Full-Stack AI Applications
FastAPI backends, React frontends, Kubernetes deployments. We own the full stack from inference endpoint to user interface. Streaming responses, health checks, rate limiting, and structured error handling built in from day one.
What happens next
- 1. Context We review the situation and constraints.
- 2. Fit We recommend an appropriate next step.
- 3. Scope If relevant, we discuss scope.
End-to-End AI Application Development
We own the full stack from inference endpoint to user interface: streaming responses, health checks, rate limiting, structured error handling, and rollout discipline.
Need reserved delivery capacity, not a generic implementation project?
If the architecture is already clear and the real need is a senior-heavy execution cell with fixed shape, minimum term, and explicit ownership, start with our Embedded Delivery Pod rather than treating the work as open-ended project staffing.
Typical engagement starts when
- a model, agent, or backend workflow exists, but there is no production-grade application surface around it yet
- the team needs backend, frontend, infrastructure, and rollout discipline handled as one delivery problem
- an existing product needs AI capabilities added without destabilizing auth, rate limits, streaming UX, or deployment safety
- leadership wants a system shipped end to end, not a pile of disconnected prototypes owned by different vendors
What We Build
| Capability | What We Deliver |
|---|---|
| API backends | FastAPI with streaming responses, health checks, rate limiting, and structured error handling |
| Frontend applications | React with real-time updates, optimistic UI, and server-state management |
| Infrastructure | Kubernetes deployments with Terraform, Helm charts, and GitOps workflows |
| CI/CD pipelines | Automated testing, staging deployments, and production rollouts |
When to Use This
| If Your Situation Is | Then We Recommend |
|---|---|
| AI model ready but no API, no frontend, no deployment pipeline | FastAPI + React + Docker with a scoped production path |
| Streaming LLM responses needed in user-facing application | FastAPI SSE + React streaming UI + fallback behavior |
| Multi-service AI system needs orchestration and scaling discipline | Kubernetes + Terraform + Helm + GitOps workflows |
| Existing application needs AI features added without rewrite | API integration layer: FastAPI microservice alongside existing stack |
| Need CI/CD for ML models, not just code | GitHub Actions + MLflow model registry + staged rollouts |
| Application is already live and launch or reliability pressure is exposing weak seams | Stabilization Sprint: bounded remediation before broader delivery resumes |
Engineering Standards
- Container-first architecture with Docker and Kubernetes
- Infrastructure-as-code with Terraform and Helm
- Automated testing at unit, integration, and E2E levels
- Observability with Prometheus, Grafana, and structured logging
These standards matter because AI applications usually break at the seams: streaming responses, auth boundaries, deployment rollback, and operational visibility across model, API, and UI.
Common failure patterns we fix
- strong model or workflow prototypes that never became reliable product surfaces
- AI features bolted into existing apps without a clean API boundary, rollback path, or observability
- streaming UX shipped without backpressure handling, auth discipline, or user-facing error recovery
- infrastructure owned separately from application behavior, so deployment and runtime issues bounce between teams
- demos optimized for speed of launch but not for operations, testing, or staged rollout under real usage
What you leave with
- a coherent application architecture from model endpoint to user-facing workflow
- deployment, rollback, rate limiting, and health checks designed into the delivery path from the start
- backend, frontend, and infrastructure decisions documented in one implementation-ready system
- a production application the internal team can evolve without reverse-engineering prototype shortcuts
Best Fit
- Team needs the full path from model endpoint to user-facing product shipped as one coherent system
- Existing application needs AI features without introducing brittle sidecar complexity
- Engineering leadership wants backend, frontend, infra, and deployment treated as one delivery problem
- Product depends on streaming UX, health checks, rollback, auth, and rate limits from day one
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FastAPIFastAPI for LLM Systems: Production Template for LangChain and LangGraph Agents
Use FastAPI to deploy LangChain and LangGraph agents in production with async request handling, Pydantic validation, dependency injection, and cleaner LLM API architecture.
Discuss your Full-Stack AI Applications path
Tell us about your system, the decision ahead, and the constraints. We will review the context and recommend the next step.
Direct contact with a principal engineer.