01The parts bin
Every project, stripped of its current purpose and reduced to the primitives it contributes: data, audience, infrastructure, models, content.
The one truth that shapes everything: the proprietary data moat is thin — every product is seed-stage. Only one holds a real dataset (6,087 institution reviews). So the advantage is not data — it is a rack of working, deployed engines: a live generative-engine-optimization monitor, a live RAG + chat-widget + CDN stack, a live AI-audit → report generator, and search infrastructure, all already running on one paid-for server. The play is to recombine engines, not sell data.
| Project | What it is today | Primitives it contributes |
|---|---|---|
| Tobalt Chat Cloud | Multi-tenant embeddable AI chat widget | A. Chat widget + CDN delivery · B. RAG pipeline (doc→chunk→vector)+LLM · C. human-handoff console · D. per-tenant demo auto-provisioning |
| "elmo" (mislabelled "geo" — not geolocation) | Generative Engine Optimization monitor: prompts, runs, citations, competitors, reports | E. scheduled LLM-prompting + answer parsing · F. citation / share-of-voice tracking · G. report generation · multi-tenant auth |
| Viešapatirtis | Institution / employer reviews — 6,087 entities indexed | H. the one real dataset · I. instant faceted search · rating engine |
| Nuomopatirtis | Landlord / tenancy review platform | J. AI-guided review-intake conversation · K. GDPR erasure / takedown + moderation |
| ITrėmėjas | IT gig marketplace + AI website-audit lead magnet | L. two-sided marketplace engine · M. AI audit → findings → report generator · N. magic-link onboarding |
| Gidai | Tour-guide directory (guides × destinations × languages) | O. faceted people-directory + booking-intent search |
| Konsulta | Consultation booking + object storage | P. booking / scheduling + file storage |
| Volunteer suite · Fedmanager · Chatwoot · Inspections | Matching, membership CRM, omnichannel inbox, field capture | Q–T. matching + micro-LMS · membership CRM · omnichannel inbox · field data capture |
02Seven recombinations
Each merges 2+ primitives from different projects, targets a non-institutional audience, and monetizes without any agency, public sector, or procurement.
1 · Echorank — "How does AI describe your business?"
Merge: elmo's scheduled LLM-monitoring + citations (E·F·G) × ITrėmėjas's audit→report + magic-link (M·N) × Chat Cloud's CDN widget/badge (A).
Audience: SMBs, local & DTC brands, solo marketers. Pain: buyers now ask ChatGPT / Gemini / Perplexity "best X near me — is Y any good," and businesses are invisible or misdescribed there with zero visibility into it.
Product: a free public AI Visibility Scan → paid monitoring, fix-it recommendations, and an embeddable "AI-verified" badge. Money: freemium → subscription.
Non-obvious: a monitoring backend, a widget front-door, and an audit lead-magnet — built for three unrelated projects — snap into one product-led funnel. · Reuse ≈ 75% / build ≈ 25%
2 · Review-intake widget
AI-guided intake (J) + review engine (H) × widget (A): a drop-in that interviews a customer and emits a rich, verified review instead of a star form.
Reuse ≈ 70% · crowded vs Trustpilot / Google.
3 · White-label AI-audit for freelancers
The audit engine (M) × GEO (E) × widget (A): marketers embed a free AI-audit on their own site to generate leads; charged per seat.
Reuse ≈ 70% · narrow B2B, sales-led. The designated pivot.
4 · Chat-to-book local concierge
Guide directory (O) × widget + RAG (A·B) × booking (P): providers get an AI concierge; consumers search and chat-to-book.
Reuse ≈ 55% · two-sided cold-start, build-heavy.
5 · Employer-truth relocation lens
The 6,087-institution dataset (H) × AI answers (E): "what do real reviews and the AIs say about this employer / city" for movers.
Reuse ≈ 60% · niche, data is locale-bound.
6 · Anti-fake-review trust API
Moderation + GDPR (K) × AI intake (J) × review engine (H): a verification layer sold as an API to other marketplaces.
Reuse ≈ 65% · sales-led, not product-led.
7 · "AI Share-of-Voice Index"
GEO monitoring (E·F) across many brands → a public benchmark + data API, monetized by sponsorship.
