AI-native startups for the world big AI hasn't reached.
Capital is pouring into AI, and the cost of intelligence is collapsing. Yet the people with the most to gain — in other languages, informal economies and overstretched public services — are the least served. Each Plankito blueprint is a researched, costed plan to build for them, with a moat the big labs can't copy.
Find the one for you↓64%
of all venture funding worldwide went to AI startups in Q3 2026 — $102B of $159B.
Crunchbase News, 2026 ↗
280×
cheaper: the fall in the cost of running a GPT-3.5-level model in under two years.
Stanford HAI, 2025 ↗
16% vs 29%
of working-age people use generative AI in the Global South versus the Global North — and the gap is widening.
Microsoft, 2026 ↗
Featured blueprint
Education · Voice AI
Pathshala AI
A patient, voice-first tutor in every student's own language.
An AI tutor that explains the national curriculum in spoken Bangla, grades handwritten work from a phone photo, and adapts to each student's weak topics. Priced for a family budget, not a coaching-centre budget — and built to travel to any exam system in any language.
Read the full blueprint →
- MVP
- 6 weeks
- Cash to MVP (est.)
- $3,400
- Margin after AI (est.)
- ~70%
- Editorial score
- 4.3 / 5
- AI leverage
- 5/5
- Moat durability
- 4/5
- Competition gap
- 3/5
- Capital efficiency
- 4/5
- Timing
- 5/5
- Scalability
- 5/5
Find your fit
Which one could you start?
Four honest answers about your cash, team, buyer and timeline. We show what fits, and what is one change away.
6 blueprints fit
- Khata Agent$2,280 · 6 wks
Owners of small shops and the wholesalers who supply them.
- Pathshala AI$3,400 · 6 wks
School students preparing for SSC and HSC exams, and their parents.
- Bhasha Voice$6,370 · 10 wks
People calling banks, telcos, hospitals and public helplines.
- Krishi Lens$4,580 · 8 wks
Smallholder farmers. It is free for them.
- Sheba Scribe$7,550 · 12 wks
Community health workers and their supervisors.
- SkillPass$3,370 · 8 wks
Workers going abroad, and the training centres that prepare them.
The index
6 blueprints, compared on the same terms. Search, sort, or filter by the kind of bet you want to make.
- 01
Pathshala AI
A patient, voice-first tutor in every student's own language.
Education · Voice AI
70%of 10-year-olds in low- and middle-income countries cannot read and understand a simple text.
- MVP
- 6 wks
- Cash to MVP
- $3,400
- Score
- 4.3
- 02
Khata Agent
An AI bookkeeper that lives in WhatsApp and speaks like the shopkeeper does.
SME finance · AI agents
90%of businesses worldwide are SMEs, and they provide more than half of all employment.
- MVP
- 6 wks
- Cash to MVP
- $2,280
- Score
- 4.5
- 03
Bhasha Voice
Voice agents that answer the phone in the languages big AI forgot.
Voice AI · Infrastructure
95%+of the world's linguistic diversity is made up of low-resource languages, which mainstream language models underserve.
- MVP
- 10 wks
- Cash to MVP
- $6,370
- Score
- 4.3
- 04
Krishi Lens
Point a phone at a sick crop. Get a diagnosis, a treatment and a price — in your language.
Agriculture · Vision AI
⅓of the world's food is produced by small family farms of under two hectares.
- MVP
- 8 wks
- Cash to MVP
- $4,580
- Score
- 4.3
- 05
Sheba Scribe
An AI copilot that lets one community health worker do the work of three.
Health · AI agents
5.85Mhealth workers short in WHO's African Region by 2030 — work that copilots must help existing staff absorb.
- MVP
- 12 wks
- Cash to MVP
- $7,550
- Score
- 4.2
- 06
SkillPass
An AI-verified skills passport for the world's migrant workers.
Workforce · Vision AI
167.7Minternational migrants work in their destination countries' labour forces — 4.7% of the global workforce.
- MVP
- 8 wks
- Cash to MVP
- $3,370
- Score
- 4.2
Cash to MVP is our estimate of setup plus running costs until launch, excluding salaries. Score is Plankito's editorial judgement, not a forecast.
The filter
Three questions every idea must survive.
- 1
AI leverage
What can AI do here today that was impractical three years ago? If the answer is “a chatbot”, the idea is cut.
- 2
Lab-proof moat
Why won't the next model release or big-tech feature absorb this business? We look for local language, workflow depth, distribution and proprietary data.
- 3
Inference economics
What does each unit cost in model and speech fees, what does it sell for, and does the margin survive scale?
The numbers most AI pitches leave out.
Most AI products fail on value, not technology. Every blueprint is written to avoid becoming one of these.
95%
of enterprise generative AI pilots show no measurable impact on profit and loss.
Fortune (reporting MIT NANDA), 2025 ↗
40%+
of agentic AI projects are forecast to be cancelled by the end of 2027.
Gartner, 2025 ↗
~25%
average gross margin among the fastest-growing AI startups — compute costs pass straight through.
Bessemer Venture Partners, 2025 ↗
Our standards
Inside every blueprint, in this order:
- 01At a glance
- 02Opportunity & evidence
- 03The AI case
- 04Market & expansion
- 05Competition & edge
- 06Business model
- 07Stack & data
- 08Costing
- 09MVP & roadmap
- 10Start this week
- 11Sources
Cited
Every figure links to its source. Estimates are labelled as estimates. Every page shows when its facts were last checked.
Honest about AI
We publish the failure rates next to the hype, and every blueprint shows its margin after model costs.
Buildable
Real stacks, real tables, real costs. Hand any blueprint to an engineer or an investor and they know what happens on Monday.
Fear of starting is usually a missing plan. Pick a blueprint, and do its first three steps this week.