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Executive Summary — InboxIntel Discovery Blueprint
Ambition: become one of the best email search and management experiences available — fast, scalable, secure, intuitive — with AI used only where it clearly beats traditional approaches, running locally and privately, and never as a hard dependency.
The opportunity (the wedge)
The market has split in two, and both halves leave a gap:
- AI, but cloud (Gmail/Gemini, Outlook/Copilot, Shortwave, Spark) — useful, but your mail is processed off-device.
- Private, but little/no AI (Proton, Thunderbird, Fastmail).
No mainstream product owns "genuinely useful AI that runs on your own machine." InboxIntel can — and it already has the seams for it. Combined with three more openings — approachable power-search as the home screen, explainability ("why this matched / was categorised"), and fast and friendly (Superhuman is fast-but-intimidating; casual apps are friendly-but-slow) — this is a defensible, differentiated position.
Positioning: Make search the fastest way to think about your inbox, with AI that runs on your own machine and always explains itself. Powerful for pros, simple for anyone.
Current state — verdict: extend, don't rewrite
A clean, secure, well-tested .NET 8 / React foundation with real Postgres full-text
search and an AI provider abstraction already in place (NullAiProvider /
OllamaProvider / OpenAiProvider). The gaps are exactly where the product wants to win:
relevance-ranked multi-mode search, a richer AI contract (embeddings/extraction),
conversation intelligence, and a modern approachable UX. None require a rewrite.
Design direction (from the interview)
Notion/Arc-professional, "anyone can pick it up": pointer-first & discoverable
(palette/shortcuts as accelerators), balanced density, dark-first (genuine light
too), subtle motion, green #3ba31f accent, clean rounded line icons, fully
responsive desktop→mobile. The pivotal decision — discoverable-first over keyboard-first
— reframes search from "a syntax you learn" into "an inviting, assisted experience."
Search & AI in a nutshell
- Search: a 4-layer engine — Structured → Lexical+Ranking (always on) → Semantic (pgvector) → AI-assisted (NL / ask-your-inbox / explanations) — with hybrid RRF ranking replacing today's date-only sort. Degrades gracefully with AI off.
- AI: extend the abstraction to embeddings + structured output behind a task facade with config-driven model routing; on the RTX 3080 (10 GB), Qwen2.5-7B (warm) + nomic-embed-text (hot) do most jobs, vision on-demand. Traditional-first everywhere; every AI feature has a non-AI fallback.
Roadmap shape
v1.0 redesign + world-class deterministic search + AI foundation → v1.1 assisted search & productivity → v1.2 semantic tier → v2.0 ambitious AI (RAG, vision, graph) → v3.0 platform (collaboration, rules, plugins, mobile). Sequenced so deterministic value ships before AI, infrastructure is amortised, and every release is complete with AI disabled.
Key risks
Migration of a full redesign (mitigated by strangler + ui.v2 flag), VRAM limits
(7–8B sweet spot + load policy), prompt injection (AI advisory-only, never acts), and
scope creep (value/effort-sequenced roadmap). See 11.
Recommendation
Proceed to v1.0.0 — the redesign, ranked/assisted search, and AI foundation. It's the biggest quality jump, it's low-risk and deterministic, and it stands entirely on its own without AI. Then layer local, private, explainable AI in value order.
Read the full blueprint
01 Architecture · 02 Competitors · 03 Users · 04 UX + Design System · 05 Search · 06 AI Strategy · 07 AI Features · 08 Architecture · 09 Roadmap · 10 Git Plan · 11 Risks & Future · Design Brief