AI that ships, not just demos.
Most AI prototypes never reach production. We close that gap by designing and deploying AI features with the observability, evaluation, and reliability your product actually needs.
What we build
RAG Pipelines
Retrieval-augmented generation over your documents, knowledge bases, or internal data.
AI Agents
Multi-step, tool-using agents that automate complex workflows with LLM reasoning.
Embeddings & Vector Search
Semantic search, similarity matching, and recommendation systems at scale.
LLM Integration & Evaluation
Structured LLM outputs, prompt engineering, evals, and cost monitoring.
Every engagement includes
- Architecture review & model selection rationale
- Implementation with full test coverage
- Prompt engineering & optimization
- Evaluation framework (automated evals + metrics)
- Observability: latency, cost, and quality monitoring
- Deployment to your infrastructure
- Technical documentation & runbook
Our AI stack
We're model-agnostic and will work in your existing stack where possible.
Common questions
Which LLMs do you work with?
We're model-agnostic. We work with OpenAI (GPT-4o, o-series), Anthropic (Claude), Google (Gemini), and open-source models via Ollama, vLLM, or Hugging Face. We'll recommend the right model for your use case and budget.
What if we already have a prototype?
Great starting point. We'll review what you have, identify what's preventing it from going to production, and build on top of your existing work.
How do you handle hallucinations and reliability?
We build evaluation frameworks into every AI engagement: automated evals, human-in-the-loop checkpoints, structured outputs, and monitoring. We don't ship AI features without a way to measure and improve them.
What does production-ready mean to you?
It means the feature is deployed, monitored, observable, cost-tracked, and handles edge cases gracefully. It means your team can maintain it without us in the room.