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
Context
Where the work started
Requests arrived from multiple channels, and the team had to rebuild context before every proposal.
Problem
What needed to change
Context was fragmented, ownership was unclear, and every quote required manual reconstruction.
Constraints
What shaped the solution
- Keep human review before client-facing output
- Do not over-automate qualification
- Preserve searchable delivery notes
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.
Solution
What shipped
Built a structured intake and triage system with clear handoff points, reusable templates, and reviewable output.
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.
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.
View projectServices Involved