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
Livello evidenza · Contesto
Da dove e partito il lavoro
Requests arrived from multiple channels, and the team had to rebuild context before every proposal.
Livello evidenza · Problema
Cosa doveva cambiare
Context was fragmented, ownership was unclear, and every quote required manual reconstruction.
Livello evidenza · Vincoli
Cosa ha formato la soluzione
- Keep human review before client-facing output
- Do not over-automate qualification
- Preserve searchable delivery notes
Livello processo · 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.
Livello processo · Soluzione
Cosa e stato pubblicato
Built a structured intake and triage system with clear handoff points, reusable templates, and reviewable output.
Livello risultato · 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.
Livello risultato · 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