Dave Chapman

AI, Agents & Automation for Real Business Work

Work

Proof logs, not portfolio tiles

Each project shows the bottleneck, what changed, what was built, and the outcome.

Bottleneck -> Boundary -> Build -> Measure

Laptop and documents showing workflow analysis and system design

Case studies organized around the actual bottleneck

The stories below are built to show the constraint, the intervention, and the measured effect.

AI-assisted support ticket triage

Median first-response handoff dropped to under 20 minutes in pilot runs.

Problem
Support teams re-read similar tickets and escalated too early.
Decision boundary
Automation classifies and drafts routing; team leads own final priority decisions.
Mechanism
Rerouted mixed-quality inbound tickets into confidence-ranked lanes.
Constraint
Low-confidence tickets always route to manual review. No silent auto-close.

Open case study

Agentic development workflow with human checkpoints

Pull request cycle time fell 28% on bounded task classes.

Problem
Teams lost cycle time to repeatable setup, regression checks, and boilerplate updates.
Decision boundary
Agents can propose and implement scoped changes; merge decisions stay human-owned.
Mechanism
Reduced review latency by automating repeatable implementation and verification loops.
Constraint
All generated changes require deterministic checks and reviewer approval.

Open case study