Data Platforms for AI
FastAPI, MongoDB, Kubernetes - LLM-Optimized APIs
Overview
We turn AI concepts into durable products. We build LLM-optimized APIs (FastAPI + MongoDB), containerized ETL, and managed Kubernetes deployments with CI/CD and observability.
The result: small, precise payloads, predictable latency/cost, and safe, frequent releases. Your AI applications get the data they need, when they need it, in the format they expect.
Technical Excellence
Approach & Methodology
- API design: LLM-ready contracts (explicit metrics, grouping, temporal breakdowns)
- Data modeling: Query patterns drive indexes; projections minimize payload size
- ETL: Idempotent upserts; CDC/incremental loads; backpressure handling
- Ops: Blue/green or rolling updates; budgets (latency/token) with alerts
Technology Stack
- Backend: Python, FastAPI, Pydantic, AsyncIO, httpx
- Database: MongoDB (motor), vector search; schema governance
- Infrastructure: Docker; Kubernetes (AKS/GKE), CI/CD; SLOs and runbooks
- Observability: Structured logs, tracing, LLM instrumentation
Measurable Impact
Expected Results
- Lower latency/cost through optimized payloads
- Simpler agent integration with clear contracts
- Safe, routine deploys with elasticity for traffic spikes
- Clear contracts that scale across teams
KPIs We Track
- p95 API latency and error rate
- Cost per 1k requests
- ETL freshness and success rate
- Time-to-deploy
- Incident count/MTTR
- Rollout success rate
How We Work Together
Discovery → Build → Harden → Operate
Weekly demos ensure alignment. Each phase delivers:
- Discovery (1 week): API contracts, data models, success metrics
- Build (3-4 weeks): APIs, ETL pipelines, initial deployment
- Harden (2 weeks): Production readiness, monitoring, runbooks
- Operate (ongoing): Performance tuning, feature expansion
Ready to Get Started?
Let's discuss how Data Platforms for AI can transform your business