Where Bloom is today

What we've built

What Bloom has already, what the full vision requires, and how we sequence from here to product — with investment.

01

What Bloom already has

The hardest part. A fully-formed product vision, a design system, UX flows, curriculum framework, AI architecture, and the pedagogical reasoning behind every decision. All of it mapped before any build spend.

02

What the full vision requires

A team of 8–10 engineers, 18 months minimum, and significant funding. The AI architecture as designed is sophisticated. The knowledge graph alone is a multi-month engineering project. This is the version we're building toward — not the version we're starting with.

03

The sequencing insight

We don't need the full system to prove Bloom works. We need a version that creates the right feeling for a child and their family — with enough real personalisation to demonstrate the concept. That version is achievable with investment. Then we use it to raise and hire.

✓  What Bloom has mapped so far

  • User flows — child UI, parent hub, educator view, and onboarding journeys
  • Maths curriculum framework using CPA pedagogy — the first 40 KS2 concepts are still to be authored
  • AI architecture written up across six modules, with the child model schema
  • Tutor AI language rules — the persona and system prompt are still to be written
  • Safeguarding policy framework, DSL brief, and governance protocol
  • Full product story, investor narrative, and competitive landscape research
  • Brand, positioning, and sources and evidence base documented
  • Parent research plan — ten interviews underway, gating the core design phase

→  What investment unlocks

  • Technical co-founder — the engineering vision to match Bloom's product vision
  • The web application — React front-end, API layer, databases
  • Auth, data security, encryption, and GDPR compliance infrastructure
  • LLM integration and prompt routing in production
  • Safeguarding infrastructure — robust, auditable, live before any child uses the product
  • Child data storage that is genuinely secure and independently auditable
  • Bespoke illustration — feeling animals and the Bloom creature
  • The closed beta — 20 families, session data, and the evidence base for a seed raise
Where we are now: The product vision, user flows, AI architecture and business case are written up. The curriculum, tutor persona and prototype come next. We are seeking a technical co-founder and seed investment to turn the plan into a working product.

Section 1 — MVP

What Bloom v0.1 actually is

Not a simplified version of the full product. A carefully chosen slice that proves the core thesis: that an AI learning service can make a neurodivergent child feel genuinely seen, safe, and capable. Everything else follows from proving that.

MVP components
🎯 The core thesis
👦 Child experience
👩 Parent experience
🤖 AI in the MVP
🛡️ Safeguarding (non-negotiable)
✕ Explicitly not in MVP

The core thesis to prove

One child. One subject. One session. Does it feel different? Does the child feel safe? Does the parent feel informed? Does the AI respond as if it actually understands this specific child? If the answer to all four is yes — Bloom has something real to build on.

🎯 The single success criterion

  • A neurodivergent child completes a session without distress
  • They voluntarily return for a second session
  • Their parent says "this actually understands my child"
  • The child uses language of capability, not deficit
  • You have at least 20 families saying all of the above

📐 What the MVP proves to investors

  • Product-market fit signal in the hardest demographic to serve
  • That the experience design works before the AI is sophisticated
  • That parents will pay for something that genuinely helps
  • That your team (you + co-founder) can ship something real
  • That the unit economics work at even a small scale
One subject only at launch: Maths. Anxiety about it is common (about 1 in 3 students, PISA 2012), and it has the clearest learning objectives, the most measurable outcomes, and the widest applicability across profiles. Proving Bloom works for a child with dyscalculia-adjacent ADHD doing multiplication is a stronger signal than a broad but shallow offering across six subjects.

Child experience — MVP

The child experience must be complete, warm, and dignified from day one. There is no "we'll improve it later" for the child-facing product. If it feels like a beta, parents will not trust it with their vulnerable child.

