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Ship intelligent product features that users feel.

Custom copilots, RAG pipelines, and GenAI features engineered into your product — with evals, cost control, and production ops.

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rag_pipeline.ts

async function answer(query: string) {
  const docs = await retrieve(query);
  const ranked = rerank(docs, query);
  return generate({ context: ranked, query });
}

eval pass · groundedness 0.94 · p95 820ms

What we build into your product.

Product copilots

In-app assistants that know your domain, permissions, and UX patterns.

RAG pipelines

Ingestion, chunking, retrieval, and re-ranking tuned for precision.

Document AI

Extract, classify, and route unstructured content at scale.

Feature embeds

Ship AI into existing products without rewriting the core stack.

Delivery

From thin slice to production in weeks.

Week 1–2

Thin slice

Data connectors
Prompt & retrieval baseline
Eval harness v0

Week 3–4

Hardening

Latency & cost pass
Guardrails
UX polish

Week 5–6

Ship

Observability
A/B or canary
Runbooks

Engineering stack

Tools we ship AI products with

Model providers, vector stores, and application frameworks matched to your cloud and compliance needs.

Ruby on Rails

Node.js

Flask

Next.js

Nest.js

Python

Django

Express.js

FastAPI

Laravel (PHP)

Go (Golang)

Java / Spring Boot

.NET Core

GraphQL

gRPC

AI development FAQ

What AI products can you build?+

Customer copilots, internal assistants, RAG search, document pipelines, recommendation features, and agent-powered product workflows.

Which models and stacks do you use?+

OpenAI, Anthropic, open-source LLMs, vector databases, LangChain/LlamaIndex patterns, and your existing cloud — chosen for fit, cost, and compliance.

How do you measure AI feature quality?+

Offline eval sets, online feedback loops, hallucination rates, latency SLOs, and business KPIs tied to each feature.

Ready to embed AI in your product?

Talk to an engineer