Pre-seed

Le modèle d'IA de dépannage HVAC que personne d'autre ne peut entraîner

Copilote IA pour techniciens terrain. L'usage entraîne le modèle — le modèle creuse l'écart.

Last updated: 22/06/2026

At a glance

Dossier investisseurs — AskMarcel

Copilote IA de dépannage HVAC. Levée pre-seed.

Last updated: 22/06/2026

ItemDetail
What we're buildingThe HVAC troubleshooting AI model that neither ChatGPT nor a manufacturer can replicate — no corpus
The wedgeA mobile app for tradespeople and technicians — usage trains the model
The businessProprietary model: service seats, then API/MCP and manufacturer SAV deflection
Amount sought€50,000, single ticket, unlocked against milestones
Runway~12 months, lean structure (solo founder + occasional freelance)
Pricing€19/month (mobile app) · €15/seat for teams ≥5 (service companies)

Traction

Actifs déjà en place

Chiffres au 22/06/2026 — corpus, produit et canaux d'acquisition.

Fault → resolution pairs

0k

Built from public web sources

Manufacturer technical docs

0k+

Integrated in database

Languages in production

0

Multilingual application

API / MCP

Test

Functional, in testing phase

Programmatic SEO articles

0

+150% organic traffic

FGAS/RGE artisan contacts

0k

Qualified emailing list

Freemium users

0

Feed the database and calibrate the model

Corpus multi-marques

Daikin
Mitsubishi
Atlantic
Viessmann
Samsung
Hitachi
Daikin
Mitsubishi
Atlantic
Viessmann
Samsung
Hitachi
Daikin
Mitsubishi
Atlantic
Viessmann
Samsung
Hitachi
Daikin
Mitsubishi
Atlantic
Viessmann
Samsung
Hitachi
Chapitre 01

Vision & founding thesis

Every HVAC service call produces structured diagnostic data — fault, brand, model, resolution. No one else owns it. AskMarcel builds the troubleshooting AI model that only this corpus can train.

1.1

The pain — lived first, then quantified

I lived it before I measured it: 15 years in HVAC engineering — field and distribution — where technical information always exists but is never accessible at the right moment, on a job site, with the customer watching.

And this is not a niche problem. The French HVAC sector represents €7–8.8B in revenue across ~45,000 companies (~78% with fewer than 10 employees). Demand outstrips capacity: ~15,000 unfilled positions per year, experienced profiles retiring faster than knowledge transfers, and REPowerEU targeting 10M heat pumps in Europe by 2027 — with an estimated shortfall of 50,000–80,000 trained technicians. Result: an increasingly electronic, multi-brand installed base serviced by increasingly inexperienced technicians. The gap widens every year.

1.2

Why generic AI is not enough

A technician who asks ChatGPT about a fault code gets a plausible answer, not a reliable one: no up-to-date manufacturer documentation, no standardized procedure, no knowledge of real faults on that specific model. A manufacturer hotline is reliable but single-brand, slow, and not scalable. A Facebook group is free and wrong half the time.

The real gap

The missing piece is not "an AI" — it is an AI trained on the right data. Multi-brand, field-sourced diagnostic data — no one owns it in structured form. That is exactly the gap we fill.

1.3

The thesis: data trains the model, the model is the moat

The tradesperson is the wedge; the proprietary model is the business.

We are not starting from zero. The database already contains ~50,000 fault → resolution pairs (built from public web sources) and 10,000+ manufacturer technical documents. That is the training foundation — cold start is behind us.

  • The data loopWe give tradespeople a deliberately generous freemium tier. They use it daily. Each interaction produces real diagnostic data that trains and refines our models.
  • Compounding advantageMore usage → better model → better product → more usage. A generalist competitor cannot catch up: no starting corpus, no field loop.
  • Downstream monetizationThis asset unlocks manufacturer SAV deflection and field intelligence — hotlines that do not scale, on a field that remains a black box.

1.4

Why me

Thibaut Marcelot. 15 years in HVAC engineering on both sides of the table: field, sales, and distribution at CIAT and Daikin, plus energy renovation and solar. I know what SAV costs a manufacturer and how a technician actually decides on a job site — most AI founders have neither. My distribution background gives direct access to the counter channel where the technicians who feed the loop already pass through.