✅ In MVP — child screens

  • Onboarding: name, avatar choice, 3 interest selections
  • Emotional check-in: illustrated animal scale, no words required
  • Today's plan — shown upfront, modifiable by child
  • Maths learning: CPA approach, visual-first, no time pressure
  • Conversational tutor: warm, consistent, patient AI learning service
  • Break tool: movement, breathing, sensory — child's choice
  • Session close: specific celebration of effort, not outcome
  • Growth journal: one entry per session, child-authored

🎨 Design non-negotiables

  • No timers visible anywhere in the product
  • No streaks, no leaderboards, no points that can be lost
  • No "wrong" — tutor AI hard-coded to never use this word
  • Max 2 elements competing for attention on any screen
  • Every transition smooth and predictable — no surprise changes
  • Font size adjustable, contrast mode available from day 1
  • Every screen keyboard-navigable — mouse not required
Simple but complete personalisation: In the MVP, personalisation is not ML-driven. It is preference-based — the child's stated interests appear in maths examples, their name is used by the tutor, their check-in state adjusts the opening activity. This is enough to feel meaningful. Real adaptive learning is Phase 2.

Parent experience — MVP

Parents are Bloom's true customer in the MVP. They pay. They decide. They will recommend Bloom to every parent in their network if it works — and the SEN parent network is exceptionally vocal and connected. Make the parent experience a product in itself.

✅ In MVP — parent screens

  • Onboarding: child profile, conditions, goals, communication style
  • Live session indicator: knowing child is in a session feels reassuring
  • Post-session summary: what happened, in plain warm language
  • Weekly digest: one celebration, one pattern, one action — auto-generated
  • Conversation starters: how to talk to your child about what they learned
  • Flag system: parent can flag "difficult morning" before a session
  • Direct message to Bloom team — real humans respond

💬 The parent promise

  • "You will understand your child's learning better than ever before"
  • "We will never make your child feel stupid or behind"
  • "If anything concerns us about your child's wellbeing, we will contact you directly"
  • "You can see everything. No black boxes."
  • "You can cancel anytime. We don't use dark patterns."

AI in the MVP — honest specification

The MVP AI is far simpler than the full architecture. That is the right decision. We build sophistication on top of real data, not on top of assumptions. Start with something that works well. Learn. Then build what the data tells us to build.

✅ AI in MVP (realistic)

  • Open-weight LLM, self-hosted on Bloom's UK/EU infrastructure, for the tutor AI
  • Carefully engineered system prompt with child profile injected
  • Interest-based personalisation through prompt construction
  • Rules-based emotional check-in response (not ML)
  • Rules-based content selection (not adaptive, yet)
  • Auto-generated weekly digest from structured session data
  • Keyword-based safeguarding flag system with human review

🚫 AI deferred to Phase 2+

  • Fine-tuning on Bloom data — base open-weight model first
  • LSTM emotion detection — rules work well enough first
  • IRT knowledge graph — simple mastery flags first
  • Real-time adaptation engine — session-level adaptation first
  • Forgetting curve scheduling — manual curriculum sequencing first
  • Longitudinal pattern analysis — not enough data yet
Data privacy: The tutor runs on a self-hosted open-weight model from the first beta session, so no child interaction data passes to a third-party AI. The trade-off is cost (around £1.8k a month in GPU hosting during the beta) and the need to test the model hard against the 50 child scenarios before any child uses it. The consent flow still explains exactly what is stored, where, and why.

Safeguarding — non-negotiable from day one

Safeguarding is not a Phase 2 feature. It is the price of admission to working with children. No child touches this product without it in place. This is both ethical and legal.

🛡️ MVP safeguarding (minimum)

  • Named Designated Safeguarding Lead before any child uses the product
  • Keyword-based detection across all child text input
  • Human review of every flagged interaction within 4 hours
  • Clear escalation pathway documented and tested
  • Parent contact protocol written and reviewed by a DSL
  • GDPR-compliant data handling — ICO registration required
  • Parental consent flow that actually explains data use

📋 Legal and compliance minimum

  • ICO registration and DPA 2018 compliance
  • COPPA and UK GDPR awareness — children's data needs specific protection
  • Safeguarding policy written, approved by qualified DSL
  • All team members with child access DBS-checked
  • Data retention policy — how long, why, deletion process
  • Privacy policy written in plain language for parents
No exceptions: Do not put a real child in front of this product until these are in place. A closed prototype test with consenting families still requires all of this. The safeguarding infrastructure is not optional at any stage.

