AI Development
Production AI features, built by engineers who ship LLMs daily.
Beyond ChatGPT wrappers. We design, build, and deploy AI features that work in production — chatbots, agents, RAG systems, and custom ML pipelines. Evaluated, monitored, cost-controlled.
What you get
Every engagement ships with these as standard
LLM integrations
OpenAI, Anthropic Claude, Gemini, open-source. We pick the right one for your use case.
RAG pipelines
Custom retrieval over your docs, knowledge base, or database. Vector DB + embeddings + re-ranking.
AI agents
Tool-using agents that take actions — book meetings, search data, call APIs — safely.
Evaluation & testing
LLM output evals, regression tests, prompt version control. We know what "good" looks like.
Cost controls
Token monitoring, caching, model routing. Your AI bill stays predictable.
Hosted or self-hosted
Deploy via OpenAI/Anthropic APIs, or self-host open-source models on your infra.
How we work
Transparent, predictable, no surprises
Use-case mapping
What problem does AI actually solve? What baseline do we beat?
Prototype
2-week spike: working demo + eval harness to measure quality.
Productionize
Caching, rate limits, monitoring, fallbacks, safety filters.
Deploy
Ship behind a feature flag, gradual rollout, A/B vs baseline.
Iterate
Weekly eval review, prompt tuning, model updates.
Tools we use
We pick the right tool for the job, not what's trendy
Frequently asked
Real answers to real questions
What kinds of AI projects do you take?
Customer support chatbots, internal knowledge assistants, document processing, AI agents for workflows, content generation, semantic search, classification & categorization. Not hardware robotics or novel model training.
Which model providers do you use?
We default to OpenAI GPT-4 and Anthropic Claude for most use cases. Open-source (Llama, Mistral) when you need to self-host for privacy or cost reasons.
How do you prevent hallucinations?
Grounding in your data via RAG, strict prompt engineering, output validation, citation requirements, and comprehensive eval suites that catch drift.
What will our AI feature cost to run?
Depends on traffic. Most projects fall in the $100-2000/month range on API costs. We estimate before we start and put caching and monitoring in place to keep it predictable.
Ready to get started?
Book a 30-minute discovery call. We'll scope your project, suggest an approach, and give you a firm timeline.
Start your AI project