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Services · 03

Backend & API Development

Typed, documented APIs and backends that your frontend, partners and AI agents can trust.

Node.jsFirebaseGraphQLPostgreSQL
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How this runs

First milestone
3–6 weeks for a first versioned API
The contract
Schema agreed before the first endpoint
What you keep
OpenAPI spec, test suite, dashboards, runbooks
Cadence
Weekly demo against real requests + changelog

Where we start

Three ways projects land on my desk

Good APIs are boring in the best way: predictable shapes, clear errors, versioned contracts and docs that match reality. I have built them for ticketing and payments, for field apps syncing from flaky networks, for hotel operations running in real time — and most recently for AI agents that call the same endpoints humans do. Every API ships with auth, rate limits, validation, logging and a test suite, because retrofitting those is where incidents come from.

  • You're starting from zero

    A product that needs its first real backend: data model, auth, payments, the works. The decisions made now are the ones you'll live with for years.

    Week one · The schema and API contract drafted and reviewed with you, plus a walking-skeleton endpoint deployed with auth and CI already on.

  • Your frontend is blocked

    The app team is ahead of the API. You need endpoints that match how the client actually queries, documented well enough that nobody has to ask.

    Week one · The client's real data needs mapped to a contract, the highest-priority endpoints stubbed against the spec so frontend work unblocks immediately.

  • Your API wobbles under load

    Timeouts at peak, mystery incidents, a p95 nobody measures. It works — until the day that matters most.

    Week one · Traffic and query analysis with baselines recorded, the top bottlenecks identified, and the cheap fixes (indexes, caching, N+1s) already in review.

The build sheet

What's on the menu

  1. REST & GraphQL APIs

    Typed schemas, pagination, filtering and versioning, with OpenAPI or GraphQL docs generated from code.

    • OpenAPI
    • GraphQL

  2. Auth & permissions

    JWT, OAuth, Cognito or Firebase Auth with role- and organization-aware access control.

    • OAuth
    • RBAC

  3. Payments & billing

    Stripe and Razorpay: checkout, subscriptions, webhooks, failed-payment retry and refunds.

    • Stripe
    • Webhooks

  4. Real-time & background jobs

    WebSockets, Socket.io, Firebase listeners, queues and scheduled functions.

    • Sockets
    • Queues

  5. Data modelling & migrations

    PostgreSQL, MySQL, Firestore: schemas that fit the queries, with safe migration paths.

    • PostgreSQL
    • Firestore

  6. AI-ready endpoints & MCP servers

    Public APIs exposed to AI assistants with scoped keys, rate limits and PII redaction.

    • MCP
    • Agents

Standards I don't negotiate

What a response should look like

An example of the shape I aim for: predictable envelopes, explicit pagination, machine-readable errors and headers that tell the client what it needs to know.

GET /v1/events?status=published&limit=2200 OK · 42ms
{
  "data": [
    {
      "id": "evt_9f3a",
      "title": "Summer Rooftop Social",
      "status": "published",
      "startsAt": "2026-06-12T18:30:00Z",
      "tickets": { "available": 42, "currency": "USD" }
    },
    {
      "id": "evt_7c21",
      "title": "Founders Breakfast",
      "status": "published",
      "startsAt": "2026-06-14T08:00:00Z",
      "tickets": { "available": 8, "currency": "USD" }
    }
  ],
  "page": { "nextCursor": "Y3Vyc29yOjI=", "limit": 2 },
  "meta": { "requestId": "req_01J9X", "rateLimit": { "remaining": 998 } }
}
  • Consistent envelope: data, page, meta, and an errors array with codes you can switch on
  • Cursor pagination so clients never miss or duplicate records
  • Idempotency keys on every write that touches money or inventory
  • Request IDs in every response and log line for fast incident triage
  • Scoped API keys with per-key rate limits and audit logs
  • OpenAPI spec generated from the code, so docs cannot drift

Always true, whatever the project

  • Secure by default

    Validation at the edge, least-privilege rules, secrets management and encrypted payloads where needed.

  • Fast and observable

    Caching, indexes and p95 latency tracking, with logs and traces you can actually search.

  • Documented contracts

    Frontend, partners and AI agents work from the same generated spec.

  • CI that stays fast

    Pipelines that test what changed, with emulators and long suites kept off the critical path.

Proof, from shipped work

Backends with real weekends behind them

Ticket drops that sell out in minutes, a thousand hotels syncing room states in real time — the backends below have been through their worst days already. Read the full stories:

7+

Years building production APIs

1,000+

Hotels served by a real-time ops backend

Housekeeping and maintenance SaaS

45%

CI time cut on a three-app monorepo

Pipeline follows the diff

Latency compounds quietly: Amazon famously measured roughly 1% of sales lost per 100ms of added latency — your p95 is a business number.

The stack

Tools picked for the job in front of us

Runtime

  • Node.js
  • TypeScript
  • Express
  • Cloud Functions
  • Firebase

Data

  • PostgreSQL
  • MySQL
  • Redis

APIs & realtime

  • GraphQL
  • AWS AppSync
  • Socket.io
  • Amazon Cognito

Ship & observe

  • STStripe
  • RARazorpay
  • Docker
  • GitHub Actions

AI in the engagement

Where AI actually shows up

Backends are where AI pays off twice: once in how fast I build them, and again when the API itself becomes something an assistant can call.

  1. 01

    Scoping

    Schema-first, everything follows

    Validators, types, clients and docs generated from one schema, so nothing drifts and the contract review happens before any code.

  2. 02

    Build

    Plumbing drafted, decisions kept

    CRUD layers, webhook handlers and migration scripts AI-drafted; data model and money paths designed by a human.

  3. 03

    QA

    Contract and load tests

    AI-drafted test matrices for auth, pagination and error paths; load profiles built from real traffic patterns.

  4. 04

    In your product

    Your API, callable by agents

    MCP servers, RAG pipelines and LLM workflows with scoped keys, redaction and provider fallbacks — evaluation baked in.

45%

CI time cut on an AI-agent-heavy monorepo

Pipeline reshaped to follow the diff

1

MCP server shipped on a public REST API

Same scopes and rate limits as any API key

3

LLM providers behind one support assistant

OpenAI, Anthropic, Gemini via LangChain

The guardrail · AI can generate the validator; it cannot decide what your refund policy should be. Contracts, money paths and security boundaries get human eyes, every time.

Process

How an engagement runs

Expand each step to see what happens and what you receive.

    • Discovery call and written brief: goals, users, constraints, success metrics.
    • Technical discovery: existing systems, integrations, data, compliance needs.
    • A milestone plan with a fixed first release and a parking lot for later ideas.

FAQ

Asked before you had to ask

Firebase or serverless for products that need to ship fast with small teams and spiky traffic. A conventional Node.js server with PostgreSQL when you need complex relational queries, long-running jobs or strict cost predictability. I have run production on all three and will recommend based on your data and team.

Need an API your frontend, partners and AI agents can rely on?

Share what you are integrating and what breaks today. I will reply with an honest take and a plan.

Let's talkrushitjivani1999@gmail.com