Explicitly not in the MVP

Being clear about what we're not building is as important as what we are. Scope creep is how solo founders fail. These are real features that will be built — just not first.

⏳ Deferred to Phase 2

  • Educator view and school dashboard — parent-direct first
  • More than one subject — maths only at launch
  • Self-Understanding subject — needs specialist clinical review
  • EHCP report generation — clinical sign-off needed
  • Offline mode — requires complex PWA architecture
  • Voice input — adds significant complexity and cost
  • Multiple languages — English only at launch
  • Early Years companion (0–6) — content strategy in Phase 0, build in Phase 2

❌ Deferred to Phase 3+

  • Bloom fine-tuned model — trained on consented Phase 1–2 data
  • LSTM emotion model — needs 50k+ sessions of training data
  • IRT knowledge graph — needs session data to train on
  • Peer / async social features
  • NHS / statutory integrations
  • Wearable / biometric integrations
  • Custom content authoring tool for educators
Sequencing rule: Every deferred feature should have a clear trigger for when it gets built — not a date, but a milestone. "Educator view ships when we have 50 paying families who've requested it" is better than "educator view ships in Q3."

Section 2 — Build Phases

How you get from here to there

Four phases. Each one has a clear output, a clear milestone, and a clear decision point. You never move to the next phase until the current one is done — and done means real children using it, not "it's mostly ready."

Phase 0 — Now · 8–12 weeks · Solo

Foundation & Co-Founder Search

Everything that doesn't require code. Build the product soul before the product itself. This phase ends when we have a technical co-founder committed — or strong enough prototype evidence to raise a pre-seed to hire one.

High-fidelity Figma prototype Maths curriculum — 40 concepts authored Tutor AI persona + prompt system Parent journey fully mapped Safeguarding policy drafted 10 parent interviews completed DSL advisor identified Pitch deck complete Technical co-founder search active
Phase 1 — Months 3–7 · You + Technical Co-Founder

Closed Beta — 20 Families

Build the real product with real children. Closed, invited, consenting families only. No public launch. Every session observed or reviewed. Weekly iterations. The goal is not perfection — it is learning fast enough to know what to build next.

Working web app (React + backend) Child UI built to spec Parent hub live Tutor AI integrated Safeguarding infrastructure live 20 families onboarded Weekly parent interviews Session data analysis Pre-seed raise (if needed)
Phase 2 — Months 8–14 · Small team

Private Launch — 200 Families + Seed Raise

Expand carefully. Add the educator view. Add a second subject. Begin building real adaptive capability using the session data from Phase 1. Raise a seed round on the back of Phase 1 evidence.

Educator view + school dashboard Early Years companion — parent hub for ages 0–6 Second subject (Literacy) Rules-based adaptation engine EHCP report generation (SENCO-reviewed) Offline mode (PWA) 200 paying families Seed round: £500k–£1.5m First ML engineer hired
Phase 3 — Months 15–24 · Growing team

Public Launch + Series A

The full product vision starts to become real. Self-hosted models. LSTM emotion detection trained on real session data. IRT knowledge graph. First school partnerships. Series A raise to build the team properly.

Full subject suite including Self-Understanding Bloom fine-tuned LLM (self-hosted) LSTM emotion model trained on Phase 1–2 data IRT knowledge graph live First 5 school partnerships 2,000 paying families Series A: £3–5m
Phase 4 — Months 24+ · US Expansion

United States — the right next market

Built on UK clinical evidence, a trusted brand, and a validated playbook. The US requires curriculum localisation and IEP reporting — bounded, planned work. The AI architecture, pedagogy, and experience design transfer entirely. IDEA's strong enforcement and the scale of the US SEN market make this a compelling next step.

