Fractional AI Architecture · Richmond, VA · From $7,500
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 build the system that fixes it.
30 minutes. Tell me what's slow, manual, or opaque in your operation — if there's a fit, we'll scope an audit on that call, and if there isn't, I'll tell you that.
Background on LinkedIn ↗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 leading data science and analytics organizations — 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, a governance & risk snapshot — data handling, vendor exposure, AI-use guardrails — and a decision-ready business case your leadership can act on immediately.
03 — Recent Builds
A plain-language assistant on a company's live job data. A frontline rep asks what's overdue or next and gets their own worklist and checklists; a manager switches roles to see the whole operation — which region's behind, which client's at risk — and can push an update back into the system. Real lookups against the data, not guesses.
Proof Sample field-service operation: 50 staff, 800 jobs, 2,800 tasks · shown live on the call
Hundreds of pages of scanned factory manuals turned into a reference tool: pick a part or describe a symptom, and get the answer, the service steps, and a link to the exact manual page it came from. Every answer is cited, so nobody has to trust it blind. The proof case is a 1970s farm tractor; the same pipeline fits any equipment, maintenance, or SOP library.
Proof 330+ manual pages indexed, 160+ parts, 50 diagnosed symptoms · live at mytractors.app ↗
Applicants who've already applied get a short, structured screening call from an AI that says it's an AI. A few yes-or-no qualifiers — availability, work authorization, physical requirements — with the transcript and a PASS or REVIEW flag waiting for your recruiter. A person makes every hiring decision.
Proof Real phone calls completed end-to-end, answers structured automatically · screening flow shown on the call
Pipelines that pull messy public permit records from multiple cities, match the same contractor across spellings and jurisdictions, and produce a ranked leaderboard of who is actually active — the list a sales or underwriting team would otherwise build by hand.
Proof 1.2 million real permit records, two cities · runs offline on the call
I'll demo any of these live on the first 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 executive 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 about it. If there's a fit, we'll scope an audit in one 30-minute call — and if there isn't, I'll tell you that too.