Internal knowledge assistant
Plain-English answers from your SOPs, policies, and records, with a link to the source document.
What I do · AI agents & assistants
AI that actually knows how your business works.
Your team's knowledge lives in documents, inboxes, and a few people's heads. I build AI agents and assistants that know your business, answer questions with the source attached, review work against your standards, and carry out multi-step tasks across your tools, including OpenClaw, set up and tuned for your business.
Sound familiar?
Where knowledge gets stuck
- The same questions asked of the same senior person every day
- Procedures that live in one person's head
- Submissions and paperwork that come back three times because something was missing
- Answers buried across shared drives, email, and old PDFs
- ChatGPT that knows nothing about your business
- Multi-step busywork: pull the data, draft the doc, update the record
What you get
AI that works like a teammate
With clear limits on what it can read, what it can do, and what needs a person's approval.
OpenClaw setup
OpenClaw, the open-source AI agent, deployed and configured around your tools and workflows.
Customer-facing chatbots
Answers customers' questions from your real information, and hands off to a person when it should.
AI document review
Submissions checked against your checklist, with exactly what's missing sent back before a person reviews it.
Drafting from your templates
Proposals, emails, and reports drafted from your own templates and past examples.
Multi-step agents
Agents that pull data, draft documents, and update records, with your team approving the output.
Why most AI assistants disappoint
The difference is how the knowledge is organized
Most AI knowledge tools chop your documents into small fragments and search for pieces that sound similar to the question. That works for simple lookups, but it breaks the moment an answer needs two documents at once, and it gets things wrong with total confidence.
I build it differently. Your knowledge gets organized and cross-referenced so the assistant reads whole documents and cites exactly where each answer came from. A procedure comes back complete, not in pieces, and anyone can check the source.
How it works
Organized first, automated second
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01
Gather what your team already knows
Documents, procedures, standards, and the answers people give over and over.
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02
Organize it so an AI can navigate it
Structured and cross-referenced, not dumped into a chatbot.
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03
Connect the tools and set the limits
What it can read, what it can do, and what needs approval.
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04
Train your team and keep it current
Documentation, training, and updates as the business changes.
Where it shows up
What this looks like
in your industry
Proof
Work I've shipped
AI review
AI document review assistant. An AI reviewer for incoming engineering requests. Submitters upload a proposal and get instant, structured feedback on what's missing or too vague, so requests arrive review-ready and the back-and-forth correction emails disappear.
Knowledge assistant
Natural-language data query. A pipeline that extracts clean, structured data from weekly operational reports into an internal AI assistant, so any manager can ask questions in plain English.
AI product
Bucket. A full AI product I built end to end. It classifies, summarizes, and categorizes everything people send it, and makes all of it searchable.
Let's talk
Let's find what's slowing
your business down
One conversation about how your business actually runs, then a written plan: what's worth automating, what it would save, and what isn't worth doing yet. The review is free, and if nothing is worth building, I'll tell you.