The knowledge and integration systems your business runs on.
Custom AI systems that move your information where it needs to go: a call mention into the CRM, a competitor's move into your intelligence report, the week's commitments onto your calendar.
Talk about a buildScoped and priced before you commit · working output in weeks.everything I do →
01 · Where it sits
Everything on this page bolts onto one core: the knowledge system, a governed workspace where the business already runs. Governed means it loads what you've settled before it acts and flags what conflicts. That page sells the core. This one is about what gets built around it.
A build doesn't require the core. If what you need is one connection built well, a roofing company's lead forms wired into its CRM, a storefront's orders flowing into a weekly report, I build that standalone, scoped and priced the same way. The rest of this page is the stronger version: the same builds socketed on the core, running as one operation.
02 · Built and running
The core holds the judgment. The spokes do the work. Each spoke is an app socketed into the same files, and the traffic runs both directions: mention a lead on a call and it lands in the CRM; settle something in conversation and the next session opens already knowing it.
Bolt forty automations onto a business and you hold forty tools, none aware of the rest. Socketed on one core, they run as one operation: every app reading the same settled judgment, writing back to the same files. Integration is table stakes now. Plumbing that's governed is the part you can't buy.
Nothing on this page is a concept. Every system below runs in production today, on my own business and on client work.
The person who built these is the person you'd be talking to.
Already know the part of your operation this should sit on?Talk about a build
03 · AI access to your proprietary information
The fourth spoke: AI with access to what's yours. Sales transcripts, outreach threads, client data, contracts, years of files. All of it queryable through a chatbot that answers only from what you hold.
Ask what's closing deals and what's losing them. Ask what a client actually agreed to in March. Ask what you quoted last time, and why. The answer comes back from your records with the source attached.
Under the hood this is the retrieval engine I run my own vault on, and it earns trust three ways:
- Routed by intent. Every query gets classified and sent to a pipeline built for that intent. Keyword, meaning, file path, and your own link structure fuse into one ranking.
- Ranked by your trust. The authority you've assigned your records boosts what you rely on over what you scratched down. The fashionable component, a neural reranker, is built, benchmarked, and switched off: on a real corpus it measured worse than the boring math.
- Measured before you rely on it. On my own vault, against a 107-question test set, the right record lands in the top five 88.8% of the time. Your build gets an acceptance set from your real questions, run against your own records.
And because every answer traces to its source, it holds up in front of an auditor, and it can ship inside your product.
04 · The sandbox: why it's safe to ship
The reasonable fear about AI inside your operation, and it is reasonable, is the model touching what it must never touch: the books, the client records, the source of truth.
I take the fear seriously because I've collected the failures firsthand. A history rewrite that reported success while quietly deleting thirty-one files of working rules. A pasted credential that an automation faithfully committed and pushed before any human saw it. Nothing crashed either time; the dangerous commands are the ones that report success.
The answer here is structural. For specified areas, the system is set to read-only. And where it matters most, read-only lives outside the model entirely: the connection to your books is made with a credential that can only read. There is no write path to talk the system into.
The model can summarize. The record stays untouched.
Every answer assembles in separate lanes. Two of them never touch a language model, so your source of truth stays structurally out of the model's reach. A control you can demonstrate to a regulator, line by line.
And where writing is the job, every write lands in version history: visible, attributable, reversible.
05 · Where the wires reach
The spokes above are mine. The same rails reach the platforms your business already runs on, and they run outbound too.
Cold outreach as a system: it reads your market from the intel engine, drafts in your voice, works the queue, and logs every touch to the CRM. Socketed on the same core, so it already knows what you've settled about who you sell to.
In flight now: Amazon Seller and Ads APIs for an ecommerce operator. The same pattern reaches Meta and the ad platforms, Shopify and the storefront layer, payments.
06 · How a build goes
No build starts from a spec template. It starts with the system learning you: how you decide, where the work actually lives. From there, every build follows the same sequence:
Two ways to point what we build:
- Your own operation (internal). The system runs over what you hold and answers to you. The fast, low-risk place to start.
- Inside your product (customer-facing). Ship it to the people who use your software. Built for regulated settings, where a wrong or invented answer is a liability.
Every engagement is scoped and priced before you commit. No open-ended retainer, no surprise hours. You see the price and the deliverable before anything starts, and working output lands in weeks. Bigger systems mean bigger scope; the model holds.
07 · Work together
The fast lane is the build intake: it asks for your stack, the bottleneck, and the data involved, and I come back with a scope. Prefer to start smaller? Tell me what you're trying to build, or the part of your operation nothing off-the-shelf fits, and I'll tell you whether I can build it and what it would take.
Or start at the bottom of the stack: the core all of this sockets into.