State curriculum standards alignment IEP report format output COPPA compliance review US school district licensing model Federal and state funding routes (IDEA) US research partnerships (Stanford, MIT) Series B to fund US go-to-market
⟐
The key sequencing rule
Each phase gates the next. We cannot raise seed money without Phase 1 evidence. We cannot raise Series A without Phase 2 evidence. The evidence is not slides — it is paying families, session data, and outcome signals.
Phase 0 → Phase 1 gate: Technical co-founder committed Phase 1 → Phase 2 gate: 20 families, NPS >70, retention >80% week 4 Phase 2 → Phase 3 gate: 200 families, seed raised, ML hire made

Section 3 — Business Case

Why this is a real business

Not a charity. Not a side project. A defensible, scalable commercial product with genuine competitive moats — built on top of something that actually helps children. The two are not in conflict.

1 in 5
pupils in England have special educational needs
1.86m pupils (DfE, 2025/26)
£8,500
a year families of children with complex needs spend on support from their own pocket
Sense, 2026 · Bloom at £168/year
35%
of parents of children with complex needs have left work
Sense / Censuswide 2026 — the demand is acute
718k
EHC plans in England — up 12.5% in a year
Driven by rising diagnosis rates + EHCP demand

🏰 The competitive moats

  • Child model becomes more accurate with every session — harder to replicate over time
  • Curriculum specifically designed for neurodivergent profiles — not retrofitted
  • Parent trust is earned slowly and compounds — high switching cost once established
  • Safeguarding infrastructure is a genuine barrier to entry for competitors
  • Self-Understanding subject is unique — no competitor has anything like it
  • Network effects: SEN parent communities are tight and vocal recommenders

⚠️ The honest risks

  • Google, Khan Academy, or a well-funded EdTech could pivot here — but won't easily (see moats)
  • Safeguarding failure would be existential — one serious incident ends the company
  • Clinical credibility takes time — parents are (rightly) sceptical of EdTech claims
  • EHCP cuts or SEND policy shifts could reduce the addressable market temporarily
  • LLM API costs could compress margins at scale before migration to self-hosted
  • Solo founding is hard — finding the right technical co-founder is the critical dependency

Section 4 — Market

Who pays, and why they will

Three distinct customer segments, each with different willingness to pay, different buying journeys, and different value propositions. The MVP focuses entirely on one.

A unique market position — the whole family journey: Bloom is designed to serve neurodivergent families from first concern through to age 16. The Early Years companion (0–6) serves parents before their child reaches school age — building trust, providing guidance, and creating a powerful acquisition funnel. When that child turns 6, Bloom is already their family's most trusted tool. Few competitors offer this continuity.
Segment 1 — Direct to parent · MVP focus · highest willingness to pay
👨‍👩‍👦
Who they are
Parents of neurodivergent children aged 6–14. Already spending on tutoring, assessment, OT, therapy. Used to paying for specialist support. Desperately underserved by mainstream EdTech.
primary target
💷
Willingness to pay
High. Families of children with complex needs already spend an average of £8,500 a year on support (Sense, 2026). Bloom at £14/month is not a stretch — it's a bargain if it works. Premium tier at £29/month also viable.
£14–29/month
📣
How we reach them
SEN parent Facebook groups (enormous, highly engaged). ADHD/autism charity partnerships. Word of mouth from parent communities — NPS-driven growth. GP/CAMHS waiting list families.
The SEN parent community is one of the most connected word-of-mouth networks that exists. A recommendation from another parent carries enormous weight.
💚
Why they stay
Their child genuinely enjoys sessions. They see progress. They feel informed. The weekly digest gives them language to advocate for their child at school. Emotional switching cost is very high.
target churn: <5%/month
Segment 2 — Schools & MATs · Phase 2 · B2B, longer sales cycle
🏫
Who they are
Primary and secondary SENCOs, SEN leads, and multi-academy trusts. Under enormous pressure to support rising SEND pupil numbers with flat or declining budgets.
💷
Willingness to pay
Moderate but real. Schools can fund it from SEN budgets, and from pupil premium where it serves eligible pupils. A per-pupil price of £180–240/year is well within existing specialist support spend.
£180–240/pupil/year
📋
What they need from Bloom
EHCP-aligned reporting. Evidence of impact for Ofsted. SENCO morning briefing. Data that reduces their administrative burden. Something they can recommend to parents with confidence.
⏱️
Sales cycle reality
6–18 months from first contact to school-wide licence. Procurement processes are slow. Start with individual SENCO champions who bring Bloom in as a recommendation to parents — they become advocates before they become buyers.
Segment 3 — NHS / Statutory · Phase 3+ · long game, high value
🏥
The opportunity
Waits are long: over 16 months on average for an autism assessment, and 223 days for CAMHS when autism is suspected. Bloom expects ICBs to look for evidenced digital support. A clinically-evidenced Bloom could become a commissioned service.
long-term, high-value
📊
What it requires
Randomised controlled trial evidence. NICE-compliant clinical evidence base. DSP Toolkit compliance. A clinical advisory board with credible NHS names. This is a 3–5 year path from now.
💷
What it's worth
Bloom's own estimate is ICB commissioning at £500–800 per child once the evidence base exists. No published figure for NHS framework contract values has been found.
transformational at scale
🌱
How to prepare now
Design the outcome measurement framework from the start. Capture the data that will become our clinical evidence base. Every session today is building the RCT dataset of the future.
Start conversations with academic partners (UCL, Birmingham, Edinburgh all have strong EdTech research groups) in Phase 2.

