AI product development
Copilots, agents, and LLM workflows shipped into real products — measured on quality, cost, and latency.
Overview
AI that earns its place in production
Most AI demos never survive contact with your data, your permissions model, or your bill. We build the product around the model: retrieval, tools, evaluation, and the Rails systems that already run your business.
You get features users can rely on — not a chatbot bolted on for the pitch deck.
- Use cases chosen for ROI, not novelty
- RAG and tools grounded in your own data
- Guardrails, logging, and human review where it matters
- Cost and latency budgets agreed up front
- Evaluation harnesses so quality does not drift after launch
What’s included
Where we put AI to work
Copilots & assistants
In-product help that knows your domain — drafts, searches, and actions with your permissions.
Agents & workflows
Multi-step jobs that call tools, update records, and escalate when confidence is low.
RAG & knowledge
Search and answer over docs, tickets, and databases with citations you can trust.
LLM integrations
OpenAI, Anthropic, Gemini, or open models wired into your Rails app with retries and fallbacks.
Eval & observability
Prompt and retrieval tests, tracing, and dashboards so regressions show up before users do.
Cost & model routing
Cache, batch, and route by task so you are not paying frontier prices for every call.
Process
How the engagement runs
-
01
Prove the use case
- Workflow and data audit
- Success metrics
- Thin vertical slice
-
02
Build for production
- Retrieval and tools
- Guardrails and logging
- Staging with real data
-
03
Operate and improve
- Eval suite
- Cost controls
- Iterate on failures
Stack
Tools we reach for
- Ruby on Rails
- OpenAI
- Anthropic
- Gemini
- pgvector
- LangChain / custom
- Sidekiq
- Redis
- PostgreSQL
- Docker
Let's talk about the AI you want to ship
Tell us where you are — an idea, a half-built product, or a system that needs AI that earns its keep. We will reply with a practical next step.