1.5

Where we stand — no embellishment

130 users on a generous freemium tier, designed at this stage for one thing: feed the database and calibrate the first model settings. I am not claiming a proven business — I am claiming a starting corpus no one else has, a data loop that is running, and the domain credibility to monetize it. That is what this raise must transform: from a training base to a model whose quality generates revenue.

Assets in place

  • ~50,000 fault → resolution pairs
  • 10,000+ manufacturer technical documents
  • 11 languages in production
  • API / MCP functional, in testing phase
  • 130 freemium users feeding the model
Chapitre 02

Market

France as the beachhead; technical knowledge without borders.

2.1

Starting ground: France

French HVAC represents €7–8.8B in revenue across ~45,000 companies, ~78% with fewer than 10 employees — a fabric of tradespeople and micro-businesses, our core target. The addressable technician pool (refrigeration, HVAC, and heating engineers working on heat pumps and boilers) is around 70,000–80,000 people in France.

Permanent tension: ~15,000 unfilled positions per year, near-zero unemployment, departure of experienced profiles. Demand overflows; what is missing is the capacity to handle it with less experienced technicians on an increasingly electronic installed base. The fleet is growing fast — ~179,000 air-to-water heat pumps sold in France in 2025, with REPowerEU targeting 10M heat pumps in Europe by 2027.

2.2

This market is not French — it is global

This is the core market thesis: we only deal with technical knowledge, never regulation. And technical knowledge has no borders. A Daikin VRV, a Mitsubishi heat pump, a Viessmann boiler are strictly identical from one country to the next — same fault codes, same procedures, same failures. Troubleshooting knowledge transfers 100% internationally, where a regulation-centric product would stay locked behind a border.

In practice, the asset we build in France is immediately reusable everywhere. The application is already multilingual — 11 languages in production. Expansion is not a localization project; it is opening a market on a product and model that are already ready:

  • English-speaking marketsUK, Australia, New Zealand — same language, same equipment, accessible without adaptation.
  • Continental EuropeDE, IT, ES… — identical equipment (AC, VRV, heat pumps, boilers), languages already covered.

2.3

How we measure this market (and why not TAM)

A TAM of "1.5M technicians × €228" proves nothing. What matters at pre-seed is a defensible bottoms-up view, market by market. Beachhead: already-digitalized tradespeople and service/maintenance companies running with temporary staff (maximum pain — generic technician, unknown machine, every day).

On a France base of ~50,000 activatable technicians, the issue is not market size but freemium conversion rate. The table below shows full-penetration potential — an upper bound, not a forecast; actual landing pace is detailed in the financial annex:

France base penetrationPaying usersARR per market
2%~1 000~228 k€
5%~2 500~570 k€
10%~5 000~1,14 M€
Full penetration potential on ~50,000 activatable technicians — a ceiling, not a forecast.

This potential replays identically on each market, at near-zero marginal product cost (11 languages already in place).

2.4

Where the real prize size comes from

The mobile app in a single country is a few million euros of ARR: a profitable beachhead, not a billion-dollar outcome. The prize size comes from stacking three levers, all unlocked by the same asset:

  • 1
    Multi-market replication11 languages already live — expansion means opening, not building.
  • 2
    API/MCP layer (P2)Functional, in testing, pricing open: sold to maintenance companies, integrators, and manufacturers — value per account far exceeds €19/month.
  • 3
    Proprietary modelThe underlying asset a generalist cannot replicate without the corpus.

The tradesperson market, multiplied by languages already covered, funds building the asset; the asset opens a global market.

Market sources: abcclim.net (French HVAC employment), recrute.gesec.fr (vacancies), European Heat Pump Association (REPowerEU technician shortfall), cedeo.fr / dispart.fr (EasySAV competitor).

Chapitre 03

Segmentation & targets

One user, three payers — service companies first, tradespeople as the data engine.

3.1

The user never changes: the field technician

Whatever the payer, the user is always the same: a technician, on a job site, facing a machine. That is what makes the model coherent — one product, one experience, one data loop. What changes is who pays and with what budget.

