Everyone’s got an AI demo that wowed once and broke twice. I build the other kind: chatbots that actually know your docs, copilots wired into your product, agents that finish real tasks — practical, tested, honest about what it can’t do.
The uses that hold up past the demo — because they're grounded and scoped.
A bot that answers from your documents — support articles, manuals, policies — with citations, not hallucinations.
An assistant inside your app that drafts, explains, or acts — wired to your data and permissions, not a generic sidebar.
Pull structured fields from emails, PDFs, forms; tag and route them. The reading-and-sorting a human hated doing.
Multi-step jobs — research, draft, check, file — run by an agent with guardrails, reporting back what it did.
Summarize, rewrite, translate, generate — at volume, in your voice, with a human check where it matters.
Not sure AI even fits? I'll tell you straight where it helps and where a plain script is cheaper and more reliable.
Why these hold up when the flashy demos don't.
Answers come from your actual data via retrieval — so it says "I don't know" instead of inventing a confident wrong answer.
A narrow job done reliably beats a broad one done unpredictably. We pick the task AI is genuinely good at.
AI drafts, a human approves anything that matters. No silent, unrecoverable actions taken on your behalf.
Atlas is our retrieval agent, glass-boxed: the plan → retrieve → rank → compose loop runs visibly, with per-chunk scores, technique toggles and citations.
Describe the task. I'll tell you honestly whether AI is the right tool — and if it is, what it'd take to ship it well.