AI Integration

API Development & Orchestration for AI Systems

Building an API for AI is not the same as building a REST endpoint. Model latency, output variability, auth boundaries, and downstream system expectations require deliberate API design, not the same patterns you use for CRUD services.

What you get

  • A documented API contract your team can maintain and extend
  • Auth and permissions boundaries aligned to your security policy
  • Orchestration logic that routes AI output into your existing systems
  • Rate limiting, error handling, and retry logic designed for model latency
  • Observability hooks so you know when the API is degrading

What This Covers

Specific capabilities and deliverables within this engagement.

API Design

  • AI-aware endpoint design (latency, variability, streaming)
  • Auth model aligned to your existing identity provider
  • Versioning strategy for model updates
  • Rate limiting and throttling for cost control

Orchestration Logic

  • Multi-step pipeline orchestration
  • Output routing to downstream systems
  • Human review loops where required
  • Fallback handling when model output is out of bounds

Documentation & Contracts

  • OpenAPI spec generation
  • Internal developer docs
  • Change log and versioning policy
  • Runbook for operations team

Observability

  • Request/response logging with PII controls
  • Latency and error rate monitoring
  • Model output quality sampling
  • Alerting thresholds for drift detection

Engagement flow

How the work progresses

Each step produces concrete decisions, artifacts, and sequencing guidance your team can use immediately.

1

Stack & Requirements Audit

Review existing APIs, auth patterns, and downstream system expectations before designing anything.

2

API Contract Design

Define endpoints, schemas, auth model, and error handling before writing a line of production code.

3

Build & Integration Testing

Implement the API layer with end-to-end testing against real downstream system behavior.

4

Observability Setup & Handoff

Configure monitoring, document the runbook, and hand off to your team with a live deployment.

Best fit signals

This work is most valuable when the need is clear but structure, ownership, and sequencing are not yet defined.

You have a working AI model or API but no clean integration to your internal systems
Your engineering team needs architecture support and a build partner, not a replacement
Your AI output currently lives in a dashboard or prototype that isn't connected to real workflows
You need the integration to be owned and maintained by your team after handoff

Ready to Get Started?

Book a strategy call to discuss your requirements and whether this engagement is the right fit.