What they do
AI agents interview staff, map work, draft roadmaps, with expert review feeding back
How close to us
4, very close
full loop for another buyer: structured knowledge from interviews, expert corrections improve system; they are the consultancy, not selling to one
Wedges
- W3 Company memory from every call, for consultancies adjacent
- W5 One layer for one type of small firm adjacent
- W7 Find and fix margin leaks adjacent
Who buys
- Firm size
- mid-market
- Buyer type
- business
- Role
- chief executive, chief operating officer or AI transformation lead
- UK
- not known
Their own description (from YC)
Foaster is building the AI-native McKinsey, starting with AI transformation.
We deploy AI agents that interview a company's employees at scale, map how work actually gets done, identify bottlenecks and AI opportunities, and draft a transformation roadmap in just 2 weeks. Expert consultants review the final outputs, add judgment where it matters, and feed their corrections back into the system.
We believe current AI models already have the capabilities required to automate most of the work consultants do. But traditional consulting firms use them to improve the old model: helping consultants work faster.
Foaster is instead rebuilding a new model from scratch, where agents do the work that needs scale and humans review the final outputs, bring expertise, and improve the system overtime.
We are starting with AI transformation, then progressively expanding to more and more consulting projects using the same approach. Every mission creates structured knowledge from interviews, workflow maps, recommendations, and expert corrections, so the system gets better with every client engagement.
The $500B consulting market will eventually be run by our AI agents and a small number of expert humans.
Full teardown
Foaster (YC Spring 2026): teardown vs XMarts
In one line: Foaster is an "AI-native consulting firm". AI agents interview every employee, map how work really runs, and draft an AI roadmap in about 10 days. Humans review it and help carry it out [site] [yc]. Threat: 2, because it targets the same size of buyer and the same "where do we start with AI" pain, but it is two people, has no named customer, and stops at a plan plus monthly guidance [site] [yc].
Expect a YC partner to ask "how are you different from Foaster?" because the brief names it as the closest funded rival to a consultancy angle. Their belief is nearly ours (agents do the work, humans review, the system learns from corrections). The honest difference is the output: they hand over a plan, we want to hand over finished work with a number.
1. Fact file
| Item | Finding | Source |
|---|---|---|
| What they say | Home page: "Your AI Transformation Partner." YC line: "The AI-native McKinsey". | [site] [yc] |
| Founded, team, funding | Founded 2026, YC Spring 2026, San Francisco, 2 people. Funding is UNVERIFIED: one aggregator says $500k pre-seed (June 2026), another source says a $130k seed, and a $1M total was not found. $500k matches the standard YC deal (inference). | [yc] [press] |
| Founders | Raphaël Dabadie (CEO) and Alexandre Combes (CTO). They ran an AI consultancy together for about a year, and built two LLM benchmarks that Greg Brockman, Elon Musk and others shared (Musk-shared study: they claim 12M+ views). Combes earlier built Répondia, AI agents that handled 60k+ customer calls for restaurant groups in France (they claim). What YC likely saw: proof they can get attention, real consulting time, and a technical co-founder (inference). | [yc] [site] |
| Customers | No named customers on the site or in the launch post. One anonymous case study: a 350-person professional services firm. | [site] [yc] |
| Price | Not public. They claim "3 to 5x" cheaper than traditional consulting. No job ads (0 open roles), so no price hint from hiring. | [site] [yc] |
| Path | No renames. Site headline says "AI Transformation Partner", YC long text says "AI-native McKinsey". Roadmap speed claim moves: "a few days" in the launch post, "about 10 days" on the site FAQ, "2 weeks" in the YC text. | [yc] [site] |
2. Their proposition, reverse engineered
| Part | What they do |
|---|---|
| Who buys | Companies from 50 staff up to large enterprises; the YC launch post asks for intros to "enterprises or mid-market companies" [site] [yc]. The signer is a CEO, COO or AI transformation lead (the case study quote is from a COO; the launch post asks for introductions to people who lead AI transformation, enablement or operations) [site] [yc]. |
| The pain | "Most companies confuse AI adoption with AI transformation." [yc] Scattered pilots, tools bought with no context, plans cut off from daily work [site]. COO quote: "We knew AI mattered, but we had no idea where to focus." [site] |
| The promise | A clear map of how the company really works and a ranked roadmap of where AI should change it, in about 10 days, then adoption support [site]. |
| How it works | 1. AI agents run parallel 30-minute interviews with all staff (about 7 days). 2. The system builds a map of work, handoffs and bottlenecks (about 2 days). 3. Agents draft a roadmap with impact and feasibility (about 3 days). 4. "Transform": agents upskill each employee and track adoption monthly; human experts give monthly guidance; Forward Deployed Engineers step in when building is needed [site]. Experts review the final output and feed corrections back into the system (they claim) [yc]. |
