Enterprise AI: AI at scale, under control.
We build the platform your company runs AI on, and the first use cases on top of it.
What we build
One platform
Model access, retrieval, tool calls and logging, deployed in your cloud account or data center. Models run there or through providers you already contract with.
Connected to your systems
Connectors to documents, databases and business apps that keep existing permissions. People see only what they could already see.
Tests and guardrails
Each use case gets a test set drawn from real work. Every change to a prompt, model or source is scored against it before release, and guardrails check inputs and outputs.
Cost and latency control
Requests go to the smallest model that passes the test set, with caching for repeat work. Spend and response time show per use case.
Security and access
Single sign-on, role-based access and an audit log of every request. Actions that change a record wait for a person’s approval.
Adoption by teams
Each use case is built with the people who will use it, inside the tools they already work in.
Pilots are easy. Production is the work.
How it works
Business apps call one model gateway. Guardrails check each request and answer, retrieval adds only what the user may see, and the gateway routes to a hosted or self-hosted model and meters every call.
Every model, every team, one gateway.
Why it holds up
Model fallbacks
When a model or provider is slow or down, requests move to another model instead of failing.
Isolated by team
Data, prompts and limits are kept apart per team, so a new use case can’t slow down or expose another.
Spending limits
A hard budget per team stops runaway spend as usage grows.
Traced in production
Each request records who asked, which sources and model, the cost and the time taken. Quality is scored on live traffic, so drift shows before users report it.
What we measure
- Pass rate on the test set
- Cost per task
- p95 latency
- Answers with cited sources
- Answers corrected by reviewers
- Weekly active users
- Time per task
Common questions
What does Stelcorp build for enterprise AI?
Stelcorp builds the platform a company runs AI on, and the first use cases on top of it: model access, retrieval, tool calls and logging in one place, connected to your documents, databases and business apps, with tests, guardrails, and cost, latency and access under control.
Can the AI platform run in our own cloud or data center?
Yes. The platform is deployed in your cloud account or data center. Models run there, self-hosted, or through providers you already contract with, behind one model gateway that meters every call. When a model or provider is slow or down, requests move to another model instead of failing.
How does Stelcorp test AI answers before release?
Each use case gets a test set drawn from real work. Every change to a prompt, model or source is scored against it before release, and guardrails check inputs and outputs. In production, quality is scored on live traffic, so drift shows before users report it.
How are AI costs kept under control?
Requests go to the smallest model that passes the test set, with caching for repeat work. Spend and response time show per use case, and a hard budget per team stops runaway spend as usage grows.
Who can see which data?
Connectors keep existing permissions, so people see only what they could already see. Access uses single sign-on and roles, every request is recorded in an audit log, and actions that change a record wait for a person’s approval.
Start with one team.
Pick one team and its daily work. In three weeks we map it, measure it and build a working prototype on your data. The next team starts on the same platform.
Tell us about the team