3.2

Three payers, one assumed hierarchy

PayerWho decidesWhy it is the right entry pointRole
Service / maintenance companies (core paying segment)Operations manager / ownerMaximum pain (temp staff, rotating crews), company budget, multiple technicians per accountRevenue and the densest data volume
Tradespeople & micro-businesses (the engine)The technician themselvesIndividual decision, fast adoption, personal/pro paymentModel fuel + brand
Manufacturers & integrators (P2)Technical / SAV leadershipSAV deflection, field intelligence, API/MCPValue layer, unlocked once the model is proven

3.3

Why the service company is the best paying entry point

The solo tradesperson is numerous but a unit sale — personal, fragile. The service/maintenance company checks every box the tradesperson does not:

  • Structural painThey run with temporary staff and rotating profiles — every day, a different technician on an unknown machine. Exactly the gap the copilot fills.
  • Budget already existsA company expense (productivity, fewer return visits), not a personal subscription to justify.
  • One account = multiple seatsOne signed logo means 5, 20, 50 technicians — 5, 20, 50 times more usage and data per customer won. The best payer is also the best data producer.

3.4

The tradesperson: data engine, not immediate cash cow

We are not betting profitability on solo tradesperson ARPU. The tradesperson is volume — feeding the model through generous freemium and installing the brand in the trade. Conversion to paid is precisely the bet this raise must quantify. We treat them as a data and awareness engine, not the revenue column we commit to.

Pricing

€19/month for individual tradespeople (mobile app). €15/seat for teams of 5 or more (service companies) — the payer #1 segment.

3.5

What we deliberately exclude — for now

No manufacturer white-label or large API deals until the model has proven its quality in the field. This is not a concession — it is focus: we sign the manufacturer layer from a position of strength, once data and model are undeniable — not by selling a promise.

Chapitre 04

Competition & alternatives

The real competitor is what technicians already do. AskMarcel does not create the need — it replaces a workaround they already perform poorly.

4.1

The real competitor: the technician's current reflex

No one waits for AskMarcel to troubleshoot. Facing an unknown fault, the technician already does one of these five things:

  • 1
    Ask ChatGPT / Gemini / Perplexity — now reflex #1, and our real competitor.
  • 2
    Call the manufacturer hotline — when it exists, is open, and is the right brand.
  • 3
    Search a PDF manual — if they can find it.
  • 4
    Post on a Facebook group / forum / watch YouTube.
  • 5
    Call a senior colleague — the historical competitor, the one retiring.

We do not create the need. We replace a workaround technicians already perform poorly.

4.2

Why each alternative falls short

Generic AI (ChatGPT and others) delivers a plausible answer, not a reliable one. No up-to-date manufacturer corpus, no knowledge of real faults on a specific model: on a niche fault code, generic AI invents with confidence. In troubleshooting, a wrong but confident answer is worse than no answer.

The manufacturer hotline is reliable but single-brand, slow, business-hours only, and does not scale — yet the technician works on ten brands in a week.

Documentation and communities: one is official but unreadable on site; the other is human but right half the time, with zero accountability.

4.3

Positioning — without fooling ourselves

Each alternative is good at something. The honest comparison is not "them bad, us perfect":

Solution comparison
CritèreAskMarcelAlternatives
Technical reliabilityHigh (dedicated corpus)Low (plausible)
Multi-brandVaries
Usable on siteDifficult
Professional accountabilityFramedUnclear
ScalabilityHighLimited
Technical reliabilityMulti-brandUsable on siteCostScale
Generic AI (ChatGPT)Low (plausible)YesYesFreeHigh
Manufacturer hotlineHighNo (single-brand)Medium (call)VariableLow
Docs / PDF manualsHighYesLowFree
Communities / YouTubeRandomYesMediumFreeHigh
Distributor tool (EasySAV)HighYes (heating focus)YesTied to distributorMedium
AskMarcelHigh (dedicated corpus)YesYes (mobile-first)LowHigh
Our unique position: the only solution combining hotline-level reliability and multi-brand AI coverage, usable on site, without being tied to a parts catalog.