Market Context

UK first. Global by design.

Bloom launches in the UK. Not because the UK is the only market — but because it is the right market to prove the model, build the evidence base, and establish the trust that opens every other market.

🇬🇧 Why UK first

  • The EHCP system and SEND Code of Practice create a regulatory environment that rewards rigorous, evidenced support — exactly what Bloom provides
  • Ofsted's SEN accountability framework means schools actively need demonstrable impact data — Bloom's reporting suite is directly valuable
  • The UK SEN parent community is one of the most organised and vocal in the world — word-of-mouth growth is exceptionally powerful here
  • GDPR and ICO compliance are well-understood in the UK context and map cleanly onto Bloom's data architecture
  • Early UK traction creates a replicable playbook, a clinical evidence base, and a trusted brand — all of which travel internationally

🇺🇸 Why the US is the right Phase 3 target

  • IDEA (Individuals with Disabilities Education Act) gives children with disabilities a federal legal right to appropriate education — stronger enforcement than much of UK provision in practice
  • World-leading neurodivergent education research comes disproportionately from US institutions — Stanford, Harvard, and MIT all have strong learning differences programmes
  • North America is the largest regional EdTech market (IMARC), and Bloom believes SEN provision there is underserved at the quality Bloom offers
  • The AI architecture, pedagogical frameworks, and experience design transfer entirely — US expansion is primarily curriculum localisation and IEP-format reporting
  • US special education law creates strong institutional demand from school districts — a B2B opportunity significantly larger than UK MAT licensing
US expansion — what needs to be built and when
📐
Curriculum localisation
Align subject content to each target state's standards (many derived from Common Core). The pedagogical approach — CPA, mastery-based, emotion-first — transfers entirely. The content nodes need localising.
Phase 3 · bounded scope
📋
IEP reporting format
US schools use Individual Education Programs (IEPs) rather than EHCPs. The reporting engine needs an IEP-format output mode. Same underlying data — different structured output template.
Phase 3 · template work
⚖️
COPPA compliance
The Children's Online Privacy Protection Act has specific requirements for products used by under-13s. Bloom's privacy architecture is already strong — COPPA compliance is an extension, not a rebuild.
Phase 3 · legal review
🏫
District licensing model
US schools buy through district procurement rather than individual school decisions. The sales model needs adapting — longer cycles, different decision-makers, federal IDEA funding as the special-education budget route.
Phase 3 · go-to-market

Section 5 — Revenue Model

The numbers that matter

Three revenue streams, unit economics that work, and a path to profitability that doesn't require becoming a venture-scale machine to survive.

