Overview
We build AI features that survive contact with real users: retrieval over your own knowledge, structured outputs your systems can consume, and evaluation harnesses that catch regressions before customers do.
No demos that die in a deck. Everything we ship includes latency budgets, fallbacks, and cost controls.
What we build
- LLM-powered chat and assistants
- RAG over internal documents and data
- Document processing and extraction
- Workflow automation agents
- AI feature integration into existing products
- Evaluation and monitoring pipelines
Our approach
- 01
Scope
We pick one workflow where AI is measurably better, and define the metric before building.
- 02
Ground
Your data, your guardrails: retrieval quality and eval suites come before model polish.
- 03
Operate
Latency, cost per request, and quality dashboards ship with the feature, not after it.
Technologies
- OpenAI / Anthropic APIs
- LangChain
- Vector databases (pgvector, Pinecone)
- Embeddings & RAG
- Whisper / speech
- Python
- TypeScript
- Vercel AI SDK
Selected work
- MangMicroHow we designed and built MangMicro, an AI-powered microlearning platform that turns dense academic PDFs into interactive learning experiences.
- Enterprise DashboardA generic internal demo: an enterprise operations dashboard consolidating metrics, role-based views, and reporting into one workspace.
- All case studies
FAQ
Our data is sensitive. Is AI still an option?
Yes. We deploy with providers that offer zero-retention endpoints, or self-host open models when policy requires it.
How do you prevent hallucinations?
Grounded generation (RAG) with citations, structured output schemas, confidence thresholds, and human review paths for high-stakes actions.
What does it cost to run per month?
We model token usage during discovery and ship with caching and model-tiering so costs scale predictably with usage.
Can you add AI to our existing app?
That's the most common engagement: a focused feature slice added to your current stack without a rewrite.