Case study
Reducing Client Intake Friction with AI
A repeatable intake pipeline that cuts context loss and makes proposals faster to scope.
- Cliente
- Small studio / consulting-style engagement
- Ruolo
- Lead engineer and workflow designer
- Durata
- 3 weeks
- Pubblicato
- 2026-03-14
Contesto
Da dove e partito il lavoro
Requests arrived from multiple channels, and the team had to rebuild context before every proposal.
Problema
Cosa doveva cambiare
Context was fragmented, ownership was unclear, and every quote required manual reconstruction.
Vincoli
Cosa ha formato la soluzione
- Keep human review before client-facing output
- Do not over-automate qualification
- Preserve searchable delivery notes
Processo
Come l'ho attraversato
- 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.
Soluzione
Cosa e stato pubblicato
Built a structured intake and triage system with clear handoff points, reusable templates, and reviewable output.
Risultato / Impatto
Cosa e cambiato
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.
Riflessione
Cosa ho imparato
- AI works best here as a drafter, not a decision maker.
- The schema is the system boundary.
Progetto correlato
AI Client Intake System
A structured intake and triage workflow that turns messy client requests into scoped delivery plans.
Vedi progettoServizi coinvolti