful handling of monadic time tracking, and exhaustive case analysis for rebalancing paths. Over 2.7 million tokens and 22 compactions, Leanstral systematically unfolded each layer of the TimeM monad, exposing the underlying computations despite their interleaving with control flow. It established an almost tight bound of 48 steps per height unit plus a constant for insertion, then connected height to tree size via a logarithmic relationship, delivering complete, verified proofs that insertion and deletion are indeed O(log n).

Bug Discovery: Finding Hidden Flaws

To test Leanstral’s bug-catching abilities, we built an automated pipeline: Aeneas translates Rust code to Lean, while Leanstral infers the user intent and generates correctness properties from the code. Leanstral then attempts to prove each property in four attempts. If they all fail, it tries to prove the negation instead, also with four attempts. Across 57 tested repositories, this process flagged 47 violated properties, with 11 pointing to genuine bugs—5 of them previously unreported on GitHub.

One such bug was in the sign function for zigzag decoding of the datrs/varinteger library. On input Std.U64.MAX, the expression (value + 1) overflowed, causing crashes in debug mode and silent corruption in release mode—an edge case that testing and fuzzing would typically miss. Leanstral’s pipeline caught it automatically, demonstrating that formal verification can already be applied to real-world codebases and find bugs that some traditional methods overlook.

Get Started

Leanstral 1.5 has a Apache-2.0 license. The weights can be found on Huggingface, while also being available now as a free API endpoint as leanstral-1-5. We recommend using it in Mistral Vibe. To begin your journey, grab an API Key, and:

1. Set up Mistral Vibe

uv tool install mistral-vibe
uv tool update mistral-vibe
vibe --setup

1. Install Leanstral 1.5

/leanstall
exit

1. Launch the agent

vibe --agent lean

1. Install Lean LSP MCP (Optional)

It is highly recommended to install Lean LSP MCP by adding the following to your ~/.vibe/config.toml

[[mcp_servers]]
name = "lean-lsp"
transport = "stdio"
command = "uvx"
args = ["lean-lsp-mcp"]
tool_timeout_sec = 600

If there are no existing MCP servers, you may have to remove mcp_servers = [].

1. Start proving

Ask Leanstral to tackle a theorem, debug a proof, or contribute to a repository. It’s that simple.

What's Changed

• Add chat templates integration by @juliendenize in #163
• Deprecate RawAudio in favor of str | bytes by @juliendenize in #227
• Generalize normalizer aggregation with dual separators by @juliendenize in #235
• Consolidate multimodal ContentChunk support for all message roles by @juliendenize in #241
• Fix AudioChunk.to_openai() serialization for raw audio bytes and prefixed base64 audio strings. by @haoruilee in #245
• Resolve tokenizer from local cache in offline mode by @sarathfrancis90 in #249
• Remove to_openai and from_openai from InstructRequest by @juliendenize in #251
• Bump version to 1.11.4 by @juliendenize in #252

New Contributors

• @haoruilee made their first contribution in #245
• @sarathfrancis90 made their first contribution in #249

Full Changelog: v1.11.3...v1.11.4

Today, we are introducing several new capabilities in Connectors for a more secure experience integrating to external enterprise platforms. Starting now, you can use:

• Enriched admin controls (GA) to set connector access per workspace and switch individual tools on or off across an org or workspace
• API keys with connector scopes (GA) to prevent impersonation in automated AI workloads that integrate with 3rd-party systems
• Multi-account connectors (GA) allowing users to authenticate to a single connector with multiple accounts
• Connectors Debugger (Public Preview) for end-to-end root cause analysis for MCP connectors
• Connectors in Vibe Code (GA) to reuse your connectors in developer interfaces
• Connectors in Workflows (Public Preview) allow for uninterrupted long-running tasks powered by all the tools you need

Async agents are moving into everyday work. For an agent to be trustworthy and useful inside an organization, it needs real enterprise data: CRM records, repositories, inboxes, knowledge bases. Connecting an agent to that data in a demo is easy. Running it in production is where most setups stall.

Production connectivity has a few non-negotiables. A connector should respect two sets of rules at once: the permissions already set in the source platform, and the controls your administrators set in Mistral Studio or Vibe. Automated work should run on behalf of a user or a service account, never impersonate the person who wrote it. When a connection breaks, you should be able to find out why. And an administrator should be able to decide, down to the individual tool, what is available in each part of the organization.

More control over what connectors can do

Enriched admin controls work at two levels: across your teams, and inside each connector.

• Workspace and org controls let you give each team its own connector access. Your finance workspace can reach internal data sources with no open web access, while engineering gets developer tools and the internet. Same directory, different rules per team.
• Tool-level controls go a step deeper. Inside any connector, turn individual tools on or off for a whole org or one workspace. Block anything that writes data, or one specific action like delete_file on a knowledge base. The connector stays connected; only the tools you approve can run.

API keys with connector scope handle identity. When you create a key, you choose whether it reaches only a workspace's shared connectors or your private ones too, so an automated job runs with exactly the access it needs. Paired with service accounts, automated work runs as a defined identity, never as the person who wrote it.

Multi-account connectors let one connector hold more than one login. Connect a personal and a work account to the same connector, set a default, and switch between them per task. Each account is stored and refreshed on its own, so an agent acts in the right one without you standing up a second connector.

See why a connection fails

Connectors Debugger tells you exactly where a connection breaks. Point it at an MCP server URL, add OAuth credentials if the server needs them, and run the check. It walks the connection through 11 steps, from reaching the server to opening the MCP session, and logs each one. A failure that used to mean guesswork, like a broken OAuth token exchange, shows up at the exact step it happens.

Connectors in Workflows and Vibe for Code

Connectors in Workflows keep long and scheduled runs from breaking on auth. You declare the connectors a Workflow needs, and the accounts it should run with, right in the Workflow definition; Studio resolves those dependencies when you start a run. Personal credentials stay out of automated work, and a job won't fail halfway because a token expired. A nightly run can pull from a CRM, update a tracker, and read a knowledge base across the whole job without dropping a connection.

