VYAN LABS / BACKEND SYSTEMS
Backend systems that keep AI useful.
Agents and workflows depend on the systems beneath them. We build the APIs, data layers, integrations, and operations that make the product reliable.
Start a project01 / WHAT WE BUILD
Backend systems.
Practical backend engineering for teams that need the AI feature to survive real users, real data, and real edge cases.
API and data design
Clear contracts, schemas, permissions, and tenant boundaries built around the product.
Async work and integrations
Queues, webhooks, caching, and external services with retries and idempotency.
Observability
Logs, traces, metrics, and runbooks so the team can diagnose and improve the system.
02 / APPLICATIONS
Where it earns its place.
A few patterns we can adapt to your actual workflow and systems.
Product APIs
FastAPI and Python services with authentication, versioned contracts, and predictable error handling.
Data and performance
Postgres models, Redis caching, query tuning, and data movement for operational workloads.
AI infrastructure
Model gateways, retrieval services, background jobs, streaming, cost controls, and evaluation pipelines.
03 / METHOD
Designed to be operated.
We build for the people who will use, review, and maintain the system after launch.
Define the boundary
Agree on the job, data, permissions, outcomes, and failure modes.
Test the real flow
Build incrementally with representative cases and clear feedback.
Hand it over
Document decisions, instrument operations, and train the team.
04 / PRODUCTION
Backend engineering for production AI.
The model can change; the product still needs stable contracts, isolation, data quality, and operational controls.
Identity and tenant isolation
Authenticate callers and propagate an explicit identity into data access. Permission checks belong in services and databases, not in prompts.
Reliable asynchronous work
Use durable jobs, idempotent handlers, retries with limits, and dead-letter paths for long-running agent and integration work.
Data and query controls
Define schemas and access policies, tune expensive queries, and put budgets around generated workloads. Our enterprise Text-to-SQL MCP note covers one demanding case.
Observability and release
Correlate requests, model calls, tool actions, and database work. Couple deployment with tests, canary checks, and rollback; see DevOps for AI.
05 / QUESTIONS
Good questions, direct answers.
A few details we usually cover before a project begins.
Do you only work with FastAPI?
No. We use the stack that fits the product and the existing team. Python, FastAPI, Postgres, and Redis are common tools in our work.
Can you improve an existing backend?
Yes. We can profile the bottlenecks, fix reliability issues, and add the interfaces or observability the AI feature needs.
Do you handle deployment?
We plan deployment and operations with your team, including configuration, monitoring, documentation, and handover.
What about security and data access?
Permissions, tenant isolation, secrets, and data handling are defined as system requirements from the start.
07 / CONTACT
Tell us what needs to work.
A useful first conversation starts with the workflow, constraints, and what success would look like.
Let’s make it real.
Share a brief outline. Sourish will reply directly to discuss scope, feasibility, and a sensible first milestone.
support@vyanlabs.in ↗