4.4

The moat — and its durability against large players

The real investor question: "What if OpenAI or Daikin does it?" Honest answer:

  • Against a generalist AIIt will not build a vertical HVAC corpus or a field data loop for a niche trade — and even if it tried, our asset (10,000+ manufacturer docs + 50,000 fault→resolution pairs + field usage) is precisely what model size cannot catch up to.
  • Against a manufacturerSingle-brand by nature. Value stops at their own machines; the technician is multi-brand. No manufacturer will aggregate competitors' data. Our multi-brand neutrality is structurally out of reach.

What compounds

The moat is not the product — it can be copied. It is the proprietary corpus + field loop + multi-brand neutrality. The more we run, the deeper it gets.

Chapitre 05

Value proposition

Reduce technical uncertainty. Discrete value, immediately perceptible in the field.

5.1

The promise, in one line

"Secure the right diagnosis the first time. We do not replace the technician — we avoid the second trip. That is all, and it is enough to pay for itself."

5.2

Value translated into money (not feelings)

For the service/maintenance company (payer #1). The painful cost is not search time — it is the return visit: a trip redone because the fault was not resolved, or the wrong part ordered. Each avoided return saves a trip, hours, and a part — the order of magnitude of a service call, far more than the monthly subscription. One avoided return per technician per month pays for the tool several times over. Bonus: a temp or junior performs much closer to a senior — exactly this segment's problem.

  • For the tradespersonLess time searching, fewer hotline calls, immediate credibility with the customer — the right answer instead of "I'll call you back."
  • For the manufacturer (P2)The same resolution that avoids a return visit also avoids a call to their hotline: direct SAV deflection and visibility on what actually fails in their installed base.

5.3

Why value grows with usage

This is the retention mechanism. The more the technician — or the company — uses AskMarcel, the more the model learns their machines, brands, and recurring faults, and the more accurate answers become for them. The tool you leave least is the one that knows you best. Value does not plateau after subscription: it accumulates. Time works for us — on usage and on the model.

5.4

Price / value alignment

At €19/month against one avoided return visit, the math does not need to be sold. No "Big Deal" to negotiate, no heavy budget approval: the decision sits with whoever feels the pain. Price is not the blocker — discovery and trust are, and that is exactly what GTM must address.

Value / price alignment

  • Accessible price (€19/month) limiting friction
  • No "Big Deal" needed to start
  • Value increases with usage (retention)
  • Model favoring solidity over speed
Chapitre 06

Marketing & go-to-market

Channel arbitrage validated 18/06/2026: keep what works, launch distributors, drop paid social.

6.1

Principle: we do not buy growth, we compound it

Two channels already drive real traction at near-zero cost. The rule is simple: double down before opening anything new, and any channel that cannot prove its acquisition cost is cut — not maintained out of habit.

  • Compound what worksSEO and emailing already drive real traction (+150% traffic, 65k contacts) — we double down before opening a new channel.
  • Payer changes, user staysThe distributor channel does not replace the tradesperson user — it changes who pays and how we reach them.
  • Credibility through the networkThe founder (ex-Daikin France sales) opens doors no ad budget can buy.
  • Discipline on channels that do not convertA channel that cannot prove its CAC is abandoned, not maintained by habit.

6.2

Foundation: programmatic SEO + emailing

Programmatic SEO. 302 technical articles already published, +150% organic traffic. Channel #1: marginal cost, compounding acquisition, and it replicates by language. With 11 languages in production, the content engine that acquires in France is the same one that acquires in the UK, Germany, or Australia. Organic acquisition is, like the product, already international.

FGAS/RGE emailing. 65,000 qualified tradesperson contacts — a direct list at near-zero cost, exactly our target. Freemium activation channel.

ChannelStatusRole
Programmatic SEO (302 articles)✅ Kept — channel #1+150% traffic, organic acquisition, marginal cost
FGAS/RGE emailing (65k contacts)✅ KeptQualified tradesperson list, near-zero-cost direct channel
HVAC distributors (counter sales)🆕 Launched — Q3–Q4 2026 priority30–50% commission, access to technicians already trusting their counter
Manufacturers (data / SAV deflection)⏸ Data only for nowNo direct revenue today, prepares white-label 2027+
Meta Ads (Facebook / Instagram)❌ AbandonedCAC too high, poorly qualified users, no retention signal
YouTube (refrigeration techs)🧪 To testExploratory channel, demonstrative content, not yet budgeted
Channel arbitrage from AskMarcel Strategy & Vision, validated 18/06/2026.