Connectors in Vibe Code bring the same governed access to the coding agent. Type /connectors to pick from local MCP servers and the workspace connectors your admins approved, choose which tools to enable, and start a task. Async agents can reach Outlook, Jira, Notion, Linear, and Confluence under the same rules you set everywhere else.

We're adding connectors continuously. The directory now covers more than 60 integrations, and when the platform you need isn't listed, a custom MCP connector fills the gap.

Start building

Enterprise controls, Connector Credentials, and Connectors in Vibe Code are generally available now. Connectors Playground and Connectors in Workflows are in public preview. All of it is live in Studio.

• Connectors documentation
• Connectors in Vibe Code documentation
• Connectors in Workflows documentation and cookbooks
• Open the Studio console

Today, we're releasing Mistral OCR 4, featuring bounding boxes, block classification, and inline confidence scores alongside extracted text. The model supports 170 languages across 10 language groups, runs in a single container for fully self-hosted deployments, and serves as an ingestion component for enterprise search, RAG, and domain-specific retrieval pipelines. OCR 4 is a small, focused model, and this post covers what's new, how it performs on public and internal benchmarks, the known limitations of those benchmarks, and guidance on when to use the model API versus Document AI.

Highlights

• Breakthrough performance. Independent annotators prefer OCR 4 over every leading OCR and document-AI system tested, with win rates averaging 72%, alongside the top overall score on OlmOCRBench (85.20). See Benchmarks below for methodology and known scoring limitations.
• Segmentation, not just text. Alongside the extracted text, OCR 4 returns bounding boxes, typed-block classification (titles, tables, equations, signatures, and more), and inline confidence scores. Bounding boxes, our most-requested capability, localize text for in-context highlighting and reliable data pipelines. At the same time, block types and confidence scores drive source-grounded citations, redactions, and human-in-the-loop verification.
• Integrated with Mistral Search Toolkit (public preview). OCR 4 is an ingestion component of Search Toolkit, Mistral's open-source, composable search framework, announced at the AI Now Summit. Its structured output supplies citation-ready inputs to the toolkit's ingestion, retrieval, and evaluation workflow for RAG and enterprise search.
• Multilingual coverage. Support for 170 languages across 10 language groups, with measurable gains on specialized and low-resource languages where several competing systems degrade.
• Run on your own infrastructure. OCR 4 is compact enough to deploy on a single container, keeping document data in your environment for residency, sovereignty, and compliance, while supporting cost-efficient, high-throughput batch processing. Self-managed deployment is available to enterprise customers.

Overview

Mistral OCR 4 extracts and structures content from a wide range of documents. Where previous generations focused on converting a page into clean text and tables, OCR 4 returns a structured representation of the document. Each block is localized with a bounding box, classified by type, and inline confidence scores are generated per-page and per-word. Downstream systems, therefore, have access not only to what the document says but also to where each element sits, what role it plays, and how confident the model is in each region.

This structure supports several downstream workloads:

• Semantic chunking for RAG: clean, classified blocks become better retrieval units.
• Structural primitives for agents: agents move from reading documents to acting on them (form filling, invoice processing, compliance checks).
• Structured content for connectors: consistent, typed output for ingestion and indexing pipelines.

OCR 4 accepts common enterprise formats, including PDF, DOC, PPT, and OpenDocument, and supports 170 languages across 10 language groups, including specialized and low-resource languages that many systems handle poorly. As a compact model deployable in a single container, it is suited to both cost-sensitive and high-volume deployments. It can run fully self-hosted, allowing organizations with data-sovereignty requirements to keep document data within their own infrastructure.

Developers integrate the model via API, and teams can use Document AI in Mistral Studio for an application-level, no-code path to the same engine. Mistral OCR 4 through the API is priced at $4 per 1,000 pages, with a 50% Batch-API discount, reducing the cost to $2 per 1,000 pages. Document AI is priced at $5 per 1,000 pages.

Benchmarks

“We benchmarked Mistral OCR 4 against the leading agentic document parsers across a chart and figure dense financial QA dataset and reached equivalent accuracy at roughly 8x lower cost and 17x lower latency. For production use cases at scale, that delta compounds fast.” - Aidan Donohue, AI Engineer, Rogo

To evaluate OCR 4, we compared it against leading AI-native OCR models, frontier general-purpose models, enterprise document services, and our own Mistral OCR 3.

Human Preference Evaluations

Automated benchmarks carry the scoring artifacts described above, so we complemented them with a head-to-head human evaluation on documents chosen to reflect real usage. We assembled 600+ documents across 12+ languages, sourced from third-party vendors to represent real industry use cases, and asked independent annotators to blindly rank each competitor's output against OCR 4's, document by document.

Annotators preferred OCR 4 in the majority of documents across all systems tested. Because these are human judgments on realistic documents rather than string comparisons against fixed references, they sidestep much of the annotation and formatting noise that affects automated scores.

Overall Performance

“Mistral OCR is roughly 4x faster per page than our incumbent provider, an impressive result for the high-volume docketing workflows where speed is critical to managing our customers' IP timelines.” - Ivan Mihailov, AI engineer, Anaqua

In addition to placing first in our human preferences, OCR 4 achieves the top overall score amongst the models we tested on the public OlmOCRBench (85.20) and leads our internal Crawl Multilingual evaluation (.98), ahead of both AI-native and enterprise solutions.

On OmniDocBench, OCR 4 achieves a score of 93.07. We report this figure with a caveat: both OlmOCRBench and OmniDocBench have known limitations in how they score certain outputs, and a single aggregate number can both understate and overstate real-world performance.