| The output | An operations map, a phased roadmap, a weekly personalised briefing per employee, a monthly adoption report. Format of the documents is not described [site]. |
| Proof style | Counts of interviews and processes mapped, and days taken. Case study: 280 people interviewed, 1,200+ processes mapped, 8 priority points, 9 days to blueprint (they claim). No money or time saved result [site]. |
| Who sells it | Founder-led: "book a call with our founders" [site]. Asks for introductions in the launch post [yc]. No sales roles advertised [yc]. |
| Price model | Three offers: one-time AI Transformation Assessment, monthly Continuous AI Transformation, and Enterprise Deployment with a dedicated Forward Deployed Engineer team. Prices not public [site]. |
| The story | Enemy: the old consulting model, "a sample of 30 interviews and a slide deck" [site]. Belief: models are already good enough to automate most consulting work, so agents do the scale work and a few experts add judgement; the $500B consulting market will be run by agents and few humans (they claim) [yc]. Blog titles: "McKinsey Won't Make You AI-Native. Agents Will." [blog] |
3. Positioning
- Category they claim: AI-native consulting firm, starting with AI transformation [yc] [site].
- Category a YC partner would put them in: an AI services company (agent-run consulting), with a process-discovery product inside (inference).
- What they want to be known for: the first consultancy where AI agents, not junior staff, do the fieldwork.
- Where they sit on the map: layers L1/L2/L3 = 2/2/1, buyer = mid-market, business. The screen pass scored them 2/2/2 from YC text alone; the full read lowers L3 to 1 (see section 4).
4. Three layers vs ours
| Layer | Them (score + evidence) | Us (Marton's logs show) |
|---|---|---|
| L1 Company brain | 2. The map of operations is kept current and the system stores interviews, workflow maps and expert corrections across engagements (they claim) [yc] [site]. It is a lasting model of the client's work, but the client sees it as a deliverable, not a brain other work reads (inference). | A vault of about 1,400 pages, 112 call transcripts and about 8,700 customer quotes, searchable by meaning. Built on his own business only. |
| L2 Agent orchestration | 2. Several agent steps (interview, map, roadmap, upskill, track) with human review and Forward Deployed Engineer handovers [site]. No claim that agents do the client's work. | About 5,600 jobs run by agents from three makers (OpenAI, Anthropic, Google), each checked by a different AI, then automatic tests. 864 of the jobs reached the main code (about 15%). |
| L3 Execution and learning | 1. They track adoption monthly [site], which counts use, not results. "Expert corrections feed back" is their claim, with no public log of a change made or a result checked [yc]. Doing the work is left to the client or to an FDE team [site]. | About 160 failure records, each with an automatic check. Offer feedback lines fell from 44 to 18 as 22 lint rules were added. Nobody outside has run it yet, and merges were unreliable in October. |
5. Same and different
| Topic | Same or different | Them | Us |
|---|---|---|---|
| Core belief | Same | Agents do the scale work, humans review, the system improves from corrections [yc]. | Same (brief). |
| Where knowledge comes from | Similar | Agent-led interviews with every employee [site]. | Marton's logs show recorded calls and checked work feed the vault. |
| Owner of the result | Different | The client owns the plan and carries it out; FDEs help when asked [site]. | Target: our agents do the work and we own one number (needs proof). |
| Kind of work | Different | Diagnosis and planning, then adoption support [site]. | Marton's logs show agents write, check and merge code and content on his own business. |
| Buyer size | Overlapping | 50 staff to large enterprise; example is 350 people [site]. | Target: SMEs and scale-ups (brief). No customer yet. |
| Proof | Different | Interviews held and processes mapped; no money result [site]. | No customer number yet. One internal number: offer feedback lines 44 to 18. |
| Delivery | Similar | Two founders plus agents plus expert reviewers [yc]. | Two founders plus the system. |
| Speed | Different | Roadmap in about 10 days [site]. | Not set yet. First value is not yet measured. |
| Price | Unknown | "3 to 5x" below consulting (they claim); not listed [site]. | Not set yet. |
| Traction | Different | One anonymous case study, no named logo [site]. | No paying customer (brief). |
6. Wedge overlap
| Wedge | Fit | Why |
|---|---|---|
| W3 Company memory from every call, for consultancies | adjacent | Same raw material (what people say about their work), different buyer: they ARE the consultancy, not a seller to one [yc]. They also compete for the same client budget a services firm would spend. |
| W5 One layer for one type of small firm | adjacent | They do cross-industry diagnosis; W5 is one type of firm, built once, sold many times. Their case study firm is professional services [site]. |
| W7 Find and fix margin leaks for commercial or finance leaders | adjacent | Same buyer size and a similar "find the waste" job, but they find AI opportunities in all roles, not money leaks, and do not run the fix [site]. |
No wedge is direct. W4 (fit call to proposal) and W6 (PM tool) do not overlap.