Best used as the content engine for #1, not a standalone business.
03Scoring & the commitment
Weights (= 100): Demand 25 · Novelty 15 · Monetization 20 · Reuse 15 · Defensibility 15 · Scalability 10.
| Concept | Dem/25 | Nov/15 | Mon/20 | Reu/15 | Def/15 | Scl/10 | Total |
|---|---|---|---|---|---|---|---|
| 1 · Echorank (AI visibility) | 23 | 14 | 18 | 14 | 12 | 10 | 91 |
| 3 · White-label audit | 17 | 10 | 16 | 13 | 9 | 8 | 73 |
| 2 · Review-intake widget | 18 | 8 | 15 | 12 | 9 | 9 | 71 |
| 6 · Trust API | 15 | 12 | 13 | 12 | 12 | 7 | 71 |
| 7 · SoV Index | 14 | 13 | 10 | 13 | 11 | 8 | 69 |
| 5 · Employer-truth | 12 | 9 | 10 | 11 | 10 | 6 | 58 |
| 4 · Concierge | 13 | 9 | 11 | 8 | 8 | 7 | 56 |
Echorank — an AI-visibility (GEO / AEO) monitor for SMBs
Why it wins. The anxiety is net-new and already felt — owners hack it by hand every week; the category is inflecting in 2025–26. The motion is clean freemium → subscription with no relationship-selling. And the hard parts already run: the GEO engine executes prompts → runs → citations → competitors and renders reports; the chat stack ships a CDN widget and auto-spins demos; the marketplace already turns an audit into a scored, magic-link-gated report. This is glue, not green-field — English-first and category-universal, so it leaves the home market on day one. Each scan compounds into benchmark data no entrant has; each embedded badge is self-replicating distribution.
Why the runners-up lost. #3 and #6 are narrower and sales-led; #2 walks into Trustpilot's teeth; #7 is better as #1's fuel than as a business; #4 and #5 need a two-sided cold-start or a bigger, locale-locked dataset.
Evidence that would flip it. If a real free-scan → paid conversion runs below 2% after 500 scans, or LLM cost per scan can't be held under ~€1 at volume, abandon broad freemium and pivot to Concept #3 (white-label seats, higher ACV, far lower free-tier burn).
04The plan for Echorank
Positioning & ICP
Exact user: the owner or in-house marketer of an SMB / DTC / local brand (solo → ~200 staff) selling in a category people now research through AI assistants. Job-to-be-done: "show me — and help me fix — how ChatGPT, Gemini and Perplexity answer questions about my category and brand, before my competitor owns that answer." Wedge: a free public AI Visibility Scan — enter a brand + category, we fan ~20 buyer-intent prompts across models, return a shareable score, a competitor share-of-voice bar, the exact sentences the AIs say about you, and every wrong "fact."
MVP scope
Reuse as-is: the GEO run/citation/report engine + multi-tenant auth; the widget + CDN + demo-provisioning; the audit report renderer + magic-link. Glue: scan intake → category prompt-set generator → scoring model → report → Stripe paywall → re-run scheduler → email alerts. Genuinely new: the public scan landing, the 0–100 score + benchmark logic, billing, and multi-model fan-out. Smallest launchable unit: free scan live + one paid tier unlocking saved history + weekly re-scan + alert.
Pricing — assumption, anchored to replacing an hour a month of manual checking
Annual = 10× monthly. Blended ARPU assumed €45/mo.
Go-to-market — first 100 paying users, no agency network
- Product-led viralityEvery free scan ends in a shareable score card + an embeddable "AI-verified" badge — each embed is a backlink and a billboard.
- Dogfooded GEO contentPublish "how to appear in ChatGPT / Gemini answers for [category]" and make Echorank itself rank inside those AI answers — the product is its own proof.
- CommunitiesIndie Hackers, r/SEO, r/smallbusiness, r/marketing, marketing Discords/Slacks, build-in-public on X / LinkedIn.
- Link-bait PRA free public "AI Share-of-Voice Index" (Concept #7) ranking known brands by category — press-friendly and it seeds the benchmark data.
- Product HuntLaunch once paywall + alerts are solid (month 2–3).