When we audited the mismatches behind our scores, most were not model errors but artifacts of how the benchmarks compare output. The recurring categories:

• Ground-truth errors. Some reference annotations are themselves incorrect: missing or extra text, transcriptions of redacted regions, or typos (for example, a cited author's name misspelled in the reference but read correctly by the model from the page). The output matches the source document, yet it is still marked wrong.
• Equivalent math notation. Different LaTeX that renders identically is counted as a mismatch, The rendered equation is correct; the string comparison is not.
• Equation segmentation. Whether an expression is emitted as a single equation or split into several inline fragments affects the match, even when the rendered content is identical, because the matcher cannot align the pieces.
• Multi-column reading order. Words split across a column boundary (for example, "certifi-cates") and column-ordering assumptions cause correct extractions to be scored as reading-order failures.
• Block-type attribution. The benchmark does not expect headers/footers in the output. To resolve this we strip headers footers from our output before scoring. But the test then checks for a string that also happens to be the title of the page which should actually be present and flags it incorrectly.

These artifacts concentrate in mathematical, scientific, and multi-column documents, and they more often penalize correct output than reward incorrect output. We therefore treat the aggregate score as directional rather than definitive.

These benchmarks are directional. All competitor scores reflect internal reproductions. We recommend evaluating on your own documents.

Performance Details

Crawl Multilingual breakdown. On our internal multilingual evaluation, OCR 4 leads across all eight language groups — English, Western Europe, Eastern Europe, Middle Eastern, Chinese, East Asian, Southeast Asian, and specialized languages (Hindi, Japanese, Georgian, Bengali, Armenian, Hebrew, Greek, Gujarati, Tamil, Malayalam, Kannada, Telugu). The gap is widest for specialized and low-resource languages, where many competing systems degrade sharply, while OCR 4 maintains high accuracy.

Recommended use cases

OCR 4 supports both high-volume pipelines and interactive document workflows, including:

• Document parsing and extraction: complex, multilingual documents.
• Retrieval-Augmented Generation (RAG): structured, classified, citation-ready content for semantic chunking and source-grounded answers. With Search Toolkit, OCR 4 output can be fed directly into retrieval pipelines.
• Agentic workflows: providing agents with the structural primitives to complete tasks such as form filling, invoice processing, and compliance checks, especially in legal, financial services, and healthcare.
• Structured data pipelines using confidence scores to enable efficient use of human verifiers: form/invoice extraction, redactions, and compliance-driven processes.
• Enterprise search and knowledge bases: OCR as a data-source component for custom ingestion and entity extraction.

Early users are applying OCR 4 to turn invoices into structured fields, digitize company archives, extract clean text from technical and scientific reports, and power enterprise search.

A note on out-of-scope use.

OCR 4 is a document-understanding model, not a decision-maker. It is not intended for medical diagnosis, legal advice or judgment, high-stakes financial decisions, safety-critical systems, real-time/latency-sensitive processing, or non-document inputs (raw audio, video, etc.).

OCR 4 API: Understanding Your Options

Mistral's OCR 4 is available through a single API endpoint. Every request runs the same underlying OCR model and always returns extracted content, bounding boxes, block types, confidence scores, and markdown-structured text. What varies is how much you layer on top.

Use OCR 4 in pure extraction mode when you want to:

• Embed fast, accurate document extraction directly into your application, agent, or data pipeline.
• Work directly with the raw response, bounding boxes, block types, and confidence scores to drive custom downstream logic.
• Run high-volume or batch ingestion with full control over throughput and cost via the Batch API.
• Self-host for strict data-privacy, sovereignty, or compliance requirements.

Activate Document AI capabilities (same endpoint, additional parameters) when you want to:

• Return structured JSON in a schema you define — pass a JSON schema alongside your document, and the OCR output is fed to mistral-small-2603 to generate content shaped to your spec.
• Annotate detected images with structured JSON by passing an image annotation schema, triggering an additional vision-language model call per image.
• Use a custom prompt alongside a JSON schema to guide how the extracted content of the full document is interpreted or summarized.
• Enable business users, solutions teams, or pilots to produce structured results without writing downstream parsing logic.

The practical decision rule: if you need raw extracted content, use OCR 4 as-is. If you need the output reshaped into a structured format, annotated with domain-specific fields, or processed with a custom instruction, add the Document AI parameters to the same call. You always get the OCR result regardless; Document AI simply adds structured layers on top of it.

Now available

“The availability of Mistral Document AI with OCR 4 in Microsoft Foundry marks an important milestone in our partnership. Together, we’re enabling customers to bring advanced, structured document understanding directly into their AI workflows, combining Mistral’s innovation with Microsoft’s enterprise platform to deliver scalable, trusted solutions for real-world business needs.” -Kimmi Grewal, VP, AI Ecosystem Partnerships, Microsoft

Both Mistral OCRv4 and Document AI (powered by OCRv4) are available via API through Mistral Studio, Amazon SageMaker, Microsoft Foundry, and coming soon Snowflake Parse Document. For organizations with stringent data-privacy requirements, OCR 4 also offers a self-hosting option so sensitive information stays within your own infrastructure. To explore self-deployment, let us know.

Get started

We offer a few ways to get started and learn more quickly.

• Try OCR 4. The new Getting Started with OCR 4 Cookbook walks through a first extraction, working with bounding boxes, and block classification.
• OCR 4 webinar. We'll cover what's new in OCR 4 with demos and Q&A on July 7th at 6:00 PM CET. Register for the OCR4 in Production webinar.
• Contact Sales for more information.

We released OCR 4 (mistral-ocr-4-0). mistral-ocr-latest now points to it.

MODEL RELEASED

Introducing include_blocks in our OCR API. When set to true, each page returns a blocks array with paragraph-level bounding boxes and a structural label (text, title, list, table, image, equation, caption, code, references, aside_text, header, footer, signature) in reading order. Learn more in our OCR documentation.

