
Paul Chambers
Fractional AI & Automation Consultant
I help companies in the $1M-$10M range stop buying AI tools and start building AI systems that actually work. Most businesses at this stage have outgrown their informal processes but aren't ready for enterprise solutions. I come in as a fractional operator, build the automation and AI infrastructure their team needs, then teach the team to run it without me. Two layers: systems built, teams equipped.
Profile
Background, skills, and how I work.
What I build
Products I've designed, built, and deployed. Each one solves a real problem for a real user.

Voice-first PWA where a contractor talks into their phone and AI routes everything to the right place: materials to shopping lists grouped by store, dates to Google Calendar color-coded by job, tasks, or per-job notes.
Fuzzy entity resolution matches phrases like "the Kellogg job" or "Chris's place" to the correct project by injecting the active job list into the system prompt. A Claude Vision path turns site photos plus voice into structured scope with materials, code flags, and cost estimates. Meeting recorder runs Deepgram diarized transcription into summarized notes. Voice input queues offline for dead zones.
Crew management with row-level security so employees see only assigned jobs. Subcontractor directory with per-job placement.

Free AI resume diagnostic. Paste a resume and job description, get a match score (0 to 100) across four dimensions (keyword match, experience relevance, trajectory fit, ATS parsing), a GO / FIX FIRST / PASS verdict, and a downloadable ATS-safe Word doc rewritten to mirror the job listing.
The core constraint: no fabrication. A three-layer guard enforces it. System prompts restrict the model to user-provided evidence. A five-question intake surfaces real stories before tailoring runs. A separate fact-check pass verifies every rewritten bullet against the original resume, regenerating failures with per-claim feedback.
Company-targeting mode: paste a URL instead of a job description and get a tailored resume with positioning angle. Cost-tiered model use (Haiku for diagnosis, Sonnet for generation). Word-level diff shows what changed.

Chrome extension that captures open tabs, auto-categorizes them, and lets users annotate, snooze, and snapshot them into restorable sessions. Built for knowledge workers who keep dozens of tabs open as external memory and lose time re-orienting after each context switch. Where competitors save URLs, TabSquirrel saves context: what the user was doing and what comes next.
The build pairs a Manifest V3 extension with a React SPA over a postMessage bridge and externally_connectable, avoiding a hardcoded extension ID. Free-tier limits are enforced at the database layer through a Postgres BEFORE INSERT trigger using SECURITY DEFINER. A split tabs/tab_meta model with URL normalization handles dedup on import. Every table runs row-level security keyed to auth.uid().

Automated pipeline that ingests sales call transcripts, extracts structured coaching insights, grades each call A through F, and verifies every product claim against a 672-document knowledge base embedded in Supabase pgvector.
Built for the sales director at Tracker Products. Two AI stages: Gemini 2.0 Flash returns strict JSON (agency, persona, objections, competitor mentions, rubric score); a LangChain compliance agent checks each claim against the knowledge base, returning PASS, FLAG, or REVIEW with evidence quotes.
Reps contest results via tracking codes in Slack, where a router resolves disputes. Reliability includes scheduled batch runs, two-layer deduplication, malformed-JSON repair, and two-step archiving.

Interactive tool that audits recurring business processes for hidden cost. Enter a process with its frequency, headcount, duration, and what mistakes it prevents. The app returns a Keep, Compress, or Cut verdict with rationale and a specific recommended action.
Runs as a deterministic rules engine with no AI calls and no network requests. A cost model converts inputs to monthly team-hours using per-frequency multipliers, and a 40-hour threshold separates Compress from Keep. Rationale and actions generate from live numbers rather than canned text. Processes accumulate into a ranked summary table with clipboard export. Everything runs client-side, nothing saved or transmitted.
Built for operators and team leads who accumulate standing meetings, reviews, and reports that nobody re-evaluates because the cost sits in payroll hours and stays invisible.
Operating model
How AI fits into how I build, deliver, and run a business.
How has AI multiplied your output?
I built a sales call analysis system for a client where the sales director reviewed maybe 5 calls a week out of 40+. The automation now processes every call: transcribes via Deepgram, runs analysis through Gemini, cross-references product claims against 672 documents in a vector database, and delivers graded reports same-day. Coverage went from partial to complete overnight.
On my own business: 100+ Claude skills chain together so a single command runs topic origination, dedup against existing content, script generation, slide production, and lead magnet specs. A full day of content production per video is now about two hours, mostly review and recording.
Describe a task where you wrote a spec for an AI agent and it executed autonomously.
I had a 52-task backlog spanning content production, workflow automation, data pulls, and infrastructure fixes. I wrote a universal batch execution wrapper for Cowork (Claude's autonomous surface) that triages every task silently, loads context conditionally, runs preflight checks, then executes in parallel. The spec: never ask questions mid-run, route blockers to a REQUIRES_PAUL block, skip over fabricate, end with a six-section status report.
The agent classified each task as SKIP, EXECUTE, or PARTIAL, handled what it could, and produced a clean report of completions, skips, and blockers. I reuse the same wrapper by appending a new task list.
What's your delegation radius?
I delegate to AI: first drafts of all written content, code generation, research synthesis, workflow building, data extraction, scheduling, and repetitive file operations. I have six named agents handling different surfaces (Slack relay, outreach automation, YouTube production, general execution).
I review: anything buyer-facing before it publishes. Voice and tone are where AI drifts fastest, so every LinkedIn post, email, and script gets a human pass against documented quality gates.
I won't delegate: strategic decisions about positioning, pricing, or which clients to pursue. Client conversations. Anything requiring judgment about relationships or trust. The "should we" questions stay with me. The "how do we" questions go to AI.
What will you delegate to AI in 12 months that you can't today?
Full-cycle lead research through qualification. Right now I use AI for individual steps (Apollo pulls, enrichment, ICA scoring) but a human stitches the pipeline together. In 12 months I expect to hand an agent a target profile and get back qualified leads with personalized outreach drafted and staged.
Multi-agent content production where I approve a topic and the system produces script, slides, lead magnet, distribution copy, and thumbnail with one review pass instead of five sessions.
Real-time client system monitoring where agents flag anomalies in deployed automations before the client notices. The pieces exist. The orchestration layer that ties them into a reliable autonomous loop is what's still being built.