6.3

Network leverage: HVAC distributor counters

This is the advantage neither SEO nor an ad budget can buy. A technician — and a service company — passes through the distributor counter several times a month: the most regular field touchpoint that exists. And I have direct access from 15 years in the sector (CIAT, Daikin) and strong knowledge of HVAC distributor networks.

The counter serves two distinct goals: get tradespeople into freemium (volume + data), and reach service/maintenance companies — payer #1 — where they already buy supplies. Execution detail is in Section 8.

Timing advantage

This channel is not a bet: it is direct exploitation of an asset the founder already holds (warm relationships, domain credibility) before competition captures it.

6.4

What we stopped — and why

Meta Ads: abandoned

CAC too high for poorly qualified users, with no correlation to retention. Budget reallocated to the SEO/emailing foundation and counter activation. Stating this in the dossier is not a weakness — it proves we read our numbers and cut what does not work.

6.5

Overall coherence

Every channel serves the same mechanism: SEO and emailing fill freemium and feed the model; counters open the door to payer #1 and densify data (one service account = multiple technicians). We are not stacking channels — we are feeding a loop.

Chapitre 07

Business model

The user never changes: the field technician. What changes is who pays.

7.1

The user never changes — the payer does

One product, one experience, one trained model. What varies is who pays — and that is precisely what makes the model robust: we never depend on a single customer type. Three revenue sources stack, from wedge to value.

7.2

Three revenue sources

SourceWho paysPriceStatusRole
Mobile app — tradespersonThe technician€19/month (€228/year)LiveWedge and data volume
Mobile app — team / serviceThe company€19/seat, tiered to €15/seat from 5 usersStartingRevenue #1 (multi-seat)
API / MCP (P2)Maintenance companies, integrators, manufacturersOpen, not definedFunctional, in testingValue layer, gated

7.3

Distributor channel — hybrid, no sales force

Two mechanics depending on the customer, as counter reality dictates:

  • Tradesperson at counter → 30–50% commissionThe distributor sells the subscription and keeps margin. No sales force to build on our side: they already sell to the technician daily. Net AskMarcel per tradesperson: €114–160/year after commission (vs €228 in direct sale).
  • Service company → direct sale, distributor as referrerOn a negotiated multi-seat account, commission makes no sense: the distributor opens the door, we contract directly, keep full margin and the customer relationship.
ParameterValue
Public price€19/month (€228/year), unchanged
Distributor commission30% to 50%
Net AskMarcel / technician / year≈ €114 to €160
TrackingCounter QR code + online referral code (Supabase)
Potential per active counter≈ €2,280 to €3,200/year net (base 20 conversions/year)
Economics recalculated on confirmed real price (€19/month).

7.4

API/MCP layer (P2) — upside, not a promise

It is already functional and in testing, but we have not set pricing — deliberately. We do not price value we have not yet measured with maintenance companies and manufacturers. What I know: value per account on an API integration has no relation to €19/month. It is the model's upside — stated as such, not as committed revenue. It unlocks once the model is proven in the field.

Status: not yet activated commercially

API/MCP is functional in test. Industrialization and pricing follow proof of field quality and first service company accounts.

7.5

Why this model holds

The tradesperson funds building the data. Data trains the model. The model unlocks high-value sources (service seats, then API/manufacturers). At no point do we depend on a single payer, and at no point do we build an expensive sales force: the distributor network and founder carry acquisition. A frugal model that turns a €19 wedge into an asset sold for much more.

Chapitre 08

Commercial strategy — distributor channel

A free pilot before any contract. Prove counter usage before asking for commission.

8.1

Principle: open the door before signing

This channel is not sold top-down. It is built through usage: we prove a counter generates real usage before formalizing a commission contract. We approach HVAC distributor networks, and usage numbers decide what happens next — not a sales deck.