API UPDATED

The pages parameter in our OCR API now also accepts a string of comma-separated digits and ranges (e.g. "0,1,2", "0-5", or "0,2-4") in addition to a list of integers.

API UPDATED

What's Changed

• Raise multiple format of reasoning for from_openai by @juliendenize in #224
• Preserve zero OpenAI seed in chat request conversion by @pragnyanramtha in #226
• Pin uv required-version and bump pre-commit hook by @juliendenize in #228
• Add to_openai reasoning format for AssistantMessage by @juliendenize in #223
• fix(tokenizer): point users at from_hf_hub on unknown model (#229) by @NishchayMahor in #231
• Fix: forward continue_final_message in tekken normalizers (V7/V15) by @matdou in #233
• Version 1.11.3 by @juliendenize in #239

New Contributors

• @pragnyanramtha made their first contribution in #226
• @NishchayMahor made their first contribution in #231
• @matdou made their first contribution in #233

Full Changelog: v1.11.2...v1.11.3

Here’s a summary of everything we’re announcing at AI Now Summit.

Mistral for Industrial Engineering

An integrated AI stack combining advanced physics models, engineering expertise, and robotics to transform mission-critical industrial operations. The solution enables industrial engineers to accelerate design, eliminate simulation bottlenecks, and optimize asset performance; while maintaining full control over proprietary data, IP, and production environments.

With Airbus, we are implementing advanced AI at the core of the company's operations and processes, from initial design to on-board capabilities. Our partnership will expand across Airbus' commercial aircraft, helicopter, defence, and space activities. It will support the next decade of innovation, contribute to improve flight safety, and maintain full control of critical data; all while adhering to strict security requirements.

For BMW Group, we are a central partner for their “Large Industry Model” (LIM) initiative. The collaboration unifies engineering knowledge and AI expertise to build multimodal reasoning models on engineering data for complex development use cases such as crash simulation.

ASML has started working with us to tackle challenging engineering use cases, such as optimizing the design of high-performance parts, surrogate models and control loops, with the intent to demonstrate that AI grounded in deep domain expertise, system knowledge and real-world constraints can create real value in advanced semiconductor environments.

On May 22, we announced our acquisition of Emmi, bringing advanced scientific capabilities to enhance our offerings for industrial engineering companies. Physics AI will redefine how manufacturers in aerospace, automotive, and semiconductors innovate, enabling them to design, simulate, and produce at unprecedented speed.

Read our blog on introducing physics AI at Mistral.

Learn more about Mistral for manufacturing.

Vibe: a unified agent for long-horizon productivity

Vibe is now one agent for long-running, multi-step work. It catches up across your inbox and calendar, runs deep research, drafts deliverables, and orchestrates the recurring processes that run your day-to-day.

It also takes coding work from request to merged change, across the web app, your editor, and your terminal. The agent builds features, fixes bugs, refactors code, and ships reviewable pull requests.

Vibe runs on flagship Mistral models optimized for reasoning, agentic tasks, tool calls, and coding.

Read more about the new Vibe in our blog post.

Les Ulis data center

The Les Ulis site (Essonne) is a new 10 MW facility dedicated to inference operations. Scheduled to open in Q3 2026, the site will address compute supply chain risks by providing direct control over capacity, providing greater security and transparency as training and inference hardware converge.

Let’s keep building together

Today’s announcements at the AI Now Summit are important milestones in our mission to build full-stack AI solutions for enterprises and governments. Across industrial engineering, agentic productivity, and secure infrastructure, we are focused on helping organizations deploy AI where it matters most: critical workflows, with measurable impact, and with full control over their data and operations.

Highlights

1. Le Chat is now Vibe—one agent and one licence across work and code, with every conversation, setting, and plan carried over.
2. Work Mode, available on web and mobile, is a powerful agent for long-range tasks that picks the right tools, streams progress, and completes complex work to finish.
3. Code Mode launches remote coding agents from a dedicated web surface.
4. A new Mistral Vibe extension for VS Code; the coding agent working across your whole project, inside your IDE.

Vibe for work

Vibe is now one agent for long-running, multi-step work. It catches up across your inbox and calendar, runs deep research, drafts deliverables, and orchestrates the recurring processes that run your day-to-day.

It also takes coding work from request to merged change, across the web app, your editor, and your terminal. The agent builds features, fixes bugs, refactors code, and ships reviewable pull requests.

Vibe runs on flagship Mistral models optimized for reasoning, agentic tasks, tool calls, and coding.

In Work Mode, Vibe is your AI agent for complex, multi-stage tasks, fluent in your knowledge, apps, and tools. It maps out a plan and gets your sign-off before it starts, then works across your connectors to carry the task through.

For one person, this turns a morning of admin into a single prompt—catch up on what you missed, pull the numbers, draft the update, and have it ready to send on. Whereas, for the organisation, the same agent runs the processes that keep a business moving, grounded in the documents, mailboxes, and systems your teams already use, and governed by the permissions you set at the admin-level.

Here is what Vibe’s Work Mode does today.

• Enterprise knowledge search: Vibe deeply grounds its work in your context, reaching across Google Workspace, Outlook, SharePoint, Slack, GitHub, and any custom connectors or libraries.
• Structured data analysis: connect a database or upload a spreadsheet, and Vibe surfaces the patterns, anomalies, and signals you asked about, rendering charts and dashboards inside the conversation.
• Document and report synthesis: Vibe drafts the deliverable using the Canvas tool, from a one-page brief to a report, an RFP response, or a board deck, ready for you to edit and push to Notion, SharePoint, or your inbox.
• Multi-step task scheduling: set a prompt to run once or on a daily, weekly, or monthly cadence, and a notification lands when a run finishes.
• Reusable skills: extend Vibe with preset or custom skills via open standards to automate your repeatable workflows with consistency and sequential precision.

Every step is visible as it happens, and each tool call and reasoning chain is expandable to its inputs and outputs.

