A Pinterest-style hub that holds the content, a studio that creates it, and an AI that answers any question.
Designed by Sandeep Karnati
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The problem
The usual way
Beakn
How it all fits
👆 Tap a step to see what happens there.
Feel it
💬 a customer, typing:
✨ the answer, grounded in the knowledge bank:
✅ Yes — 16A relay per channel handles 1.5-ton ACs (7–10A draw) [1]
🔌 No neutral required — works with both wirings [1]
🛡️ 4KV surge protection, isolated SMPS [2]
— Source: LPF 6 Switch · Specs · FAQ (tap to open)
Chapter 2
A masonry feed of every product and feature — videos, photos, reels, files and live pages, flowing in gap-free. Browse freely; sign in only to ask.
👆 Tap a room.
It all feeds one knowledge base — which the studio writes and the AI answers from.
Chapter 3
Images, web pages, articles, videos and reels — each generated for your real product, with its own set of creative controls. Tap a type.
Chapter 4
Decode a reference once, then apply it to any product, forever. Tap each step.
👆 Tap a step in the studio pipeline.
It never invents the device. A vision model writes a "visual spec" from the actual product photo, then that photo is handed to the image model as a reference — so it renders YOUR product, not the reference's.
Per-medium fields — occasion, style, shot, lighting, hook, format, layout… — each backed by a researched phrase library so a tap becomes a rich instruction.
Articles & pages pull live keywords (DataForSEO, India/English) scoped to the product & category, with a one-click remove for competitor terms.
Place the Beakn logo cleanly in a corner (composited for real), or generate logo-free — with optional designed captions when you want text.
Off → subtle → medium → wild: sample fresh style modifiers so the same recipe yields a different, on-brand take every time. One-tap reroll.
Videos aren't just scripts — they're rendered for real with Veo, polling until the finished clip downloads to the media library.
Chapter 5
A great prompt shouldn't be a one-off. Capture it, name it, star it, and re-cook it for any product on demand.
Any decoded or hand-tuned prompt is saved as a recipe with its medium, facet tags, reference thumbnail and a use-count — then re-applied to a different product in one tap, filling that product's real context.
A searchable, paginated library of recipes and creations — filter by medium, product, starred or facet; sort by newest, most-used or A–Z; and compare prompts side by side.
Prompts use {{product}} / {{feature}} / {{category}} variables; missing values are cleanly stripped so a raw token never reaches the model. Open straight into ChatGPT, Gemini, Claude, Midjourney, DALL·E or Sora.
Every generation is kept with its product, recipe and settings — approve or keep as draft, delete, or turn an image straight into a card on the product. Output is saved to media with a reference link.
Chapter 6
Before the AI can answer, every product's knowledge is distilled and indexed. This is how.
A tight 200–300 word overview of each product, category and feature — the fast facts.
A full document (overview, specs, features, use-cases, installation, FAQ, compatibility…) generated from the category source + specs, with versions.
A vision model describes the product from its own photo, so generated content and answers match the real device.
Drafts can be seeded from a product's cards — or extracted from uploaded PDF & DOCX spec sheets — then edited and published.
👆 Tap a step of the pipeline.
See what's indexed per product, category and feature — never / stale / indexed, with last-indexed dates — and re-index everything or just one product in a click.
A dedicated tab to browse every indexed chunk, with search, filter by source, sort and pagination — so you can see the AI's raw knowledge.
Edit any answer in the Conversations log; it's pinned as a canonical source that survives every re-index — a quiet "human corrects the AI" loop.
Questions the knowledge base can't answer are logged as gaps in Analytics — a to-do list of content to create next.
Chapter 7
A self-hosted RAG pipeline — running on your own server, not a black box.
👆 Tap any block of the pipeline.
A simulation of how the right knowledge lights up.
Chapter 8
Every claim comes with its source, and a human is always one tap away. Watch an exchange replay:
Only the chunks the answer actually used are shown — numbered [1][2], each linking straight to that product page.
Thumbs-down answers and "talk to a human" requests land in a support inbox with the full transcript.
If the knowledge base has no answer, it says so and logs a content gap — instead of guessing.
A semantic cache recognises the same question asked differently and answers in a blink — no model call.
Follow-ups like "and the 4-channel one?" understand the conversation so far.
An account is required to ask; customers get a daily question allowance; staff are unlimited.
Chapter 9
Some brains run on your own server; the writers are swappable from Settings — and you pick the best by comparing them.
Turns every knowledge chunk and every question into a meaning-fingerprint, stored in a self-hosted vector database.
A cross-encoder that re-reads the candidate chunks against the question and keeps only the truly relevant ones. Runs on your hardware.
The models that write answers and content — any of them, routed per purpose (chat, knowledge, studio) through one gateway, switchable without touching code.
A vision model describes each product; image models render it; Veo renders the videos. Provider keys are stored encrypted.
⚖️ Compare mode: run one question through several writers side-by-side, see each answer's speed and cost, and lock the best as the default — with per-request token & cost tracking (live OpenRouter pricing) and today / month spend dashboards.
Chapter 10
The same brain, surfaced everywhere it's needed. Tap a surface.
For customers
For the team
👆 Tap any surface above.
Chapter 11
Every feature shipped through an enforced pipeline. Nothing is "done" until it's verified live.
Tracked in Linear · versioned on private GitHub · deployed behind a Cloudflare tunnel · images auto-optimised to tiny WebP (a 7 MB render → ~15 KB).
A Pinterest-style feed of every product & feature — cards, profiles, categories, boards, reels, packs.
The Creative Studio: decoders for images, pages, articles, videos & reels, with recipes, SEO & real generation.
Per-product knowledge banks (meta + write-up + visual spec), then indexed into a vector store.
A self-hosted RAG stack — Qdrant + a cross-encoder reranker + embeddings + hybrid search.
Grounded streaming answers with citations, a semantic cache, compare mode, and cost dashboards.
Semantic search, an AI product-compare, feedback & escalation, My Questions, and a WhatsApp assistant.
Content, creation and support — one hub, one brain, getting smarter with every question asked.
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