Downshift · Pre-Seed · 2026

THE PRIMER

A devoted tutor for every child on Earth.
Starting where there are none.

An adaptive AI tutor that teaches children to read, write, and do arithmetic. Fully offline, on the phone their family already owns.

The problem

The children the system already lost

71%
of 10-year-olds in Latin America cannot read a simple text
World Bank learning-poverty measure, post-COVID
13%
of rural Peruvian 2nd graders read at a satisfactory level. Urban: 37.8%
INEI / ECE national assessment
88%
of Peru's bilingual-education teachers lack completed teaching credentials
DEIB. In remote communities, one teacher covers every grade.

In the Andes and the Amazon, the teacher exists on paper. The child is on her own.

Why now

Three curves just crossed

01 · It works without teachers

The $15M Global Learning XPRIZE put offline tablets in front of 2,700 unschooled Tanzanian children. In 15 months, non-readers were cut in half. No teacher present.

02 · Content is now nearly free

The winners hand-authored 20,000+ activities over years. A frontier model now writes an entire scaffolded curriculum in batch. The expensive part of 2019 costs almost nothing in 2026.

03 · The device is already there

Android holds 90 to 96% of every target market. No hardware to ship, no lab to build, no OLPC. The classroom is already in the family's pocket.

Proof the model works. Content at zero marginal cost. Hardware already deployed. Nobody has combined the three.

The product

A tutor, not an app. Offline, not connected.

  • 150MBOne download, then zero connectivity required. Forever.
  • VoiceVoice-first. A pre-literate child cannot type or read menus.
  • HerA character with a name who remembers what she struggled with last week.
  • YearsA learner model per child: what she knows, what comes next, proof for the parent.
Layered fidelity

Text + drawings · the complete tutor, works anywhere
+ Songs · when a connection passes by
+ Video · when there is real bandwidth
+ Live dialogue · when the village gets its antenna


Same tutor, same curriculum, same child. The product meets the household where it is, and gets richer as connectivity arrives.

How it works

Engineered so it cannot lie to a child

Deterministic brain

A mastery engine decides what to teach next. Interpretable, sub-100ms, offline. No hallucination surface.

Precomputed lessons

Content is authored in the cloud, reviewed by humans, and shipped as signed packs. The device plays; it never improvises facts.

On-device speech

Recognition and a warm voice run locally. The child speaks, draws, taps, and traces. No data leaves the phone.

Spreads phone to phone

Signed packs pass by Bluetooth, Wi-Fi Direct, or SD card. One connected phone bootstraps an entire village.

The generative AI does the authoring, once, where we can review it. The child gets a system that is correct by construction.

Unit economics

A lesson is 10 kilobytes, not 9 megabytes

13MB
holds 1,000 lessons plus the full icon library
Lessons ship as drawn compositions, not video files. ~900x smaller.
$0.18
all-in cost per learner per month at 1M learners
$0.12 at 10M. Marginal cost approaches zero after distribution.
$48
per student for the World Bank's cloud AI tutoring pilot in Nigeria
Development funders buy on cost per learning outcome. We sit alone on that chart.

Every connected competitor pays per token, per child, forever. We pay once, at authoring time.

Market

Peru first. Then all the way up.

  • 1Rural Peru. Quechua + Spanish, Andes and Amazon. The sharpest need with a reachable payer.
  • 2The Andes. Ecuador, Bolivia. Same languages, same rural profile.
  • 3Colombia + Central America. Worst literacy, strongest remittances.
  • 4Mexico → the US border. Where the diaspora funds the whole corridor.
One spine, nineteen countries

One Spanish curriculum serves ~19 countries. Indigenous packs (Quechua, Aymara, Guaraní, Maya) layer per region. Visuals are language-neutral; narration is a text swap.


Latin America invented teaching remote children without teachers: Radio Sutatenza reached 900+ municipalities by radio and workbooks. We are its smartphone-era successor.