Vibe for code

Code Mode is the new coding surface in the Vibe web app, where you can connect to GitHub, manage your projects, start sessions, and see them through to a pull request. Inside a session, the agent runs in an isolated sandbox, and you manage sensitive actions and inspect diffs as it’s writing code.

Sessions can run in parallel, can persist while your machine is off, and can be triggered from third-party apps, such as Slack (coming in June)—in addition to your code editor or the Vibe CLI.

The new Mistral Vibe extension for VS Code

We’re also releasing a new plugin that brings the Vibe coding agent into VS Code. Vibe works across your whole project in a side panel that reads, edits, and executes commands beside your files. Open files attach automatically, selections can be line-ranged and @ mentions will pull in more context from other directories or files.

The extension runs on the same harness as the CLI, and features are replicated across both surfaces:

• write unit and integration tests that match your existing patterns, and document what it ships, from the README down to inline comments;
• refactor and translate, moving a module to a new pattern, or a legacy file to a more modern language, with behaviour preserved and tests completed;
• connect to your entire stack, pulling context from GitHub, GitLab, Jira, or Linear, so a change arrives with the issue it resolves and the conventions your team follows.

Updates to Vibe CLI

The Vibe CLI is getting several important updates, including more session controls.

• Skills turn repeatable workflows into / commands.
• Custom modes and subagents route specialised work within a session.
• The agent's plan is editable before it runs, and it can ask multiple choice questions mid-run.
• Permissions are session-scoped (always, never, or ask), including overrides for files, commands, and directories.
• /teleport moves a live session between your terminal and the cloud, keeping history and approvals intact.

Get started with Vibe

Vibe is live at chat.mistral.ai. Download the mobile app from the App Store and Google Play. If you were on Le Chat, your plan, history, and settings are already there within the Chat mode. *

• Free: quick answers and simple everyday tasks.
• Pro, $14.99/month: complex tasks, deeper reasoning, and all-day coding.
• Team, $24.99/user/month: a shared workspace with admin controls and more storage.
• Enterprise: custom deployments, model training, and dedicated solutioning.

See Vibe’s plans to learn more.

For code, Vibe is on the web at chat.mistral.ai/code, in VS Code through the new extension, and in your terminal with the CLI:

curl -LsSf https://mistral.ai/vibe/install.sh | bash
uv tool install mistral-vibe

You can also build through the API in Mistral Studio, with the free Experiment plan for testing and prototyping.

• We will soon sunset Chat mode; rest assured, all of your Le Chat history will continue to be preserved in the Work Mode.

Today, we're releasing Search Toolkit in public preview. Search Toolkit is a composable framework for building production search pipelines for AI applications. We built it because teams building search infrastructure still spend too much engineering time on plumbing. Most stitch together separate tools for ingestion, retrieval, and evaluation, each with its own interface and its own assumptions about data. Search Toolkit brings all three into a single framework with a shared interface, so teams spend their time improving search quality instead of maintaining integrations. Search Toolkit is open source and runs wherever your infrastructure does. Cloud, on-premises, edge.

Search infrastructure is still harder than it should be.

Most teams building retrieval systems spend more time assembling infrastructure than improving search quality. Ingestion requires one set of tools. Retrieval requires another. Evaluation, if it happens at all, is bolted on with a separate framework and separate assumptions about data shape.

Teams report weeks of integration work before they can run a single query against their own data. Measuring whether the retriever is returning the right results often requires yet another toolchain. For organisations building RAG workflows or internal knowledge systems, that overhead multiplies at every layer.

Where it fits.

Enterprise search.

Most organisations don't have a search problem. They have a dozen search problems. Internal wikis, support ticket systems, document repositories, file storage, codebases. Each source has different structure, different metadata, and needs different processing to index well. Teams typically end up building a separate ingestion pipeline for each one, with its own parsing logic, its own chunking strategy, and its own assumptions about what a "document" looks like. The result is a set of isolated indexes that can't be searched together, or a brittle custom layer that tries to unify them and becomes its own maintenance burden. Search Toolkit provides consistent processing and indexing patterns across source types within a single framework, so teams add new sources without rebuilding the pipeline each time.

RAG and retrieval quality.

When a RAG system returns poor results, the first question is whether the problem is retrieval or generation. In practice, most teams have no clean way to answer that. They tweak prompts, adjust chunking strategies, and swap models without knowing whether the retriever is surfacing the right context in the first place. And even teams that do focus on retrieval often lack the tooling to compare strategies rigorously, on their own data, with their own relevance judgments. The alternative is writing custom evaluation scripts for each experiment. Search Toolkit includes built-in evaluation that measures retriever performance independently, so you can isolate retrieval quality from generation quality and compare configurations as your corpus evolves.

Domain-specific retrieval.

Legal filings, medical records, codebases, financial disclosures. Off-the-shelf retrievers are trained on general-purpose text and tend to struggle with specialised terminology, document structures, and relevance criteria that differ from web search. Teams that need domain-tuned retrieval often end up building custom retrieval infrastructure from scratch, which is expensive to maintain and hard to evaluate.

Search in an agentic world

Agents working on enterprise tasks need access to enterprise context. They make retrieval decisions autonomously and at high volume, so the quality of the search infrastructure underneath them directly affects every downstream step. For searching across large document corpora, agents perform semantic search on an index, which gives them precise results at low latency.

Agents also need live data. With Connectors, they pull directly from source systems like CRMs, code repositories, and productivity tools through MCP integrations. An agent can query an indexed corpus when it needs to search across a large body of content, and pull live data from a source system when it needs the latest state. Search Toolkit gives your agents a high-quality indexed search path to call on alongside live retrieval.

What's inside.

Ingestion.

Index and process data from multiple sources with configurable pipelines. Search Toolkit handles document parsing, chunking, and embedding generation. Custom document formats and preprocessing steps plug in through a standard adapter interface.

