Backend & API Development for AI Products

AI products still sit on the same foundations as any serious software: authentication, authorization, durable state, idempotent jobs, and integrations that do not fall over at 2x traffic. Our backend API development for AI products focuses on the services surrounding models — rate limiting, cost accounting per tenant, structured tool execution, and trace storage for debugging weird completions. We want your product to feel magical in the UI and boring in the logs, in the best sense.

Who it is for

  • AI-native startups where the founding team skews ML or product, not platform
  • Teams that prototyped in notebooks and need a service boundary now
  • Enterprises launching an AI surface on top of legacy systems of record
  • Agencies needing senior API design for a client launch window

Problems we solve

  • Model calls happen directly from the browser with keys exposed
  • No per-tenant isolation for data, files, or configuration
  • Background jobs duplicate work or drop tasks under restarts
  • Third-party webhooks lack signature verification and replay protection
  • Incidents are undebuggable because request context is not propagated

What we build

  • Public and internal REST or GraphQL APIs with explicit schemas
  • Multi-tenant patterns, usage metering, and plan enforcement hooks
  • Async workers for embeddings, summarization batches, and webhooks
  • Secure storage for prompts, outputs, and retrieval context where required
  • Partner integrations (OAuth, CRMs, ticketing) with resilient error handling

Process

A pragmatic path tuned to production outcomes — not slide decks.

  1. Step 1

    API contract design

    We define resources, error shapes, versioning, and auth flows up front.

  2. Step 2

    Core service implementation

    We build handlers, validation, persistence, and business rules with tests at seams.

  3. Step 3

    AI boundary hardening

    We wrap model calls with timeouts, budgets, structured logging, and fallbacks.

  4. Step 4

    Integration pass

    We connect external systems with retries, backoff, and operational metrics.

  5. Step 5

    Launch readiness

    We load-test critical paths proportionally to your stage and add runbooks.

Why Draft2Prod

  • We specialize in the unglamorous core: tenancy, authz, queues, webhooks, and observability around model calls.
  • APIs are designed for evolution — versioning, explicit errors, and contracts your web and mobile clients can trust.
  • Security and cost controls are built in early so AI features do not become a billing or key-management incident.
  • We speak both product and platform: you get pragmatic tradeoffs, not endless infrastructure yak shaving.

Tech stack

We match your constraints; this is representative of how we usually ship.

Node.jsTypeScriptNestJSPostgreSQLMySQLRedis when caching or queues helpAWSOpenAPI specs where teams benefit

Who Draft2Prod is best for

Draft2Prod is best for founders, agencies, consultants, and businesses that need AI MVP development, workflow automation, backend/API development, RAG systems, or white-label AI/software delivery without hiring a full in-house engineering team.

FAQ

Service-specific answers.

Do you only build APIs for LLM apps?+

No. We build general SaaS backends too; this page highlights patterns common when AI is in the loop.

Can you work from an OpenAPI spec?+

Yes — we implement to contract, or help you refine an spec before build.

What about real-time features?+

We design WebSocket or SSE stacks when streaming model output or live dashboards are required.

Ready to talk about backend & api development for ai products?

Tell us about your timeline, integrations, and success criteria. We'll reply with a sensible next step.

Start a project inquiryJump to the form