Data & AI
Hire GenAI engineers who ship LLM features that hold up in production.
LangChain, LlamaIndex, RAG, agents, evals — our nearshore GenAI squads turn LLM demos into reliable product.
Why it matters
Why production GenAI is its own discipline
Building a working LLM demo takes a weekend. Building a production GenAI feature — with retrieval, evals, observability, cost controls, prompt versioning, and graceful degradation — is its own engineering discipline. The talent market is full of prompt-curious engineers; the ones who have actually shipped LLM features that survive contact with real users are rare.
Where LangChain & GenAI earns its keep
RAG over proprietary documents (support, internal knowledge, contracts)
Agentic workflows that orchestrate tools and APIs
AI copilots embedded inside existing products
Document understanding, summarization, and structured extraction
Why outsource
How outsourcing accelerates your GenAI roadmap
GenAI moves too fast to bet on permanent headcount. Nearshore squads let you scale with the technology, not against it.
Production GenAI patterns
RAG that works at scale, evals that catch regressions, observability that keeps you sane.
Cost and latency engineering
Model selection, caching, batching, prompt optimization — your AI bill stays bounded.
Eval-driven development
Engineers who write evals before features. The only way GenAI ships safely.
Multi-model fluency
Anthropic, OpenAI, open-source models on Bedrock or Vertex — right model per job.
What we ship
What our GenAI squads deliver
Most GenAI projects stall at the demo stage. Ours ship to production with the discipline that keeps them there.
RAG systems
Vector stores, hybrid retrieval, chunking strategy, citation discipline.
Agent frameworks
LangGraph, function calling, tool orchestration, safety boundaries.
Evals and observability
LLM-as-judge, golden datasets, drift detection, cost dashboards.
Hire GenAI engineers in 48 hours.
Stop shipping demos. Start shipping AI features. Our GenAI squads are ready.