👨‍👩‍👦

Direct-to-Parent Subscription

B2C · monthly recurring · low friction
  • Free tier: 3 sessions/month, one subject, no parent hub
  • Bloom Family: £14/month — full access, parent hub, weekly digest
  • Bloom Plus: £29/month — EHCP report generation, priority support
  • Annual discount: 2 months free (increases LTV, reduces churn)
  • Target: 2,000 paying families by month 18 = £28k MRR
At 5,000 paying families at blended £18/month = £90k MRR / £1.08m ARR — enough to be a real business.
🏫

School Licence

B2B · annual · per-pupil pricing
  • Per-pupil licence: £180/year (min 10 pupils)
  • Includes educator dashboard, EHCP reporting, SENCO briefing
  • MAT licence: volume pricing for multi-academy trusts
  • Funded from SEN budgets, or pupil premium where it serves eligible pupils
  • 10 schools × 30 pupils = £54k ARR from B2B alone
B2B has higher ACV but slower sales cycles. Don't depend on it for survival — treat it as upside in Phase 2.

📊 Unit economics (target)

  • CAC (parent): <£30 via community + word of mouth
  • LTV (Family plan, 18mo avg): £252
  • LTV:CAC ratio: 8.4× — healthy for B2C SaaS
  • Gross margin target: 70%+ (AI hosting costs are the key variable)
  • Payback period: <3 months at target CAC

⚠️ Margin watch: self-hosted AI costs

  • Self-hosted GPU: around £1.8k/month once the beta is live (proposed budget)
  • A largely fixed cost — high per child at 20 families, a deliberate trust cost
  • Spreads across families as Bloom grows; more GPU capacity added with scale
  • Smaller fine-tuned models in Phase 3 bring cost per session down
  • No child data to a third-party AI at any phase

Section 6 — Technical Co-Founder

Finding the right person

Not just a developer. A true co-founder — someone who owns the technical vision the way you own the product and design vision. The wrong hire here is worse than no hire. Take the time to find the right person.

The honest pitch to them: "I've done the work that most technical founders never do — the product vision is mapped, the AI architecture is written up, the business case is built. We need someone who can turn that plan into a working system. You get equity in something with real vision and real first-mover advantage in a market that desperately needs this."
🧑‍💻
The ideal technical co-founder
Full-stack · AI/ML exposure · mission-aligned

Someone who has shipped a production web application before. Ideally with experience in consumer products (not just internal tools), some exposure to AI/LLM integration, and a personal connection to the mission — a neurodivergent family member, a background in education, or a deep belief in equitable access to good learning.

React / Next.js Node or Python backend PostgreSQL LLM API integration Data privacy awareness Has shipped a product
Non-negotiables
  • Has built and shipped something real — not just side projects
  • Understands why data privacy in children's products matters
  • Can hold a technical opinion and defend it to you
  • Aligned on mission — not just looking for equity and exit
  • Wants to be a co-founder, not a contractor with equity
⚠️
Who to avoid
Red flags and misaligned profiles

The wrong technical co-founder will slow you down, create technical debt that takes years to unwind, or misalign your incentives at the worst moment. Better to take longer to find the right person than to move fast with the wrong one.

Red flags
  • Wants to rebuild your design system from scratch before shipping anything
  • Doesn't ask questions about the children who will use this
  • Dismisses the safeguarding requirements as "we can add that later"
  • Only interested in the AI/ML parts — not the full product
  • Has never shipped a consumer product to real users
  • Wants a salary from day one without meaningful equity commitment
  • Treats your design decisions as obstacles rather than constraints

Where to find them

Channels — in rough order of likelihood to work
🤝
Warm network first
Former colleagues, design directors you've worked with who went technical, developers at agencies you've collaborated with. Tell everyone what you're building — the mission attracts the right people.
The SEN parent community is worth activating here. Parents of neurodivergent children who happen to be engineers will apply with extraordinary motivation.
🎓
Founder networks
Entrepreneur First and ODF (formerly On Deck) match co-founders; Zinc, Nesta Mission Studio and Founders Factory build ventures around founding teams.
Entrepreneur First — strongest match
🌐
EdTech and SEN communities
BESA (British Educational Suppliers Association), Nesta EdTech events, Ufi VocTech Trust (grants and investment), nasen (National Association for SEN). Engineers in these communities already care about the mission.
📝
Public writing
Write about Bloom on LinkedIn and Substack. Share the design thinking. Show the documents. The right technical co-founder will find you if they can see what you're building and believe in it.
This also builds the parent waitlist simultaneously. One stone, two birds.