Unit economics — assumptions labelled
0518-month cash-flow forecast
Revenue = end-of-month paying × €45 · COGS = paying × €6 · fees = 3% of revenue · owner draw starts month 7 · €3,000 startup lands month 1. All EUR. The paying-customer ramp is the one optimistic assumption — policed by the kill-criteria below.
| M | Paying | Revenue | COGS | Fees | Tools | Mktg | Draw | One-off | Net | Cumulative |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 0 | 0 | 0 | 80 | 200 | 0 | 3,000 | −3,280 | −3,280 |
| 2 | 5 | 225 | 30 | 7 | 80 | 150 | 0 | 0 | −42 | −3,322 |
| 3 | 12 | 540 | 72 | 16 | 80 | 200 | 0 | 0 | +172 | −3,150 |
| 4 | 22 | 990 | 132 | 30 | 80 | 300 | 0 | 0 | +448 | −2,702 |
| 5 | 35 | 1,575 | 210 | 47 | 80 | 400 | 0 | 0 | +838 | −1,864 |
| 6 | 55 | 2,475 | 330 | 74 | 80 | 600 | 0 | 0 | +1,391 | −473 |
| 7 | 80 | 3,600 | 480 | 108 | 150 | 800 | 1,000 | 0 | +1,062 | +589 |
| 8 | 110 | 4,950 | 660 | 149 | 150 | 1,000 | 1,500 | 0 | +1,491 | +2,080 |
| 9 | 150 | 6,750 | 900 | 203 | 150 | 1,200 | 2,000 | 0 | +2,297 | +4,377 |
| 10 | 200 | 9,000 | 1,200 | 270 | 150 | 1,500 | 2,500 | 0 | +3,380 | +7,757 |
| 11 | 260 | 11,700 | 1,560 | 351 | 150 | 1,800 | 3,000 | 0 | +4,839 | +12,596 |
| 12 | 330 | 14,850 | 1,980 | 446 | 150 | 2,200 | 3,500 | 0 | +6,574 | +19,170 |
| 13 | 410 | 18,450 | 2,460 | 554 | 300 | 2,600 | 4,000 | 0 | +8,536 | +27,706 |
| 14 | 500 | 22,500 | 3,000 | 675 | 300 | 3,000 | 5,000 | 0 | +10,525 | +38,231 |
| 15 | 610 | 27,450 | 3,660 | 824 | 300 | 3,500 | 6,000 | 0 | +13,166 | +51,397 |
| 16 | 730 | 32,850 | 4,380 | 986 | 300 | 4,000 | 7,000 | 0 | +16,184 | +67,581 |
| 17 | 870 | 39,150 | 5,220 | 1,175 | 300 | 4,500 | 8,000 | 0 | +19,955 | +87,536 |
| 18 | 1,030 | 46,350 | 6,180 | 1,391 | 300 | 5,000 | 9,000 | 0 | +24,479 | +112,015 |
Cash-flow narrative. The business is cash-negative only in months 1–6, driven almost entirely by the €3,000 startup outlay. The trough is shallow (≈ €3.3k) and short; cumulative cash crosses zero in month 7 and self-finances thereafter. A textbook bootstrap: a shallow trough funded from pocket, then self-financing growth — no external raise, no debt.
06Risks & kill-criteria
| Risk | Failure mode | Mitigation | Kill signal → stop / pivot |
|---|---|---|---|
| Demand | Free scans don't convert | Tighten wedge, add "wrong-facts" urgency, retarget | < 2% free→paid after 500 scans → pivot to #3 |
| Technical / cost | LLM fan-out too expensive at scale | Batch, cache, cheaper models for scans | Cost/scan > €1 unrecoverable → gate free tier hard |
| Competitive | Incumbents (Profound, Peec, Otterly…) | Undercut on price; win via PLG + badge + benchmark data | Can't hold a price/PLG edge for two quarters |
| Key-person | Solo founder is the bottleneck | Keep infra automated end-to-end on the managed box | Ops can't run one week unattended |
| Retention | Novelty churn | Turn scans into a standing dashboard + weekly habit | Churn > 10%/mo sustained |
| Traction pace | Ramp far below model | Reforecast; low burn keeps runway long | < 15 paying by month 3 |