Retrieval.

Search Toolkit ships with BM25 sparse retrieval, dense embedding-based retrieval, and hybrid configurations that combine both. Each is configurable to your data and use case.

Evaluation.

Measure search quality with built-in metrics: recall, precision, MRR, and NDCG. Run evaluations against your own test sets, compare retriever configurations side by side, and track quality across releases.

All modules share a common configuration interface. Replace your indexer, swap your retriever, add an evaluator. The rest of the pipeline adapts.

Search Toolkit has been designed for advanced use cases for the enterprise, and battle tested across financial services, manufacturing, public sector, and media & entertainment verticals. CMA CGM uses Search Toolkit alongside Voxtral to help journalists detect fake news. The pipeline processes audio from three distinct data sources and returns alerts within 15 seconds end to end.

Watch the demo

Get started.

The fastest way to try Search Toolkit is with our starter app template.

Prerequisites

Install Docker. You also need uv in the generated project.

Scaffold a new project

uvx copier copy gh:mistralai/search-starter-app my-search-project
cd my-search-project

Run it

# Start Vespa locally with Docker
make setup-vespa
# Index sample data
make ingest path=sample_data/hello.txt
# Run a query
make search query="hello world"

The template includes:

• Pre-configured Vespa indexing
• Hybrid retrieval (BM25 + vector)
• Sample data and ingestion pipeline

For full details, see the starter app README.

What’s next

Once you’ve tried the starter app, dive deeper:

• Tune your ingestion pipeline – Configure parsers, chunking strategies, embedding models, and extractors for specific file types to handle your data sources.
• Manage Vespa schema & relevance – Optimize indexing and ranking profiles for your use case.
• Build your dream retrieval – Leverage advanced features like LLM query rewriting, reranking, and hybrid retrieval.

For the full reference, see the Search Toolkit documentation.

Highlights

1. Le Chat is now Vibe—one agent and one licence across work and code, with every conversation, setting, and plan carried over.
2. Work Mode, available on web and mobile, is a powerful agent for long-range tasks that picks the right tools, streams progress, and completes complex work to finish.
3. Code Mode launches remote coding agents from a dedicated web surface.
4. A new Mistral Vibe extension for VS Code; the coding agent working across your whole project, inside your IDE.

Vibe for work

In Work Mode, Vibe is your AI agent for complex, multi-stage tasks, fluent in your knowledge, apps, and tools. It maps out a plan and gets your sign-off before it starts, then works across your connectors to carry the task through.

For one person, this turns a morning of admin into a single prompt—catch up on what you missed, pull the numbers, draft the update, and have it ready to send on. Whereas, for the organisation, the same agent runs the processes that keep a business moving, grounded in the documents, mailboxes, and systems your teams already use, and governed by the permissions you set at the admin-level.

Here is what Vibe’s Work Mode does today.

• Enterprise knowledge search: Vibe deeply grounds its work in your context, reaching across Google Workspace, Outlook, SharePoint, Slack, GitHub, and any custom connectors or libraries.
• Structured data analysis: connect a database or upload a spreadsheet, and Vibe surfaces the patterns, anomalies, and signals you asked about, rendering charts and dashboards inside the conversation.
• Document and report synthesis: Vibe drafts the deliverable using the Canvas tool, from a one-page brief to a report, an RFP response, or a board deck, ready for you to edit and push to Notion, SharePoint, or your inbox.
• Multi-step task scheduling: set a prompt to run once or on a daily, weekly, or monthly cadence, and a notification lands when a run finishes.
• Reusable skills: extend Vibe with preset or custom skills via open standards to automate your repeatable workflows with consistency and sequential precision.

Every step is visible as it happens, and each tool call and reasoning chain is expandable to its inputs and outputs.

Vibe for code

Code Mode is the new coding surface in the Vibe web app, where you can connect to GitHub, manage your projects, start sessions, and see them through to a pull request. Inside a session, the agent runs in an isolated sandbox, and you manage sensitive actions and inspect diffs as it’s writing code.

Sessions can run in parallel, can persist while your machine is off, and can be triggered from third-party apps, such as Slack (coming in June)—in addition to your code editor or the Vibe CLI.

The new Mistral Vibe extension for VS Code

We’re also releasing a new plugin that brings the Vibe coding agent into VS Code. Vibe works across your whole project in a side panel that reads, edits, and executes commands beside your files. Open files attach automatically, selections can be line-ranged and @ mentions will pull in more context from other directories or files.

The extension runs on the same harness as the CLI, and features are replicated across both surfaces:

• write unit and integration tests that match your existing patterns, and document what it ships, from the README down to inline comments;
• refactor and translate, moving a module to a new pattern, or a legacy file to a more modern language, with behaviour preserved and tests completed;
• connect to your entire stack, pulling context from GitHub, GitLab, Jira, or Linear, so a change arrives with the issue it resolves and the conventions your team follows.

Updates to Vibe CLI

The Vibe CLI is getting several important updates, including more session controls.

• Skills turn repeatable workflows into / commands.
• Custom modes and subagents route specialised work within a session.
• The agent's plan is editable before it runs, and it can ask multiple choice questions mid-run.
• Permissions are session-scoped (always, never, or ask), including overrides for files, commands, and directories.
• /teleport moves a live session between your terminal and the cloud, keeping history and approvals intact.

Get started with Vibe

Vibe is live at chat.mistral.ai. Download the mobile app from the App Store and Google Play. If you were on Le Chat, your plan, history, and settings are already there within the Chat mode. *

• Free: quick answers and simple everyday tasks.
• Pro, $14.99/month: complex tasks, deeper reasoning, and all-day coding.
• Team, $24.99/user/month: a shared workspace with admin controls and more storage.
• Enterprise: custom deployments, model training, and dedicated solutioning.

See Vibe’s plans to learn more.

