Case Study
Reducing Client Intake Friction with AI
A repeatable intake pipeline that cuts context loss and makes proposals faster to scope.
- Client
- Small studio / consulting-style engagement
- Role
- Lead engineer and workflow designer
- Duration
- 3 weeks
- Published
- 2026-03-14
Evidence layer · Context
Where the work started
Requests arrived from multiple channels, and the team had to rebuild context before every proposal.
Evidence layer · Problem
What needed to change
Context was fragmented, ownership was unclear, and every quote required manual reconstruction.
Evidence layer · Constraints
What shaped the solution
- Keep human review before client-facing output
- Do not over-automate qualification
- Preserve searchable delivery notes
Process layer · Process
How I moved through it
- Mapped request sources and failure points.
- Defined a small schema for project context, constraints, and urgency.
- Added AI-assisted classification and summary drafting.
- Designed review checkpoints before anything was sent to clients.
Process layer · Solution
What shipped
Built a structured intake and triage system with clear handoff points, reusable templates, and reviewable output.
Outcome layer · Result / Impact
What changed
The team got a calmer client intake flow, faster scoping, and fewer back-and-forth loops before kickoff.
Scoping became less dependent on memory and more dependent on clear context.
Outcome layer · Reflection
What I learned
- AI works best here as a drafter, not a decision maker.
- The schema is the system boundary.
Related Project
AI Client Intake System
A structured intake and triage workflow that turns messy client requests into scoped delivery plans.
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