Deliverables tailored to ai development—designed for production, not prototypes that stall after launch.
Task-specific agents with tools, memory, and guardrails—designed for real workflows, not demos.
Model selection, prompt systems, evaluation harnesses, and fine-tuning where it actually moves metrics.
Ingestion, chunking, embeddings, retrieval quality, and observability for trustworthy answers over your data.
Billing-safe AI UX: rate limits, cost controls, audit logs, and admin tooling for production traffic.
OpenAI GPT-4o, Anthropic Claude, Mistral, Gemini
LangChain, LlamaIndex, LangGraph, custom Python/TypeScript services
AWS Bedrock, Azure OpenAI, GCP Vertex, vector DBs (Pinecone, pgvector)
Python, FastAPI, Node.js, React, Next.js, TypeScript
We map goals, constraints, and define scope with a senior architect.
Senior architect designs the system—no junior guesswork on foundations.
Agile sprints with live demos every Friday and a shared project board.
Production deployment, documentation, and full codebase ownership.
Explore outcomes from similar builds—filter by product type on the portfolio index.
Both. We scope a thin end-to-end slice first (often 6–10 weeks), prove value with real users, then scale architecture and reliability.
We design caching, routing, fallbacks, evaluation sets, and monitoring up front so you get predictable spend and measurable answer quality.
Yes. Most engagements integrate into current auth, APIs, and data stores, with clear boundaries for PII and compliance needs.
You receive repos, IaC, runbooks, and documentation—plus a launch checklist so your team can operate the system confidently.
After a short discovery, we give a fixed quote and milestone plan. Complex data or compliance requirements are scoped explicitly.