8.2

Two motions at the same counter

One counter, two distinct targets activated differently:

  • Tradespeople enter freemium via the counter (QR code, referral code). Once usage is proven, the distributor resells the subscription and earns commission. This fills volume and data.
  • Service/maintenance companies buying at the same counter are identified and worked through direct sale (payer #1, multi-seat contract).

Everything is measured, not declared: counter QR code, referral code, Supabase tracking. We know exactly which counter produces which usage.

8.3

The 90-day plan

W1 – W4

Free pilots

2–3 counters from the direct network (ex-Daikin) test the tool free. Gate 1 (G1): at least 5 active technicians per week on the same counter, for 3 consecutive weeks.

W5 – W8

First commission contracts

First commission contract signed (30–50%) on the validated pilot counter. Gate 2 (G2): at least 3 subscriptions sold through that counter.

W9 – W12+

Replication

Outreach to 29 mapped distributors outside the direct network. Gate 3 (G3): at least 3 active counters simultaneously — the threshold that opens white-label discussion (Phase 3, see chapter 7).

Gates, not promises

Each step is conditioned on a measurable threshold (G1, G2, G3), not a fixed calendar. The channel only advances when field usage confirms it — consistent with the marketing discipline described in chapter 6.

8.4

Sequence: independents and regionals first, nationals later

We start with independent and regional distributors. They are more agile, decisions happen fast, and critically they currently have no diagnostic tool to offer their customers. We bring a service that differentiates them, on a neutral, multi-brand product.

Nationals come second, from a position of strength once the model is proven. It is also a competitive necessity: some, like Saint-Gobain (CEDEO/Dispart), already push their own app (EasySAV) — so either locked or head-on. No point attacking them first; better to prove the model where the field is open.

8.5

Why we win against a distributor tool like EasySAV

EasySAV (Dispart / Saint-Gobain) vs AskMarcel
CritèreAskMarcelEasySAV
IndependenceNeutral — best answer, not the right catalogTied to distributor, built to sell parts
NatureTrained AI model + field data loopLookup database (docs + fault codes)
ScopeMulti-equipment (AC, VRV, heat pump, boiler), 11 languagesMostly heating/boilers, France
AlignmentServes the technicianServes the distributor

Their existence proves the need. Their dependence on a parts catalog is precisely the door we take: an independent does not want to send customers to a national competitor's tool.

Chapitre 09

Product roadmap

Evolution driven by the distributor channel launch, not feature stacking.

9.1

Principle: data-driven, not feature-driven

The roadmap follows one logic: run the data loop and move up the value stack. We are not chasing features — we consolidate the asset (corpus + model) and open monetization layers in order.

9.2

Three phases

P1 — In progress

The foundation

Mobile app stabilization, generous freemium, usage tracking (counter QR, referral code, Supabase), continuous model reliability (Mistral, Qwen). Mobile app stabilization · Counter QR + referral tracking · AI model reliability · Undeniable answer quality.

P2 — Functional, in test

Value layer (API / MCP)

API/MCP industrialization for service companies, integrators and eventually manufacturers. Sellable API/MCP · Service company and integrator accounts · From €19 wedge to high-value accounts.

P3 — Upcoming

Replication and manufacturer data

Market expansion (11 languages already in place), field intelligence for manufacturer SAV deflection. Multi-market expansion · Manufacturer SAV deflection · Manufacturer field intelligence.

9.3

What we are not doing (deliberately)

  • No full CMMS or technical ERP
  • No manufacturer white-label before the model has proven itself
  • No proprietary hardware

Each "no" protects focus on the only asset that matters: the model and its data.

9.4

Alignment with means

Realistic execution

  • Roadmap carried by a solo founder with occasional freelance, no heavy technical debt
  • Product priority aligned with distributor channel Gates G1–G3 (chapter 8)
  • Controlled AI inference costs (Mistral, Qwen)
  • Full consistency with the financial forecast
Chapitre 10

Organization & team

A deliberately light structure — solo founder, hire after proof.

