The platform your organization builds AI on.
Every team building its own model access, prompt storage, and eval process isn't progress, it's duplicated work waiting to sprawl. Crescent AI engineers the developer portal, model gateway, agent and prompt registries, golden paths, and governance that let every team build AI systems on one shared, self-service platform.
The shared system teams build AI on, not a stack per team.
AI Platform Engineering is the discipline of building the internal product layer that lets teams create, evaluate, deploy, govern, and operate AI systems consistently. It adapts platform engineering and internal developer platform practices, catalogs, golden paths, self-service infrastructure, to AI-specific assets: models, prompts, agents, tools, eval datasets, retrieval indexes, guardrails, and traces.
We design the catalog schema, build the first golden path, wire the model and prompt registries to a promotion gate, and centralize model access behind a gateway so cost and usage are attributable, before expanding the platform to a second team or a second golden path.
What a production AI platform needs
Six ways teams end up rebuilding the same AI stack.
One team's prototype works. Ten teams doing the same thing separately is the actual cost.
Every team builds differently
Each team wires up its own model access, prompt storage, and eval process from scratch. What one team calls a golden path is undocumented knowledge in someone's head, and the next team starts from zero.
Duplicated infrastructure
Three teams stand up three vector stores, three tracing setups, and three ad hoc eval scripts, solving the same model-access and observability problem in isolation instead of once.
Model sprawl
Model IDs hardcoded in application code, no alias or version pinning, no record of which production system calls which model. A provider deprecates a model and nobody knows what breaks.
Agent sprawl
Nobody can answer which agents can send email, write to the CRM, or touch customer data. Agents ship without an inventory, an owner, or a risk tier, until one of them does something nobody approved.
Poor developer experience
A developer waits on the platform team for every model access request, prompt review, and deployment approval, because there's no self-service path, only tickets.
Scaling bottlenecks
What works for one team's prototype breaks down at ten teams and fifty AI workflows: no shared registry, no cost attribution, no gate stopping an untested prompt change from reaching production.
Built for teams scaling past one team's AI stack.
We work with five kinds of teams, all past the point where one team's ad hoc setup is a good enough answer for the whole org.
AI-Native Startups
Pre-revenue to $20M ARR, shipping AI-native product
B2B SaaS
Adding AI to an existing product surface
Enterprise Engineering
Internal platform teams scaling AI org-wide
Digital-First Enterprises
200-3,000 employees integrating AI across a cloud-native product
Global Capability Centers
Captive engineering centers building internal AI tooling and developer platforms
Eight surfaces, engineered as one platform.
Organized around what teams need to self-serve, not which vendor's dashboard hosts it.
AI platforms
The internal product layer, catalog, registries, gateway, and golden paths, that lets teams build AI systems without re-solving model access, evaluation, and deployment from scratch.
Model gateways
Centralized, governed model access with rate limits, fallback routing, and usage attributable by team, app, and environment, not direct provider calls from every service.
Agent registries
An inventory of every agent in production: owner, tools, permissions, model and prompt versions, eval suite, and risk tier, so "which agents can touch customer data" has an answer.
AI SDKs
Internal libraries that wrap model access, tracing, and evaluation behind a consistent interface, so teams get observability and guardrails by default instead of by convention.
Developer portals
A catalog of AI apps, agents, prompts, models, and datasets, with ownership and relations. More than a chat interface backed by a wiki.
Golden paths
Approved, self-service templates for a RAG app, a tool-using agent, or a document-extraction workflow, that produce required metadata, evals, and tracing automatically.
Self-service infrastructure
Provisioning teams can run themselves: model access, vector stores, eval datasets, without a ticket to the platform team for every request.
Control planes
The policy layer that enforces which models, which environments, and which promotion gates apply, in code, not in a wiki page nobody reads.
A single agent's architecture, orchestration, memory, and tool use sits with Agent Engineering. GPU serving economics and model hosting sit outside this scope. Compliance program design for SOC 2 or the EU AI Act sits with AI Governance & Control.
Seven layers, reasoned through on every platform.
Every AI platform needs the same seven layers designed deliberately, whichever tools run underneath.
Developer layer
The portal, catalog, and golden path templates a developer actually touches: docs, ownership, and a self-service starting point for a compliant first build.
Gateway layer
Approved model endpoints, rate limits, fallback routing, and cost tracking, centralized so usage is attributable by team, app, and environment instead of scattered across direct API keys.
Model layer
Model registry and version aliases, so production code references a pinned alias, not a hardcoded model ID that breaks when a provider deprecates it.
Agent layer
Agent registry: owner, tools, permissions, model and prompt versions, eval suite, and deployment endpoint for every agent in production.
Data layer
Eval datasets, RAG indexes, and the trace and feedback store, versioned and owned, so production failures convert into new benchmark cases instead of getting lost.
Infrastructure
Dev, staging, and production environments with versioned releases and rollback pointers. GPU serving and inference economics sit with AI Infrastructure, not this layer.
Governance
Ownership, risk tiering, access control, and audit logs enforced at promotion time, with an exception process for sandboxes that doesn't block experimentation.
A platform is judged by adoption, not completeness.
Platform engineering treats developers as customers. The platform is judged by whether teams actually use it, not by how many features it has.
We track the same metrics a platform-as-product team would: adoption, speed to a first working build, and how often teams route around the golden path instead of using it. A platform with a complete feature set and a 20% adoption rate isn't a platform yet.
Self-service, or it's not a platform.
A portal with no backend controls is a demo. The benchmark is whether a developer can ship a compliant first AI app without a ticket.
Time-to-first-agent under one working day is the standard we build to. That only holds if the golden path actually produces a working, compliant build, ownership, evals, and tracing included, without a developer waiting on the platform team to unblock them.
Access control and audit logs, enforced at promotion time.
Every production AI asset carries an owner, a risk tier, and an audit trail. Promotion to production is a gate, not a suggestion.
Governance that blocks experimentation gets bypassed. We keep sandbox paths separate from production paths, so a team can explore freely while every asset that actually reaches customers still needs an owner, an eval, and an approval.
Threat modeling, adversarial testing, and red-teaming across your AI systems is AI Security Engineering. Compliance program design and policy authoring is AI Governance & Control.
The same nine-phase lifecycle, applied to a platform.
Discovery through knowledge transfer, with a defined gate at every step. For a platform build, Validate covers golden-path completion testing, registry hygiene, eval-gate testing, and full-system scenarios across teams.
What you receive.
Scoped to the engagement, from a readiness audit to a full platform build.
A platform measured by adoption, not by completion.
The checklist we hold a platform to: teams using it on day one, reporting bugs, and routing around it when it doesn't fit.
The other seven pillars.
AI Platform Engineering rarely stands alone. These are the disciplines it most often connects to.
Common questions.
Bring us the stack every team is rebuilding separately.
Whether you need one golden path proven on one team, or a full catalog, registry, and gateway across the org, we'll walk through where your teams stand before we recommend anything.
No hype · No forced roadmap · Just a clear view of what the platform needs next