Production FastAPI, done right.
Work directly with Dipankar Sarkar, the maintainer of FastWorker, on FastAPI architecture, async migrations, background-job design, and performance audits for Python teams shipping at scale.
What we do
We focus on a narrow stack so we can go deep: FastAPI, async Python, Pydantic, task queues, observability, and production deployment.
FastAPI architecture review
A structured 1–2 week review of your FastAPI codebase: async patterns, dependency injection, Pydantic models, middleware, error handling, OpenAPI hygiene, and deployment topology. Delivered as a written report with prioritized action items.
Async migration
Move a sync Flask/Django or thread-bound FastAPI service into a properly async architecture. We redesign I/O boundaries, kill blocking calls, and set up a task queue for anything that does not belong in the request path.
Background-job design with FastWorker
Design a production-grade background job layer for your Python app using FastWorker. Covers task modules, priority tiers, retries, observability, and deployment — plus migration if you are coming from Celery or RQ.
FastAPI performance audit
Benchmark your service end to end, identify the real bottlenecks (database, serialization, middleware, event-loop starvation), and ship a concrete optimization plan with measured before/after numbers.
Production readiness
Observability (OpenTelemetry, metrics, logs), health checks, graceful shutdown, connection pooling, DB migrations, and deployment pipelines for FastAPI on Kubernetes, ECS, or CapRover.
Team mentoring & code review
Recurring code review and pairing sessions with your engineering team. Ideal for teams new to async Python or newly responsible for a mission-critical FastAPI service.
Who this is for
Shipping FastAPI services and hitting async, performance, or deployment issues they want resolved fast.
Scaling from MVP to production without hiring a full platform team yet.
Choosing a task-queue, background-job, or observability stack for a new Python project.
Frequently asked
Who is behind Neul Labs?
Neul Labs is Dipankar Sarkar's independent consulting practice. Dipankar is the maintainer of FastWorker and has spent over a decade shipping production Python — across startups, research labs, and infrastructure projects.
How do engagements start?
Send an email with a short description of your problem and stack. We schedule a 30-minute scoping call to validate fit and agree on deliverables, then start inside a week on small engagements or with a proper SOW for larger ones.
Do you work with CrewAI / agent teams?
Yes — many consulting clients run agent workloads (CrewAI, LangGraph, custom orchestrators) on top of FastAPI. We can help you run agents at scale, queue their work correctly, and instrument them properly. The focus is still production Python architecture.
Do you take equity / retainer / fixed-price work?
All three are options depending on scope. Most engagements are fixed-scope projects or monthly retainers; happy to discuss what fits your team.
Start a conversation
Send a short note describing your stack, problem, and timeline. You'll hear back within one business day.
Email [email protected]