From raw model to shipped product — one environment to design, build, test, and deploy AI applications without stitching together a dozen disconnected tools.
Building an AI application sounds simple until you try it. The integration work between scattered tools dwarfs the AI itself — and it's where quality quietly leaks.
A model here, a vector store there, prompts buried in a repo, evaluation in a spreadsheet, deployment by hand. The seams between those parts are where time is lost.
When prompts live in one place, data in another, and evaluation nowhere at all, no one can say with confidence why the application behaves the way it does.
Moving from prototype to production requires fragile glue code, manual processes, and custom infrastructure — just to ship something that might already be broken.
foundry.ms brings the full AI application workflow under one roof — from first prototype to hardened production. The same project carries you from rough idea to the version real users depend on.
The distance between an idea and a working, trustworthy product shrinks from weeks of integration work to an afternoon of building.
A complete platform across every stage of the AI application lifecycle.
Design prompts, connect data sources, and wire up tool calls in one integrated workspace instead of juggling five disconnected products.
Start with any model and swap it later without rebuilding the application around it. Compare models on your own test cases and keep the version that wins.
Prompts, configurations, and data are treated like real code — fully versioned, diffable, and reversible. Change anything and know exactly what you changed.
Measure quality against real cases before a single user sees output. Catch regressions automatically. Flag hallucinations, unsafe responses, and off-policy behavior early.
Connect documents, databases, and APIs as the knowledge foundation for your application. Keep the app's knowledge in sync as your sources change, with permissioned access throughout.
Trace every request and watch real-world quality after launch. Set alerts on quality, cost, and latency so issues surface immediately — not after users complain.
Explore each stage of building with foundry.ms — from first prototype to production-grade application.
Build through an approachable interface — connect your model, wire up data sources, craft and version prompts. Drop into code for fine-grained control whenever you need it. Fast iteration: change a setting and see the effect immediately.
Run repeatable evaluations against your own real-world test cases. Compare models, prompts, and retrieval settings side by side. Catch regressions, hallucinations, and unsafe responses before they reach production.
Move from prototype to production in the same environment — no fragile handoff, no custom glue. Guardrails and access controls ship with the application. Release gradually and roll back instantly if something looks wrong.
Trace every request. Monitor quality, cost, and latency in real time. Set alerts so issues surface immediately. Spot regressions early and iterate safely — the production loop lives in the same environment as the build loop.
A direct comparison of assembling an AI stack from scattered parts versus building in one place.
| Dimension | Building It Yourself | With foundry.ms |
|---|---|---|
| Tooling | Patchwork of disconnected tools | ✓ One integrated environment |
| Prompts & Config | Scattered files, hard to track | ✓ Versioned like code |
| Evaluation | Manual, ad hoc, often skipped | ✓ Built in and repeatable |
| Deployment | Custom glue and fragile handoffs | ✓ Seamless, same environment |
| Production Visibility | Bolted on after the fact, if at all | ✓ Observability from day one |
| Swapping Models | Rebuild the app around new model | ✓ Swap without rebuilding |
| Who Can Build | Specialists only | ✓ Engineers + product + domain experts |
A unified foundry doesn't just save time — it changes who gets to build and what gets to production.
Stop building throwaway scaffolding. The same project that starts as a Monday prototype becomes the hardened, observable app real users depend on by the end of the week.
Evaluation isn't a nice-to-have you run once — it's a repeatable discipline built into every iteration. Ship with confidence because you measured before you deployed.
When the plumbing is handled, product managers and domain experts can shape AI applications directly instead of waiting in a queue behind a small number of specialists.
A single foundry moves an organization from a handful of AI experiments to AI built into every product and workflow — a repeatable engineering discipline, not a one-off effort.
foundry.ms handles every category of AI application a modern team needs to ship.
Build copilots that answer from your own documents, databases, and systems — so employees get instant, accurate answers grounded in your actual knowledge, not the internet.
Embed AI assistants and question-answering directly in your product, fully grounded in your knowledge base and governed by your policies.
Build apps that read, extract, classify, and summarize documents at volume — with the accuracy you've measured and the observability to know when quality drifts.
Build agents that plan, call tools, and complete real tasks end-to-end — with guardrails that scope what they can do and full traceability of every decision.
Replace keyword search with question-answering grounded in your knowledge base — answers that are current, accurate, and traceable to a source.
Automate multi-step workflows where AI makes judgment calls at each decision point — with the evaluation history and observability to prove the decisions are sound.
foundry.ms connects to the models, data sources, and tools your team already uses. No lock-in — your data stays where it already lives.
Governance, observability, and access controls are built into the platform — not bolted on after the fact.
The application only ever sees the data it is authorized to use. Your data stays where it already lives.
Control who can edit, evaluate, and deploy each application. Separate concerns cleanly across product, engineering, and domain teams.
Every prompt and configuration change is tracked and reversible. Keep a complete record of what shipped, when, and by whom — audit-ready by default.
Scope what the application can do and govern who can change it. Guardrails deploy alongside the application — not as a separate layer.
Trace every request through the application end to end. Know exactly what data was retrieved, which prompt ran, and what the model returned — for every call.
Promote proven components into a shared library the whole organization builds on. Standardize how AI is built across every team without mandating uniformity.
The bottleneck in AI isn't the models — it's everything around them. Stop assembling, start building.