AI Platform Engineering
Build the platform your engineering teams build AI on. We build the internal AI platform (developer portal, model gateway, agent and prompt registries, ready-made paths, and testing checkpoints) so every team ships AI features on one shared foundation instead of rebuilding the same infrastructure five times.
Building a single agent, including its design and tool use, is AI Agent Development, not this. This is the shared platform underneath every team's agents.
A Shared Platform, Not Five Teams Rebuilding the Same Stack
Every team solving model access, deployment, and testing on its own isn't a staffing problem, it's a missing platform. We build the catalog, gateway, registries, and ready-made paths that let every team ship AI on one shared foundation.
What is AI platform engineering?
AI platform engineering is building the shared internal system that lets several engineering teams build and run AI applications without each team building its own infrastructure. That usually means a model gateway, a developer portal, agent and prompt registries, ready-made paths, and governance rules.
Tools We Use
Vendor-neutral, on whatever stack you already use: a Backstage-style catalog, MLflow or LangSmith to track models and prompts, Databricks or cloud-native tools if you're already there.
Recognize the symptoms
When You Need AI Platform Engineering
If two or more of these are already true, this isn't a tuning problem.
- Several AI teams exist, and each one handles model access, deployment, and testing differently
- Work is duplicated: three teams built three vector databases, three tracing setups, three sets of test scripts
- Deployment differs between teams, with no shared pipeline or release standard
- Testing differs from team to team, so results can't be compared
- Nobody can say which team owns a given model, prompt, or agent in production
- AI infrastructure cost is rising and nobody can trace it to a model, team, or feature
- Every team calls model providers directly, with its own keys and its own limits
When this starts happening, a shared AI platform becomes an engineering problem, not a tooling problem. It's the same design discipline behind AI Development, applied to the whole platform instead of one system.
The shared system teams build AI on, not a stack per team.
- AI Platform Engineering covers the internal system that lets every team build, test, launch, govern, and run AI the same consistent way.
- It takes the practices used for internal developer platforms (catalogs, ready-made paths, self-service setup) and applies them to AI: models, prompts, agents, tools, test data, search indexes, safety rules, and logs.
- We design how the catalog is organized, build the first ready-made path, and connect model and prompt tracking to a checkpoint that has to pass before anything goes live.
- We put model access behind one shared gateway so cost and usage can be traced, before expanding the platform to a second team or a second ready-made path.
What We Build
Eight parts that work together, organized around what teams need to do for themselves, not which vendor's dashboard hosts it.
AI platform
The shared internal system (a catalog, version tracking, one point of model access, and ready-made paths) that lets teams build AI without solving model access, testing, and deployment from scratch every time.
Model gateway
One shared, controlled way to reach AI models, with usage limits, automatic backup if a provider goes down, and spending tracked by team and app, instead of every service calling providers directly.
Agent registry
A full record of every agent in production: who owns it, what tools and permissions it has, which model and prompt version it runs, and how it's tested. So "which agents can touch customer data?" has a real answer.
Shared code libraries (SDKs)
Internal code libraries that handle model access, logging, and testing the same way every time, so every team gets visibility and safety checks by default instead of having to remember to add them.
Developer portal
One place to see every AI app, agent, prompt, model, and dataset you have, who owns it, and how they connect. More than a chatbot backed by a wiki page.
Ready-made paths
Approved templates for common builds, like a search-and-answer app, a tool-using agent, or a document-extraction workflow, with the required tracking, testing, and logging built in.
Self-service setup
Things teams can set up themselves, like model access, a search database, or test data, without filing a ticket to the platform team every time.
Rules layer
The layer that enforces which models can be used where, and what has to pass before something goes live, automatically in code, not on a wiki page nobody reads.
How a single agent is built, including its coordination, memory, and tool use, is part of AI Agent Development. Running your own model servers is outside this scope. Compliance work for SOC 2 or the EU AI Act is part of AI Governance.
What a Production AI Platform Needs
Eight parts that work together, not eight separate tools that don't talk to each other.
A catalog of everything
A list of every AI app, agent, model, prompt, tool, and dataset you have, who owns each one, and how risky it is.
Ready-made paths
At least one approved path a developer can follow start to finish without filing a ticket. Often called a golden path.
Model registry
A central record of which model version each app uses, so you can swap models without rewriting code.
Prompt history
A version history for prompts, with a safe way to test changes and undo them if something breaks.
Agent registry
A record of every agent in production: what tools it can use, what it's allowed to do, and how it's tested.
Release checkpoints
A stop that keeps a change from going live if it fails testing, with testing kept separate from production.
One model gateway
One shared way to reach AI models, so you can see exactly what each team and app is spending.
Automatic rule enforcement
Rules enforced automatically before anything goes live, not written on a wiki page nobody reads.
Platforms We Build
Organized around where you actually are, not a one-size platform built before anyone has used it.
Starter platform
One team, one approved template, proven end to end before the platform grows to a second team.
Shared platform for several teams
A shared catalog and model gateway across teams, with cost and usage traced back to each one.
Platform with governance built in
Approval steps, audit logs, and risk ratings built into the path to launch, for organizations that need evidence, not just infrastructure.
Platform ready for agents
Registries and permission controls built for agents, treating them as platform users alongside human developers.
Hybrid and on-premise platform
The same catalog, gateway, and ready-made paths, built to run in a regulated or on-premise setup instead of defaulting to public cloud.
How the Platform Is Structured
Not a vendor's reference diagram.
Model gateway
One shared, controlled way to reach AI models, with usage limits, automatic backup, and spending tracked by team and app.
Central team, product teams as users
A central platform team owns the catalog and shared services. Product teams use them through the gateway instead of each building their own.
Ready-made path (golden path)
An approved, pre-wired template for a common build that comes with tracking, testing, and logging built in, so following it is easier than going around it.
Rules kept separate from requests
The rules (what's allowed, what has to pass) live apart from the layer that runs requests, so changing a rule doesn't mean redeploying every application.
What You Receive
AI Platform Engineering vs MLOps and LLMOps
- MLOps engineering covers the lifecycle of models trained on your own data: feature pipelines, training runs, model registries, and drift monitoring. Most companies buying platform work today aren't training models. They're building applications and agents on top of one someone else trained.
- LLMOps covers what changes week to week on top of that model: prompt versions, search indexes, tool definitions, gateway routing, and guardrails. It's the right layer for one application. It isn't built to hold up once ten teams are each running their own version of it.
- AI Platform Engineering ties MLOps and LLMOps together into one system every team actually uses: a shared model gateway, one place where prompts and agents get versioned, and a ready-made path so the fifth team isn't solving what the first four already solved.
AI Platform Engineering vs AI Infrastructure
AI infrastructure (GPU capacity, Kubernetes scheduling, model serving, speed tuning, and on-premise or hybrid deployment) is the computing layer underneath. It's necessary, but not enough: a team can get the infrastructure right and still have five disconnected ways of shipping AI on top of it. AI Platform Engineering is the layer teams actually use day to day, running on top of that infrastructure, not replacing it.
From our experts.
Writing on AI infrastructure, cost, and platform decisions.
Our AI Platform Engineering Process
The same nine-phase process we use everywhere, with a checkpoint at every step. For a platform, testing covers whether teams can finish the ready-made paths, whether the tracking records are clean, whether the safety checkpoints work, and whether it holds up across teams.
Related Engineering Services
Common questions.
Bring us the stack every team is rebuilding separately.
Whether you need one ready-made path proven on a single team, or a full catalog, tracking system, and shared model access across the company, we'll look at where your teams stand before recommending anything.
No hype · No forced roadmap · Just a clear view of what the platform needs next