For code, Vibe is on the web at code.mistral.ai, in VS Code through the new extension, and in your terminal with the CLI:

curl -LsSf https://mistral.ai/vibe/install.sh | bash
uv tool install mistral-vibe

You can also build through the API in Mistral Studio, with the free Experiment plan for testing and prototyping.

Search Toolkit in public preview

Today, we're releasing Search Toolkit in public preview. Search Toolkit is a composable framework for building production search pipelines for AI applications. We built it because teams building search infrastructure still spend too much engineering time on plumbing. Most stitch together separate tools for ingestion, retrieval, and evaluation, each with its own interface and its own assumptions about data. Search Toolkit brings all three into a single framework with a shared interface, so teams spend their time improving search quality instead of maintaining integrations. Search Toolkit is open source and runs wherever your infrastructure does. Cloud, on-premises, edge.

Search infrastructure is still harder than it should be.

Most teams building retrieval systems spend more time assembling infrastructure than improving search quality. Ingestion requires one set of tools. Retrieval requires another. Evaluation, if it happens at all, is bolted on with a separate framework and separate assumptions about data shape. Teams report weeks of integration work before they can run a single query against their own data. Measuring whether the retriever is returning the right results often requires yet another toolchain. For organisations building RAG workflows or internal knowledge systems, that overhead multiplies at every layer.

Where it fits.

Enterprise search.

Most organisations don't have a search problem. They have a dozen search problems. Internal wikis, support ticket systems, document repositories, file storage, codebases. Each source has different structure, different metadata, and needs different processing to index well. Teams typically end up building a separate ingestion pipeline for each one, with its own parsing logic, its own chunking strategy, and its own assumptions about what a "document" looks like. The result is a set of isolated indexes that can't be searched together, or a brittle custom layer that tries to unify them and becomes its own maintenance burden. Search Toolkit provides consistent processing and indexing patterns across source types within a single framework, so teams add new sources without rebuilding the pipeline each time.

RAG and retrieval quality.

When a RAG system returns poor results, the first question is whether the problem is retrieval or generation. In practice, most teams have no clean way to answer that. They tweak prompts, adjust chunking strategies, and swap models without knowing whether the retriever is surfacing the right context in the first place. And even teams that do focus on retrieval often lack the tooling to compare strategies rigorously, on their own data, with their own relevance judgments. The alternative is writing custom evaluation scripts for each experiment. Search Toolkit includes built-in evaluation that measures retriever performance independently, so you can isolate retrieval quality from generation quality and compare configurations as your corpus evolves.

Domain-specific retrieval.

Legal filings, medical records, codebases, financial disclosures. Off-the-shelf retrievers are trained on general-purpose text and tend to struggle with specialised terminology, document structures, and relevance criteria that differ from web search. Teams that need domain-tuned retrieval often end up building custom retrieval infrastructure from scratch, which is expensive to maintain and hard to evaluate.

Search in an agentic world

Agents working on enterprise tasks need access to enterprise context. They make retrieval decisions autonomously and at high volume, so the quality of the search infrastructure underneath them directly affects every downstream step. For searching across large document corpora, agents perform semantic search on an index, which gives them precise results at low latency.

Agents also need live data. With Connectors, they pull directly from source systems like CRMs, code repositories, and productivity tools through MCP integrations. An agent can query an indexed corpus when it needs to search across a large body of content, and pull live data from a source system when it needs the latest state. Search Toolkit gives your agents a high-quality indexed search path to call on alongside live retrieval.

What's inside.

Ingestion.

Index and process data from multiple sources with configurable pipelines. Search Toolkit handles document parsing, chunking, and embedding generation. Custom document formats and preprocessing steps plug in through a standard adapter interface.

Retrieval.

Search Toolkit ships with BM25 sparse retrieval, dense embedding-based retrieval, and hybrid configurations that combine both. Each is configurable to your data and use case.

Evaluation.

Measure search quality with built-in metrics: recall, precision, MRR, and NDCG. Run evaluations against your own test sets, compare retriever configurations side by side, and track quality across releases.

All modules share a common configuration interface. Replace your indexer, swap your retriever, add an evaluator. The rest of the pipeline adapts.

Search Toolkit has been designed for advanced use cases for the enterprise, and battle tested across financial services, manufacturing, public sector, and media & entertainment verticals. CMA CGM uses Search Toolkit alongside Voxtral to help journalists detect fake news. The pipeline processes audio from three distinct data sources and returns alerts within 15 seconds end to end.

Watch the demo

Get started.

The fastest way to try Search Toolkit is with our starter app template.

Prerequisites

Install Docker. You also need uv in the generated project.

Scaffold a new project

uvx copier copy gh:mistralai/search-starter-app my-search-project
cd my-search-project

Run it

# Start Vespa locally with Docker
make setup-vespa
# Index sample data
make ingest path=sample_data/hello.txt
# Run a query
make search query="hello world"

The template includes:

• Pre-configured Vespa indexing
• Hybrid retrieval (BM25 + vector)
• Sample data and ingestion pipeline

For full details, see the starter app README.

What’s next

Once you’ve tried the starter app, dive deeper:

• Tune your ingestion pipeline – Configure parsers, chunking strategies, embedding models, and extractors for specific file types to handle your data sources.
• Manage Vespa schema & relevance – Optimize indexing and ranking profiles for your use case.
• Build your dream retrieval – Leverage advanced features like LLM query rewriting, reranking, and hybrid retrieval.

For the full reference, see the Search Toolkit documentation.

Engineering ambition has rarely been greater than it is today. Defense readiness, the energy transition, the push towards sustainable aviation, the need to scale AI data centers, and next-generation chips: every one of these developments depends on engineering teams shipping more capable hardware, faster—with thinner margins for error.