10.1

The team today: deliberately lean

Thibaut Marcelot — founder, sole operator. 15 years in HVAC engineering: field, sales, and distribution (CIAT, Daikin), plus energy renovation and solar. I build the product and lead commercial. My distribution background gives direct access to the counter channel.

Development of certain features uses occasional freelance — no hiring ahead of need, no heavy structure. Spend when there is proven need, not before.

Legal structure

The product currently runs in test phase via my existing web development structure (th1b4ut.dev). Upon funding validation: creation of a SAS — France or Estonia, to be decided with investors — and transfer of domains, database, and Android/Apple store accounts to that entity. No legal costs committed in advance.

10.2

Hiring plan: traction-driven, not plan-driven

We do not hire based on a forecast. The first hire — likely a commercial profile to replicate the distributor channel — only happens after the first signed contract. Product/support reinforcements follow API layer scale-up, not the reverse. Same discipline as channels: we fund what is proven.

After G2

First commercial hire

Replicate the distributor channel once the first commission contract is signed.

Year 2

Product & support reinforcement

User support for growing service accounts. Development for reliability as API layer scales.

10.3

Why this is a strength, not a confession

A solo founder supported by occasional freelance is minimal fixed cost and maximum decision speed during the most uncertain phase. The risk it creates (key-person dependency) is real — I address it head-on in Section 11 rather than hiding it.

Chapitre 11

Risks & key factors

A lucid approach to risk, integrated from the start into the business model design.

11.1

Risks — named frankly

Risque Élevé

Founder dependency (solo)

Mitigation

Lean structure assumed during uncertain phase; freelance for execution; first hire after first contract. Founder combines product + domain + commercial — rare and hard to rebuild as a team.

Risque Élevé

Freemium conversion

Mitigation

Central bet — what this raise must measure. Service company (payer #1) reduces dependence on tradesperson ARPU; value anchored on money saved (avoided return visits), not comfort.

Risque Moyen

Model reliability (hallucination)

Mitigation

Proprietary corpus (manufacturer docs + 50k faults), continuous fine-tuning on field data, decision-support positioning — never prescription.

Risque Moyen

Established competitor (EasySAV, generic AI)

Mitigation

Multi-brand neutrality (out of reach for manufacturer or distributor), trained model a generalist cannot replicate, entry through independents/regionals left open.

Risque Faible

Legal liability

Mitigation

Decision support, never prescription; explicit disclaimers.

Risque Moyen

Cash / runway

Mitigation

Minimal fixed costs, single gated ticket, expansion conditioned on proof.

11.2

What makes the project solid

The project does not rest on a single bet or a "miracle." The pain is real and lived, the asset (corpus + model) already exists in part, the market is global by nature, and the cost structure is frugal by design. Major risks — solo founder and conversion — are stated plainly, because a serious investor prefers a lucid founder over a dossier that pretends there are none.

Risk mastery

  • The project does not rely on a single hypothesis or a "miracle"
  • Risks are identified and mitigated structurally
  • Funding need is anticipated to secure execution
  • Trajectory is prudent by design
Chapitre 12

Conclusion & vision

A realistic ambition: become the HVAC field assistance standard.

12.1

A realistic ambition, a real asset

"Rather than radically transforming the trade, AskMarcel secures the decision at the moment it costs the most — diagnosis — and captures, in doing so, the only data neither a generalist nor a manufacturer can assemble: real, multi-brand, field diagnostics."

This pragmatic approach is the project foundation. It enables a deliberately progressive trajectory, limiting risk and strengthening credibility with users.

12.2

The size of the prize

The HVAC technical assistance standard, in Europe and beyond. A product already multilingual, in a market where equipment is identical country to country, backed by a proprietary model whose quality deepens with every intervention. The tradesperson beachhead funds the asset; the asset opens service companies, integrators, and manufacturers.

  • A field assistance standard for HVAC in Europe
  • A reference tool for skills development
  • Reliable support in a structurally strained sector

12.3

Why now, why me

Why now?

