What we build
Five ways to move a product forward, from deciding what to build to shipping and operating it. Use the starting points below to find the engagement that matches the problem in front of you.
- AI Development
AI Development Services
Custom AI development for startups, SMBs, and enterprises: LLM features, AI agents, RAG, and Model Context Protocol integrations, built by senior engineers and shipped to production.
- Start here when
- Products that need an AI feature that works on real user data, not a scripted demo
- First step
- Start with the user task, the inputs the feature will actually see, and the answers it must never give. That determines whether an LLM, retrieval, an agent, or a simpler rule is the right tool.
- Agent-Driven Development
Agent-Driven Development
AI-native engineering with Claude Code, Codex, and Cursor: ship faster without giving up code quality, plus workflows and guardrails for your own team.
- Start here when
- Engineering teams that want to adopt AI coding tools without a quality regression
- First step
- Look at how work moves from task to merged change today: where context is lost, where generated code needs the most scrutiny, and which checks already catch regressions.
- Full-Stack Development
Full-Stack Product Development
Frontend, APIs, data, and cloud infrastructure, including blockchain and smart-contract integrations, designed and built as one system, not handed between vendors.
- Start here when
- Products that need one team accountable for the whole stack, not four vendors to coordinate
- First step
- Trace the important user flows through the interface, APIs, data, and infrastructure before choosing frameworks. The seams between those layers are where hand-offs usually create cost and delay.
- Prototyping + MVP
MVP Development
A working product in weeks, not quarters. We build the smallest version of your product that can survive contact with real users, then iterate on what the data says.
- Start here when
- Founders who need a fundable, demo-ready product fast
- First step
- Decide what must be learned from real users before adding a feature list. A disposable prototype can settle a design question; the MVP should test the business-critical workflow.
- Technology Consulting
Technology Consulting & Advisory
Architecture reviews, build-vs-buy decisions, AI readiness assessments, and vendor evaluation, from senior engineers who also ship, not just advise.
- Start here when
- Companies about to commit budget to a build and want a second opinion first
- First step
- Review the architecture, code paths, operating costs, and team constraints that actually shape the decision. A recommendation without that baseline is guesswork.