And yet physics analysis remains stuck at the front of the product lifecycle, tied to solver methods that haven't fundamentally changed in decades. Engineers still evaluate a handful of variants when they should be exploring thousands. And once a product is in operation, engineers lose the physics insight they had at design time, because the solvers behind it are too slow to keep up with live data.

We believe physics deserves its own frontier AI models. That's why we've brought Emmi AI into Mistral. In this post, we share what physics AI is, why it matters now, and what it makes possible for our partners like ASML, Airbus, Safran, and Siemens Energy.

We are building out a new foundational capability inside Mistral's enterprise solutions for AI-native industrial engineering—alongside our existing models, our tools for building and operationalizing agentic workflows, and the secure deployment and integration enterprises require.

Together they form a single stack spanning the engineering lifecycle: deployed where the customer needs it, integrated with their environment, fully under their control. Find out more about our AI for manufacturing offering.

The limits of traditional simulation: why engineering is inherently slow

When running these “numerical physics simulations,” engineers use computers to predict how physical systems behave by solving partial differential equations. They are the language of physics: they describe how fluids flow, how structures deform, how heat moves. Rather than building and testing every prototype in the real world, engineers solve the governing physics equations on a computer by dividing an object into millions of tiny pieces and calculating what happens at each one.

A typical CFD or FEM workload looks much the same in 2026 as it did in 2006: prepare CAD geometry, discretize it into a mesh, configure boundary conditions, queue the run on an HPC cluster, wait. The result is a workflow that is slow, taking hours to weeks of compute time per design variant, and expensive: HPC capacity, solver licenses and specialist expertise gate the number of simulations that are being run. True design-space exploration is mathematically possible but economically impossible at this cost and tempo.

The consequence is structural. Engineers iterate on a handful of designs when they should be exploring thousands. Many teams settle for "good enough" because "optimal" is unaffordable in compute and calendar time. Every downstream constraint—manufacturability, certification, cost—compounds in terms of time and cost.

What is physics AI?

Data-driven physics AI is a class of AI models that learn from physics solver outputs and predict physical behavior directly from geometry and boundary conditions, or even measurement data. It maps inputs to full physical fields in a single forward pass, on the order of seconds, on a single GPU.

A few clarifications about what physics AI is not:

• It is not a replacement for first-principles solvers in every regime. It is a step-change in throughput for the vast majority of design-loop iterations, with traditional solvers reserved for verification and edge cases.
• It is not an LLM trained on simulation data. The architectures, training objectives and evaluation regimes are fundamentally different.
• It is not a regression on a single geometry. The point of physics AI is geometric and parametric generalization – one model serving an entire design family, not one model per part.

Now that model architectures allow for industrial scale (see e.g. AB-UPT) and GPUs have become powerful and accessible enough to train and serve physics workloads at production economics, it is the right point to double down from a research and solutions perspective.

What physics AI unlocks

Once inference moves from hours to seconds, both the engineering and operation of products reorganize around what's suddenly possible.

Accelerated product design

This is about the hardware itself—the car body, the wing, the chip package, the motor.

What becomes possible:

• Thousands of design variants explored in the time a single simulation used to take
• AI models that propose design candidates, not just evaluate them
• Simulation earlier in the process and usable beyond specialists

What it delivers:

• Better-performing products at the same development cost
• Shorter time from concept to validated design
• Fewer expensive surprises late in development

Accelerated tooling and process design

This is about how the product is made—the molds, dies, fixtures, and process settings that turn a design into a manufactured part. Tooling geometry, materials, and process parameters together determine quality, cost, and yield.

What becomes possible:

• Thousands of tooling variants explored in the time a single simulation used to take
• Tooling geometry and process parameters optimized together, not in sequence
• Manufacturing defects predicted before any tool is cut

What it delivers:

• Faster tool development
• Higher yield and fewer scrapped parts
• Shorter ramp-up to stable production

Real-time digital twins

A digital twin is a virtual model of a physical asset—a turbine, a power grid, a battery, a chemical reactor—that mirrors its behavior.

What becomes possible:

• Continuous physics predictions on live sensor data
• Models that update in real time as the asset operates
• What-if scenarios on running assets, without taking them offline

What it delivers:

• Predictive maintenance before failures occur
• Higher operational efficiency across the asset’s lifetime
• Extended asset life and deferred capex on replacement

Where physics AI applies

Physics AI is a horizontal capability with vertical impact. This is a non-exhaustive map of where it is creating immediate value:

Aerospace: external aerodynamics, structural analysis, thermal management, propulsion, aeroelasticity.
Automotive: vehicle aerodynamics, crashworthiness, battery thermal management, motor design.
Electronics & semiconductors: chip and package thermal analysis, signal and power integrity, data-center and rack cooling, lithography optics.
Energy & utilities: wind and gas turbine design, grid-equipment optimizations, reactor thermal-hydraulics, subsurface flow and reservoir simulation.
Industrial equipment: heat exchangers, pumps and compressors, electric motors, tooling design.

The same model class, retrained or fine-tuned on the relevant physics, transfers across these domains.

Part of an enterprise platform for the AI-native industrial engineering lifecycle

We believe that physics AI is most valuable when it composes with the rest of an engineering organization's AI stack. That is why we ship it as one capability inside Mistral's enterprise platform, alongside:

• Language and multimodal reasoning models
• Model training and customization pipelines
• AI workflow design, orchestration and monitoring tools
• Unified AI productivity and coding agent
• Private AI infrastructure
• And expert services to accelerate your AI-native transformation

We’re building the first fully integrated AI stack that rethinks traditional engineering workflows end-to-end: Engineers define the intent and verify outcomes - the stack executes in between. The result: manufacturers explore orders of magnitude more design candidates, build the next generation of products faster, and maintain continuous performance gains across operational assets at scale.

Get started

If you're building the next generation of aircraft, vehicles, energy systems or electronics—and you're tired of waiting on the solver—we'd like to hear from you.

We also opened new roles to build out our AI 4 Engineering team. Apply here!