Fractional AI Architecture · Richmond, VA
I'm Will Cassell — a former data science & analytics leader who now builds AI agents and systems hands-on. I find the business problem first, then assemble the AI, data, and automation stack that actually solves it.
01 — The Gap
Companies keep getting caught between the two: advice with no implementation, or implementation with no business judgment. The work that moves a P&L needs both halves in one head.
| Provider | Advice | ImplementationBuild | Owns the outcomeOwns |
|---|---|---|---|
| Strategy firmsDeck, no build | Yes | No | No |
| Dev shopsCode, no judgment | No | Yes | No |
| This practiceBoth halves, one head | Yes | Yes | Yes |
Years running data science and analytics organizations at the SVP level — budgets, org change, vendor selection, and the politics of getting new systems adopted rather than shelved.
Daily, hands-on work with LLM agents, voice systems, data pipelines, and workflow automation. When I recommend something, it's because I've already built a version of it.
02 — The Engagement
A fixed-fee, three-week engagement on one department or process. You leave with a ranked roadmap and a working pilot of the top opportunity — not a recommendation to go build one.
Interviews with your team and a workflow map of the target process: where time, money, and errors actually accumulate.
Every opportunity scored on ROI against feasibility — then I build a working pilot of the one at the top of the list.
A 90-day roadmap, the live pilot demo, and a decision-ready business case your leadership can act on immediately.
03 — Recent Builds
AI agents that place real phone calls and run structured interviews end-to-end — transcripts, scoring, and follow-ups landing in a database, no human dialing.
A plain-language assistant on an organization's live job data. A frontline rep asks what's overdue or next and gets their own worklist and task checklists; a manager switches roles to see the whole operation — which region's behind, which client's at risk — and can push an action back into the system. Real tool-calls against the data, not guesses.
Data pipelines that turn messy public records into ranked, decision-ready intelligence — the kind of signal a sales or underwriting team can act on weekly.
A private dashboard for a fleet of AI agents running across machines. It rolls every agent's usage logs into one view — tokens, cost, tool activity, reliability, by project and machine — so you can see whether the automation is earning its keep. Runs entirely locally; no keys, nothing leaves your infrastructure.
Any of these are available to be demoed on initial call.
04 — After the Audit
When the audit surfaces more than one thing worth building, I stay on as your fractional AI lead — owning the roadmap, building the pilots, selecting the vendors, and upskilling your team on a monthly retainer.
I hold two retainer seats at a time — one is open right now. That constraint is the point: you get a senior operator's attention, not a bench of juniors.
1 of 2 filled. One seat open now — senior attention by design, never a bench of juniors.
05 — About
For 25+ years I led data science and analytics organizations, most recently at the SVP level — the years where you learn that technology fails for organizational reasons more often than technical ones.
Now I work as an independent architect because I like the craft: finding the business problem, then personally assembling the AI, analytics, and automation blocks that solve it. CR Tech Lab is the practice that work lives under.
06 — Contact
Tell me what's slow, manual, or opaque in your operation. If there's a fit, we'll scope an audit in one call — and if there isn't, I'll tell you that too.