Stop rebuilding the same infrastructure every time a team ships AI.
A shared AI platform becomes how your organization ships AI, instead of every team building its own. One place to manage models, prompts, testing, and ownership — with a standard way to start something new — replacing scattered, one-off setups.
A platform contract, not a rewrite.
The same service catalog, experiment tracking, and automated infrastructure setup your engineering org already runs — made the default way of working instead of optional.
Recognize the symptoms
The symptoms that show up first.
Every team solving the same problem independently is the tell.
- Each team configures its own model access, with API keys scattered across a dozen services
- Every team writes its own way to test quality because there's no shared test set or grader
- Tracking what happened is scattered across three different tools, and incident knowledge lives in people's heads, not documents
- Nobody can say what data already exists because every agent builds its own way to pull it in
Every team is solving the same six problems, separately.
- Model versioning, prompt management, where to host agents, how to evaluate, who owns what, who gets paged when something breaks — every team answers these questions independently, and every answer is slightly different.
- That's not eight teams moving fast — it's eight teams doing the same setup work over and over, with nothing shared between them.
Six sources of infrastructure sprawl
A unified platform, not an assembly of off-the-shelf tools.
Five pieces that bring scattered, duplicated work together into one shared platform.
Model registry and gateway
One place to access every production model, with version history, a clear way to promote a new version, and cost tracked by team.
Prompt registry
Prompts tracked and versioned like code, each one with a clear owner, tagged to its environment, linked to its test results, and only promoted once it passes.
Agent catalog
A single list of every agent running in production — who owns it, what it's for, what it can access, how risky it is, and how to respond if it breaks.
Unified evaluation
One shared set of test cases and scoring rules across teams, so every release gets checked the same way and a drop in quality gets caught automatically.
Shared observability
One dashboard across every AI system — speed, cost, failures, and quality issues all in the same place instead of scattered tools.
From our experts.
Writing on why AI pilots stall and what production infrastructure actually costs.
The same nine-phase process, applied to the platform.
From discovery to handoff, migrating systems in phases so nothing has to shut down to rebuild.
Common questions.
A unified platform is how scale happens.
Every team shouldn't have to solve model access, testing, monitoring, and permissions from scratch. A shared platform makes that the default. Let's build it for your organization.
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