La couche de données techniques HVAC que ni ChatGPT ni un fabricant ne peut répliquer
Le Cerveau derrière les agents IA du dépannage HVAC. Un corpus sourcé constructeur, une API/MCP en production.
Last updated: 22/07/2026
At a glance
Dossier investisseurs — AskMarcel
La couche de données techniques HVAC pour les humains (app) et les agents IA (API/MCP). Levée equity — 35k€/10% ou 100k€/25%.
Last updated: 22/07/2026
| Item | Detail |
|---|---|
| What we're building | The HVAC technical data layer for humans (app) and AI agents (API/MCP) — a manufacturer-sourced corpus that neither ChatGPT nor a manufacturer can replicate |
| Positioning | The Brain, not the UI: we plug in behind the conversational stacks already running at the client — zero rebuild of their infra |
| The business | €490 pilot → €149/month Business subscription → Scale (manufacturers, distributors); €19 self-serve app as a volume motion |
| Amount sought | €35,000 for 10% (seed) or €100,000 for 25% (acceleration) |
| Runway | 12 to 18 months depending on the ticket, lean structure (solo founder + occasional freelance) |
| Traction | MCP in production · corpus of 126 brands / 905 models / 400,000+ error codes · SEO traffic ×5.8 · 65k qualified tradespeople |
Traction
Actifs déjà en place
Chiffres au 22/07/2026 — corpus sourcé constructeur, API/MCP en production et canaux d'acquisition.
Brands covered
Manufacturer-sourced
Models referenced
Series and ranges
Sourced error codes
Manual + page citation
API / MCP server
Claude · ChatGPT · Gemini connectors validated
Programmatic SEO articles
Organic traffic ×5.8
Qualified FGAS/RGE tradespeople
Named emailing list
Languages in production
European corpus
Corpus multi-marques
























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 covers 126 brands, 905 models and 400,000+ manufacturer-sourced error codes, with manual + page citation. 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
- 126 brands · 905 models · 400,000+ sourced error codes
- Manufacturer-sourced corpus with manual + page citation
- 11 languages in production
- API / MCP server live in production (Claude, ChatGPT, Gemini connectors)
- SEO traffic ×5.8 · 65k qualified tradespeople
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 ~65,000 activatable technicians and tradespeople, the issue is not market size but 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 penetration | Paying users | ARR per market |
|---|---|---|
| 2% | ~1 300 | ~0,3 M€ |
| 5% | ~3 250 | ~0,7 M€ |
| 10% | ~6 500 | ~1,5 M€ |
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:
- 1Multi-market replication11 languages already live — expansion means opening, not building.
- 2API/MCP connector layerLive in production, monetized at €149/month (Business): maintenance companies, CMMS/ERP vendors, e-commerce — value per account far exceeds the €19 self-serve app.
- 3Proprietary 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).
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
| Payer | Who decides | Why it is the right entry point | Role |
|---|---|---|---|
| Service / maintenance companies (core paying segment) | Operations manager / owner | Maximum pain (temp staff, rotating crews), company budget, multiple technicians per account | Revenue and the densest data volume |
| Tradespeople & micro-businesses (the engine) | The technician themselves | Individual decision, fast adoption, personal/pro payment | Model fuel + brand |
| Manufacturers & integrators (P2) | Technical / SAV leadership | SAV deflection, field intelligence, API/MCP | Value 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 (self-serve app). €149/month (Business, API/MCP) for 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.
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:
- 1Ask ChatGPT / Gemini / Perplexity — now reflex #1, and our real competitor.
- 2Call the manufacturer hotline — when it exists, is open, and is the right brand.
- 3Search a PDF manual — if they can find it.
- 4Post on a Facebook group / forum / watch YouTube.
