Inboxintel/docs/discovery/07-ai-feature-catalogue.md

07 — AI Feature Catalogue (Phase 6)

Every realistic AI feature, judged honestly against the guiding rule: AI only where it clearly beats traditional approaches. Each entry: problem · AI role & why · could traditional solve it? · complexity · performance · privacy. All AI is local (Ollama), optional, advisory, and explainable (see 06). Complexity = S/M/L/XL.

Legend for the verdict column: 🟢 AI clearly wins · 🟡 traditional-first, AI for the hard tail · ⚪ not AI at all

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Group 1 — 🟢 AI clearly wins

LLM/embeddings are genuinely the right tool; traditional approaches are weak here.

Feature	Problem	Why AI (and why traditional falls short)	Cx	Perf	Privacy
Thread / conversation summary	Long threads are walls of text	LLMs summarise free text; regex/extractive summaries miss nuance & context	L	Warm 7B; cache per-thread, invalidate on new msg	Body → local LLM only
Conversational "ask your inbox" (RAG)	"What did Sarah say about the invoice?"	Retrieval + generation over many emails; impossible with filters alone	XL	Semantic retrieve → 7B answer w/ citations; ~1–3s	Retrieval + gen fully local
Reply suggestions / writing assistant	Blank-page drafting, tone	LLM drafts context-aware replies; templates can't adapt to content	L	Warm 7B, streamed	Thread context → local
Task / meeting / calendar / reminder extraction	Commitments hide in prose	LLM structured-JSON extraction of {task, date, attendee}; regex catches only rigid formats	L	7B format:json, async on read/sync	Body → local
Entity extraction (amounts, orgs, dates, order #s)	Can't search/facet by meaning	LLM/NER generalises across phrasings; regex is brittle per-vendor	L	7B or small NER, batched at sync	Local
Document / attachment understanding	Can't search inside files	OCR/vision + summarise; no traditional equivalent for images/PDF meaning	XL	Vision model on-demand (heavy); OCR async	File content → local
Cross-thread linking / related conversations	Related context is scattered	Embedding nearest-neighbours find semantic links; keyword join misses paraphrase	M	pgvector HNSW; precomputed	Vectors local
Relationship mapping / knowledge graph	No view of who/what connects	Extraction + embeddings build a people/topic graph; not expressible in SQL alone	XL	Batch build; incremental	Local graph store
Conversation insights (decisions, sentiment shift)	"What was decided / how's this going?"	LLM reads intent/sentiment over a thread; rules can't	L	7B; cache	Local
Sentiment analysis	Gauge tone (angry client?)	Small model/LLM classifies tone; lexicon methods are crude/misleading	M	small model or embeddings	Local
Email comparison ("what changed vs last quote?")	Manual diffing of prose	LLM semantic diff; text-diff shows characters, not meaning	M	7B on two bodies	Local
Explain search results / decisions	Trust & learnability	For semantic/NL, only the model can say why; lexical uses ts_headline (non-AI)	M	cheap (reuse retrieval)	Local

Group 2 — 🟡 Traditional-first, AI for the hard tail

Heuristics/rules do 70–90% cheaply and instantly; AI handles ambiguity and adds explanations. The existing HeuristicClassifier and unsubscribe signals are the traditional backbone.

Feature	Problem	Traditional core	Where AI adds value	Cx	Perf / Privacy
Automatic categorisation	Sort inbox into buckets	Rules on sender/domain/headers (exists)	Embedding zero-shot / small-LLM for the ambiguous long tail + confidence	M	Rules instant; LLM only on "unknown"; local
Smart filing / smart labels	Where should this go?	Rules + user's past filing patterns	LLM/embedding suggestions with confidence, user-correctable	M	Suggest async; local
Priority prediction	What needs me now?	Behavioural signals: your reply-rate to sender, frequency, VIPs, keywords, direct-to-me	ML/LLM refines ranking for edge cases	M	Mostly SQL/heuristic; local
Follow-up / awaiting-reply detection	Dropped balls	Heuristic: you sent, contains a question, no reply in N days	LLM confirms "expects a reply" & drafts nudge	M	Heuristic instant; LLM optional; local
Smart notifications	Notification fatigue	Rules over priority + quiet hours	LLM tunes "is this actually urgent" for borderline	S	Rules-first; local
Spam detection	Junk	Rules/Bayesian + provider signals	Small model for novel spam; LLM explains	M	Fast; local
Phishing detection	Safety	URL/domain analysis, SPF/DKIM hints, lookalike detection (+ existing SSRF guard)	LLM reasons about social-engineering cues; runs async on flagged mail, explains risk	L	Rules sync; LLM async on suspicious; local
Duplicate email detection	Clutter / repeats	Exact hash for identical	Embedding cosine for near-duplicates	M	hash instant; vector cheap; local
Inbox assistant (daily brief)	"Catch me up"	Compose from priority/follow-up/counts (rules)	LLM writes the natural-language brief over that structured data	L	7B once/session; local

Group 3 — ⚪ Not AI (don't waste VRAM)

Feature	Do it with	Why not AI
Language detection	fastText-lid / CLD3 library	Instant, accurate, ~0 VRAM; an LLM is pure overhead
Exact duplicate detection	content hash	Deterministic and free
Unsubscribe detection	List-Unsubscribe header parsing (exists)	Structured signal already present

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Selection guidance (feeds the roadmap)

• First AI wins (highest value / lowest risk): thread summary · follow-up detection (heuristic + AI confirm) · reply suggestions · NL search parse. All reuse the one warm 7B.
• Semantic tier (needs pgvector + embeddings): related/find-similar · near-dup · conversation insights · categorisation long-tail.
• Ambitious tier: ask-your-inbox (RAG) · knowledge graph · attachment/vision understanding.
• Never gate the core on any of these — each has a non-AI fallback or simply hides when AI is off.

Privacy posture (applies to all)

Email bodies and attachments are processed on-device via Ollama; embeddings, summaries, extractions, and graphs are stored locally in Postgres. No content leaves the machine unless the user deliberately configures a cloud provider — and even then, per-feature consent should gate it. This is the product's defining trust advantage (see 02).