  • Real, identified, growing market demand
  • Clear, validated value proposition
  • Readable, scalable business model
  • Disciplined, agile organization
  • Coherent, prudent financial trajectory

Need is documented and growing, the asset is already seeded (10k+ docs, 50k faults, 11 languages, API/MCP in test), and the founder knows both sides of the table — field and distribution. The window is open: players like Saint-Gobain are already moving. Funding transforms a seeded asset into a model whose quality creates revenue — not masking a structural weakness.

The Ask

Levée Pre-Seed — 50 000€

Ticket unique, débloqué contre jalons d'usage. Runway ~12 mois, structure lean.

Montant recherché

50,000€

Single ticket, unlocked against usage milestones.

Runway~12 months, deliberately frugal structure

Ce que ça finance

  • 1Prove freemium → paid conversion and activate the HVAC distributor counter channel (QR, referral)
  • 2Industrialize API/MCP for service companies and integrators
  • 3Improve AI model reliability over 12 months of accumulated field data

Allocation des fonds

Founder living expenses19,200€ · 38%

€1,600/month — 100% on the project

Product & AI14,000€ · 28%

Inference (Mistral, Qwen), model reliability, occasional freelance, API/MCP industrialization

Infra & security9,000€ · 18%

Hosting, database, SAS creation, domain + store account transfer

Acquisition7,800€ · 16%

Programmatic SEO, emailing, counter materials (QR, referral)

Jalons

  • W41 counter generating ≥5 active technicians/week for 3 weeks
  • W81st commission contract signed + 1st direct service company
  • W12≥3 active counters + measured freemium→paid conversion rate
  • OngoingIndustrialized API/MCP, 1st service company/integrator on API

At exit: measured conversion rate, first paying service accounts, monetizable API in hand, and a model sharpened over 12 months of field data.

Écrire au fondateur
Chapitre 14

Annexe — trajectoire financière

Bottoms-up 18–24 mois. Pas de P&L à 5 ans.

14.1

Principle: bottoms-up, not a 5-year P&L

We model the next 18–24 months from real drivers — paying users, service seats, active counters — so every assumption is debatable.

14.2

Unit economics

Building blockPublic priceNet to AskMarcelWhy
Tradesperson — direct sale€19/month (€228/year)~€155–195/yearAfter store commission (15–30%)
Tradesperson — via counter€19/month~€114–160/yearAfter distributor commission (30–50%)
Service seat (team ≥5)€15/month (€180/year)~€150–180/yearDirect sale, off-store if proper invoicing
Real gross margin around 75–85% after inference and commissions — not 90%.

14.3

18–24 month scenarios

DriverLowMidHigh
Paying tradespeople at M1280200400
Service seats at M12103070
MRR ≈ M12~€1,700~€4,250~€8,650
Paying tradespeople at M24200450800
Service seats at M2440100200
MRR ≈ M24~€4,400~€10,050~€18,200
ARR ≈ M24~€53k~€120k~€218k
Calculation: tradespeople × €19 + service seats × €15. No API/MCP or manufacturer revenue included — that's the upside, kept out of the model.

MRR trajectory (mid scenario)

14.4

What really drives the result

Three variables drive the outcome: freemium conversion rate, number of active counters, and service companies signed.

With €50k and a lean structure (€1,600/month living expenses, controlled infra/inference), operational break-even does not require enormous MRR. In the mid scenario, M24 MRR (~€10k) covers a light structure — without touching the API/manufacturer layer, which is the real amplifier.

L'équipe

Fondateur solo, crédibilité terrain

Thibaut Marcelot

Thibaut Marcelot

Founder

15 years in HVAC, before writing the first line of code. Field work, sales, network development — CIAT, Daikin, energy renovation and solar.

One problem followed me throughout: the right information is never there at the right time. The technician is on site, under pressure, and the manufacturer manual they need is in an unreadable PDF somewhere on a server.

It's not an intelligence problem. It's an access problem.

I built AskMarcel to solve that — not as an engineer discovering an industry, but as someone who lived the problem from the inside.

Documentation

Accédez au dossier complet

Dossier investisseur et annexe financière disponibles sur demande pour investisseurs qualifiés.

Dossier investisseur

Thèse, marché, GTM et modèle économique.

Annexe financière

Scénarios bottoms-up 18–24 mois (Bas/Moyen/Haut).