- 5Call 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":
| Critère | AskMarcel | Alternatives |
|---|---|---|
| Technical reliability | High (dedicated corpus) | Low (plausible) |
| Multi-brand | Varies | |
| Usable on site | Difficult | |
| Professional accountability | Framed | Unclear |
| Scalability | High | Limited |
| Technical reliability | Multi-brand | Usable on site | Cost | Scale | |
|---|---|---|---|---|---|
| Generic AI (ChatGPT) | Low (plausible) | Yes | Yes | Free | High |
| Manufacturer hotline | High | No (single-brand) | Medium (call) | Variable | Low |
| Docs / PDF manuals | High | Yes | Low | Free | — |
| Communities / YouTube | Random | Yes | Medium | Free | High |
| Distributor tool (EasySAV) | High | Yes (heating focus) | Yes | Tied to distributor | Medium |
| AskMarcel | High (dedicated corpus) | Yes | Yes (mobile-first) | Low | High |
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 (126 brands · 905 models · 400,000+ sourced error codes + 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.
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
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.
| Channel | Status | Role |
|---|---|---|
| Programmatic SEO (302 articles) | ✅ Kept — channel #1 | +150% traffic, organic acquisition, marginal cost |
| FGAS/RGE emailing (65k contacts) | ✅ Kept | Qualified tradesperson list, near-zero-cost direct channel |
| HVAC distributors (counter sales) | 🆕 Launched — Q3–Q4 2026 priority | 30–50% commission, access to technicians already trusting their counter |
| Manufacturers (data / SAV deflection) | ⏸ Data only for now | No direct revenue today, prepares white-label 2027+ |
| Meta Ads (Facebook / Instagram) | ❌ Abandoned | CAC too high, poorly qualified users, no retention signal |
| YouTube (refrigeration techs) | 🧪 To test | Exploratory channel, demonstrative content, not yet budgeted |
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.
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
| Source | Who pays | Price | Status | Role |
|---|---|---|---|---|
| Mobile app — tradesperson | The technician | €19/month (€228/year) | Live | Wedge and data volume |
| Mobile app — team / service | The company | €149/month (Business, API/MCP) | Starting | Revenue #1 (Business) |
| API / MCP (connector layer) | Maintenance companies, integrators, manufacturers | €149/month (Business) | Live in production | Core recurring revenue |
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 Business account (API/MCP), commission makes no sense: the distributor opens the door, we contract directly, keep full margin and the customer relationship.
| Parameter | Value |
|---|---|
| Public price | €19/month (€228/year), unchanged |
| Distributor commission | 30% to 50% |
| Net AskMarcel / technician / year | ≈ €114 to €160 |
| Tracking | Counter QR code + online referral code (Supabase) |
| Potential per active counter | ≈ €2,280 to €3,200/year net (base 20 conversions/year) |
7.4
API/MCP — the connector layer, live in production
The API/MCP server is live in production, with Claude, ChatGPT and Gemini connectors validated in real conditions. It is now the core of the model: we plug in behind the conversational stacks the client already runs (ManyChat, ElevenLabs, MCP agents), with no rebuild of their infra. It is monetized through the €149/month Business plan — value per account has no relation to the €19 self-serve app.
Status: live and monetized; Scale contracts not yet pre-counted
The bottom-up forecast stays deliberately conservative: it does not pre-count Scale contracts (manufacturers, distributors), activated once the first €490 pilots convert to Business subscriptions.
7.5
Why this model holds
The tradesperson funds building the data. Data trains the model. The model unlocks high-value sources (Business API/MCP, then Scale/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.
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, Business API/MCP 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
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.
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.
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
| Critère | AskMarcel | EasySAV |
|---|---|---|
| Independence | Neutral — best answer, not the right catalog | Tied to distributor, built to sell parts |
| Nature | Trained AI model + field data loop | Lookup database (docs + fault codes) |
| Scope | Multi-equipment (AC, VRV, heat pump, boiler), 11 languages | Mostly heating/boilers, France |
| Alignment | Serves the technician | Serves 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.
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
The data foundation
Manufacturer-sourced corpus (126 brands, 905 models, 400,000+ codes), MCP server in production, answer-layer quality audit (brand filter, enforceable manual/community citation). Manufacturer-sourced corpus · MCP server in production · Enforceable manual + page citation · Audited answer quality.
The connector layer (API / MCP)
We plug in behind conversational stacks already in production (ManyChat, ElevenLabs, MCP agents). €490 pilot → €149/month Business across three fronts: e-commerce, distributors, service companies. No-code + voice connectors (ElevenLabs) · €490 pilots → €149 subscriptions · Service companies, CMMS/ERP vendors.
