Inbo.

The problem

Small business owners run client relationships across email, phone calls, texts, and memory. Call details never get captured, context scatters, follow-ups get forgotten, and deadlines turn fuzzy.

Before the next reply or call, the owner wastes time reconstructing the client story — searching email or relying on memory. As the narrative puts it, owners already use memory as their CRM, but memory has terrible search and no backup. Most AI email tools only summarize the inbox and miss the context that gets lost when work moves to a phone call.

The solution

INBO is an AI client cheat sheet. After a call, the owner selects the client and saves a quick note; INBO connects that note with existing email history to generate a source-backed client brief showing current status, what changed, risks, deadlines, and a recommended next step.

The key differentiator is capturing the missing call-note context and turning all of it into a client cheat sheet. It's designed mobile-first for owners moving between meetings and job sites, and the brief is always traceable back to its sources so a busy owner can trust it quickly.

How it works

INBO uses lightweight RAG over call notes and email records. The MVP stores both emails and call notes in a single unified context_items table in Supabase, with seeded Gmail-like records and user-added notes. Brief generation runs through an OpenAI gpt-4o-mini edge function — chosen for being fast, cost-effective, and strong enough for structured summarization, extraction, and RAG-style generation over small retrieved context sets.

The system prompt instructs the model to use only retrieved context_items, avoid inventing facts, prefer recent source dates and call notes, and show sources. Iteration moved it from generic brief generation to lost-context recovery, and removed unreliable blank sections. Human-reviewed scenario testing achieved 6/6 usable results, meeting the 80% MVP threshold.

Who it's for

INBO is for external, B2B/prosumer users — small business owners, consultants, contractors, freelancers, and small service teams who manage client relationships directly and own the follow-ups and deadlines.

The primary persona is the mobile small business owner managing clients from calls, email, and memory while moving between meetings, job sites, and appointments. The persona is a composite informed by interviews with a former sales VP, a contractor and mobile Gmail user, and a new business owner.

Why it matters

INBO targets the large and growing segment of small service businesses, independent consultants, and solo operators who need to remember client context but don't want a heavy CRM — a demand fed by AI assistants, mobile-first work habits, and comfort with lightweight automation. The potential model is freemium or subscription SaaS, with paid tiers for more clients, higher brief limits, and stronger Gmail and Zapier capture.

At prototype/MVP stage, the goal is to validate the core loop — Add Call Note, Retrieve, Brief — before scaling. Success is measured by call notes saved, briefs generated, repeat usage, time saved preparing for follow-ups, and owners' reported confidence before replies and calls.

At a glance

Project

Inbo

Built by

Inbo

One-liner

Inbo is an AI client cheat sheet for small business owners who lose critical client context across calls, emails, and follow-ups.