Business model

Two engines. Neither depends on the other.

$1/month · the family

An adoption price, deliberately at the floor. Low enough to never gate a child, real enough to signal value.


$15-30/month · the diaspora. A relative abroad sponsors a child back home. Remittances to LMICs reached $656B a year. The monthly progress report is the receipt that makes a subscription feel like school fees.

Funded partnerships

Mining and energy companies fund rural schools near operations as social license. Antamina alone put S/14M into two rural school programs and has run one with Enseña Perú since 2017.


One CSR deal funds thousands of children plus the school's satellite antenna for sync. Partnerships buy the beachhead; the subscription scales north.

Competition

The field is empty. We checked in Spanish.

onebillionXPRIZE winner, 3M+ children. Hand-authored courseware. No tutor, no LLM, no funding since the prize era.
KolibriOffline platform in 220+ countries. Institutional installs. A library, not a tutor.
Read AlongGoogle's reading feature, 30M children. A feature in a portfolio, not a relationship.
KhanmigoCloud, urban, affluent. Its cost structure cannot follow us down-market.
What nobody has

The tutor. Everyone built content or platforms. Nobody built someone the child returns to.

We searched for a fully offline, permissionless, parent-downloaded child tutor in Spanish and Portuguese and demonstrated its absence. The niche is open, and it is contested only by companies structurally unable to enter it.

Defensibility

The moat is in the layers that don't commoditize

Evidence

Our bar: delayed, unassisted, independently administered post-tests. The whole category fails it. Proven efficacy is the one asset money can't copy quickly.

The character

Songs and characters are the only free acquisition ever proven at scale in these markets. Ubongo reaches 11M viewers a week on that engine.

The learner model

A per-child record of mastery, compounding for years. It powers the tutor, the parent report, and the sponsor receipt.

Reading assessment

Scoring a child reading aloud, on-device, in Spanish and Quechua. No open solution exists. We build it where the speech data actually exists.

We do not compete on running models offline. Google gives that away. We compete on the tutor, the evidence, and the character.

Why us

A character studio that knows pedagogy

  • 9consumer AI products shipped by a tiny team: video, music, voice, education.
  • 85the numeric ship gate on our pedagogy eval harness. Adversarially verified, citation-backed. Almost nobody in edtech measures teaching quality at all.
  • per minute of generated, narrated, animated lesson content. The cost position that makes localization in 20+ languages possible.
  • K-8COPPA-grade child accounts, parent consent, and curriculum standards already shipped in production.
The unfair part

Everything the Primer needs, we already operate: explainer generation, music-based learning, voice cloning, an eval culture that refuses unmeasured claims, and years of practice making software feel like someone rather than something.

The plan

Arithmetic first. Reading where the data lives.

Days 1-30

Arithmetic tutor MVP in Spanish + Quechua, running offline on a $70 Android. Learner model live. Diaspora price test running before code is even finished.

Days 31-90

First funded deployment conversation: Enseña Perú and a mining CSR program. Pilot design for 2,000 children. Reading assessment prototyped on existing Spanish child-speech corpora.

Months 4-18

Pilot in the field. Independent efficacy study designed to the bar the category fails. Second country on the same spine. The evidence becomes the moat.

Arithmetic needs no speech recognition, so it ships first. Reading follows where the corpora already exist. Every risk has a sequenced answer.

The ask

$1.5M pre-seed

18
months of runway
Prototype → funded pilot → efficacy study design
2,000
children in the first pilot
Rural Peru, with an implementing partner and a CSR funder
3
hires
Learning engineer, Peru operations lead, child-speech data collection

In range for the round: LatAm AI-education pre-seeds cleared at $1.4-1.6M this cycle. Nobody in the cohort is rural, offline, or evidence-gated. That position is open, and it is ours to take.

The Primer

Every child gets a tutor.

The phone is already in their pocket.

Manuel Zamora · Downshift
Rural Peru → all of Latin America → every child on Earth