Scale & replication
Scale contracts with manufacturers/distributors (white-label, SAV deflection), corpus expansion to the top 50 EU brands, FR·EN·DE rollout. First Scale contracts · Multi-market expansion (EU) · Manufacturer SAV deflection.
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
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 via my existing web development structure (th1b4ut.dev). Upon funding validation: creation of a SAS in France — chosen for eligibility to French public support and distributor trust — 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.
First commercial hire
Replicate the distributor channel once the first commission contract is signed.
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.
Risks & key factors
A lucid approach to risk, integrated from the start into the business model design.
11.1
Risks — named frankly
Founder dependency (solo)
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.
Freemium conversion
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.
Model reliability (hallucination)
Proprietary corpus (126 brands, 400k+ sourced codes), continuous fine-tuning on field data, decision-support positioning — never prescription.
Established competitor (EasySAV, generic AI)
Multi-brand neutrality (out of reach for manufacturer or distributor), trained model a generalist cannot replicate, entry through independents/regionals left open.
Legal liability
Decision support, never prescription; explicit disclaimers.
Cash / runway
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
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 (126 brands, 400k+ sourced codes, 11 languages, API/MCP live in production), 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 equity — 35 000€ ou 100 000€
Deux tickets d'entrée selon votre horizon : 35 000€ pour 10% (amorçage) ou 100 000€ pour 25% (accélération). Débloqué contre jalons.
Tickets d'entrée
Seed
12-month runway — cash in the first €490 pilots, prove conversion to €149/month Business plans and activate the HVAC distributor channel
Acceleration
18 months — industrialize the API/MCP connector layer, expand the corpus (top 50 EU brands), open FR·EN·DE and sign the first Scale contracts
Runway — 12 months (seed ticket) to 18 months (acceleration ticket), deliberately frugal structure
Ce que ça finance
- 1Convert €490 pilots into €149/month Business subscriptions across three fronts: HVAC e-commerce, independent distributors, service companies
- 2Industrialize the API/MCP connector layer — plug in behind conversational stacks already in production (ManyChat, ElevenLabs, MCP agents)
- 3Expand the manufacturer-sourced corpus and harden the model over 12 months of field data
Allocation des fonds
Inference (Mistral, Qwen), model reliability, API/MCP industrialization, no-code + voice connectors (ElevenLabs)
The three fronts: e-commerce outbound, distributor network, counter materials (QR, referral)
100% on the project through the pre-revenue phase
Hosting, database, company setup, domain + account transfer
Indicative allocation on the acceleration ticket (€100k); scaled down pro rata on the seed ticket.
Jalons
- W41st €490 pilot cashed in (e-commerce or distributor front)
- W83 active €149/month Business subscriptions + 1 distributor counter activated
- W12Measured pilot→subscription conversion rate + 1st Scale contract in negotiation
- OngoingIndustrialized API/MCP connector layer, corpus expanded to top 50 EU brands
At exit: measured conversion rate, first paying service accounts, monetizable API in hand, and a model sharpened over 12 months of field data.
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 block | Public price | Net to AskMarcel | Why |
|---|---|---|---|
| "Marcel Inside" pilot | €490 one-off | credited on annual | Entry point that triggers the subscription — not a services business |
| Business (API/MCP, connectors) | €149/month (€1,490/year) | ~€1,490/year | Service companies, CMMS/ERP vendors, e-commerce — core recurring revenue |
| Self-serve app (tradesperson) | €19/month (€228/year) | ~€155–195/year | Volume motion on the 65k dataset, after store commission |
| Scale (manufacturer / distributor) | on quote | annual contract + advance | White-label, SAV deflection, bounded regional exclusivity |
14.3
18–24 month scenarios
| Driver | Low | Mid | High |
|---|---|---|---|
| Business €149 subscriptions at M12 | 10 | 25 | 50 |
| Self-serve app €19 at M12 | 30 | 40 | 80 |
| MRR ≈ M12 | ~€1,700 | ~€4,250 | ~€8,650 |
| Business €149 subscriptions at M24 | 25 | 55 | 100 |
| Self-serve app €19 at M24 | 60 | 120 | 250 |
| MRR ≈ M24 | ~€4,400 | ~€10,050 | ~€18,200 |
| ARR ≈ M24 | ~€53k | ~€120k | ~€218k |
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
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.
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).