The equity conversation

⚖️ Equity split principles

  • Don't give less than 30–40% to a true technical co-founder — it signals you don't think they're a co-founder
  • 4-year vest, 1-year cliff — standard, fair, protects both parties
  • Equal split (50/50) is defensible if they join before any revenue and build the whole system
  • Defer the exact split until after 4–6 weeks of working together — you'll know more about relative contribution
  • Agreeing roles clearly is more important than the exact percentage

🗣️ The pitch narrative

  • "The product is mapped in detail — you're not starting from a blank page"
  • "Demand is rising fast — EHC plans are up 12.5% in a year, a structural tailwind"
  • "The mission is real — this helps children who are genuinely underserved"
  • "The design-led founder is rare in EdTech — most are technical-led with poor UX"
  • "Phase 0 can be completed in 3 months — you see a working product very fast"

Section 7 — Action Plan

The next 90 days — specifically

Not a vague roadmap. A concrete list of things to do, in order, that builds momentum and ends with a technical co-founder committed or a pre-seed round in progress. Most of this is work Bloom can progress now, before writing a line of code.

Weeks 1–2 — The foundation

Get the basics in order

The unglamorous but necessary work that lets everything else happen.

Register a company (Companies House — online, £100, usually within 24 hours) Register with ICO for data protection (required before any data collection) Secure bloom.co or similar domain Set up a simple waitlist landing page Open a business bank account Identify 3 SEN charities to build relationships with
Weeks 2–5 — Research and prototype

Talk to 10 parents before building anything

Your assumptions about what SEN parents need will be partially wrong. Find out which parts before committing them to a design system.

Recruit 10 parents via SEN Facebook groups 30-minute interviews: current struggles, tools they use, what's missing Watch 3 children attempt to use existing EdTech (with consent) Synthesise findings — what changes, what gets confirmed Build high-fidelity Figma prototype of child onboarding + first session Test prototype with 3 children (guerrilla testing)
Weeks 4–7 — Content and AI

Build the curriculum and the tutor persona

Work that can be done now, with Claude, that will be handed directly to your technical co-founder as a complete specification.

Author 40 maths concepts (KS2 focus) with CPA approach Write the tutor AI system prompt — full persona specification Test tutor persona against 50 child scenarios using Claude Write the emotional check-in flow — all states, all responses Write parent weekly digest templates — 8 variants Draft safeguarding policy (with DSL advisor review)
Weeks 6–10 — Business case and pitch

Build the narrative that attracts a co-founder and early investors

The pitch deck is also your co-founder recruitment document. Build it to do both jobs.

Pitch deck — 12 slides, investor-ready Financial model — 3-year projections, unit economics One-pager for co-founder recruitment Apply to Entrepreneur First or Zinc VC Begin LinkedIn / Substack writing about the problem and vision Identify 5 SEN-specialist angels to approach for pre-seed
Weeks 8–12 — Co-founder search active

Find and commit to the right technical co-founder

This is the most important thing you do in Phase 0. Do not rush it. Do not settle. But also do not let perfect be the enemy of good — a committed, capable, mission-aligned engineer is what you need.

Post co-founder brief publicly (LinkedIn, Workspaces, CoFoundersLab) Work trial: 4-week paid project before committing equity Technical interview: can they spec a simple version of the child model? Values interview: what do they think about children's data privacy? Reference check: has anyone used this product they built? Term sheet signed: equity, vesting, roles, decision rights
⟐
End of 90 days — where you should be
Not "almost ready to build" — actually building, with the right person, on a solid foundation.
Technical co-founder committed with signed term sheet OR pre-seed funding agreed to hire one 10+ parent interviews completed and synthesised Figma prototype tested with real children 40 maths concepts authored and structured Tutor AI persona fully specified and tested 100+ waitlist families signed up
The single most important thing: Start talking to SEN parents this week. Not to sell them anything. To understand them. Every conversation before building a single screen makes the product better and the story more credible. The parents we talk to become our first 20 beta families. The problem we articulate from their words becomes our pitch. Start there.