Writing is everywhere — email, chat, docs, code comments.
People expect instant, context‑aware suggestions that feel natural.

Our product: a compact, privacy‑friendly Next‑Word Predictor that plugs into a Shiny app or any editor via API to: - Speed up writing with fewer keystrokes and fewer typos
- Keep data private (runs locally; no third‑party calls)
- Stay transparent (interpretable, tunable n‑gram model)

Tip: press f for fullscreen, w for widescreen while presenting.

Pipeline at a glance 1. Clean & normalize text; map rare tokens to unk
2. Build n‑grams (uni/bi/tri; 4‑grams optional) and keep only top continuations per history
3. Compute two signals:
- Stupid Backoff (SB): fast back‑off 4→3→2→1 with factor α
- Modified Kneser–Ney (KN): continuation‑aware counts for rare contexts
4. Mixture model: p = w·p_KN + (1−w)·p_SB (tune α and w on validation)
5. Serving: bound candidates, score, sort → Top‑k suggestions in milliseconds

Why this design?
- Fast & lightweight for laptops and VMs
- Accurate enough for real typing, even without huge neural models
- Interpretable & debuggable for enterprise adoption

Shiny app demo flow (90–120 s) 1. Type: “this is a” → show Top prediction and Top‑k bar
2. Toggle Model: Mixture ↔ KN ↔ SB → compare ranks & confidence
3. Show uncertainty (entropy/margin) + latency badges
4. Point out lightweight model (quick load, small footprint)

Everyday value - Less friction: auto‑completes common phrases, handles rare ones gracefully
- Consistent language: encourages standard terms and spellings
- Works offline: ideal for privacy‑sensitive or low‑connectivity settings

Benefits teams care about - Productivity: fewer keystrokes and faster composition
- Quality: fewer typos; more consistent terminology
- Cost & risk: zero external API dependency, no token bills, on‑prem ready

How you’ll measure it in a pilot - Keystrokes saved and time‑to‑compose (instrumented in app)
- Accuracy@k and perplexity on your domain text
- Latency (ms/query) and model size (MB) for deployability

Action: pick 1–2 target teams (support, sales, engineering docs) and measure before/after.

Roadmap - Domain adaptation: fine‑tune on your corpora for higher relevance
- Personalization (opt‑in): per‑user re‑weighting without storing raw text
- Smarter OOV: subword fallbacks for novel words or names
- Editor integrations: API/plugin for Outlook, VS Code, internal CMS

Ask - Approve a 4‑week pilot with success metrics (keystrokes, time saved, accuracy, latency)
- If targets are met, green‑light rollout across high‑impact teams

Contact: prasant@example.com • Pilot playbook available on request