7. Whitespace (where they stop)
- Layer gap: the work after the roadmap is monthly guidance, or a human FDE team [site]. To own it we need one customer workflow where our agents do the work and a person approves it, run for 4+ weeks.
- Buyer gap: they start at 50 staff [site]. Under 50, and the price of a consultancy-style engagement, may be out of reach (price unknown). To own it we need small-firm buyers telling us they have the pain.
- Number gap: their case study has no money or time saved [site]. Their proof is a count of processes mapped. To own it we need one before/after number a finance person would sign.
- Price gap: cannot judge. Price is not public. Ask a buyer who has had a quote.
- Speed gap: first value is a plan in 9 to 12 days [site], not a result. To own it we need a measured result inside the same window, which we have not shown yet.
8. For the YC application
- Proven today: Marton's logs show his system already runs agents that do and check work every day, on his own business: about 5,600 jobs, each checked by a different AI, with about 160 failure records that each added an automatic check. Foaster's public results count interviews held and processes mapped, and its own plan ends with advice plus monthly guidance [site].
- Draft answer to "how are you different from Foaster?": DRAFT. "Foaster's agents find where AI should go and hand over a plan. Our system already runs agents that do the work and check it, every day, on our own business." Founders say it their own way. If pushed on "do you have a customer", the honest answer is no: nobody outside has run it yet (brief).
- Future claim (needs proof): "They tell a company where to start. We run the workflow and own the number." Needs one customer workflow run by our agents for 4+ weeks, with a before/after number.
- What we understand that they don't: a company brain should learn from checked results of real work, not only from interviews and expert comments.
needs proofon a customer; Marton's logs show it on his own work only (offer feedback lines fell from 44 to 18 as rules were added). - Copyability (M3): medium. They already sell Enterprise Deployment with Forward Deployed Engineers, so "doing the work" is on their menu [site]. But it is human-led and they are two people on an interview-and-map product. Agents that run and check the client's actual workflow is a different build (inference). They would want to go there: they say they will expand to "more and more consulting projects" [yc]. Not
hard, so M3 can not score above 3 against Foaster. - Why now (M4): None found. Do not use "agents can now interview and do the work": that is general AI progress.
- Do not claim:
- "Nobody does the full loop": Foaster's YC text claims the loop (interviews, map, roadmap, expert corrections back into the system) [yc].
- "Foaster has no customers": they have at least one case study (anonymous). We only know they name none.
- "We are the AI-native McKinsey" or any consultancy headline: that is their line [yc].
- "Our agents own a number" or "we deliver in days": no customer run yet.
- "Company brain" as the headline: their YC text uses the same idea and Within's does too.
9. Ideas worth taking
- Their 30-minute private interview by an agent: no prep, anonymised in the deliverable [site]. A cheap way to capture what people know, and a way to gather Voice-of-customer for a buyer pain.
- The case study shape: team size, people interviewed, days to result [site]. Use the same shape once we have a customer, but with a money or time number added.
- Public benchmarks that earn attention from the right people before any sales [yc]. Our version could be a public failure log or "what breaks when agents run 24/7" write-up, using Marton's logs.
10. Unknowns
- Whether any customer pays them, and what they pay. Ask a buyer who has had a call with them.
- Funding: $500k or $1M? Check later YC or press announcements.
- Whether the "expert corrections feed back" loop exists in practice. No log is public. Look at their X posts and a demo.
- Whether they sell in the UK or Europe. The founders have France links (inference from Répondia) but the site is silent.
- How they keep interview data safe. The how-it-works page says nothing; the FAQ mentions NDAs, DPAs and encryption [site].
- The date of the launch post and the case study (no dates shown).
Sources
- foaster.ai home page [site]
- How it works [site]
- Case study [site]
- About [site]
- Contact [site]
- Research [site]
- Blog index [blog]
- YC company page [yc]
- YC launch post [yc]
- Foaster profile on yespress.io [press]
- FundedIQ funding profile [press]
- Caplight profile [press]
- Job ads: none found (YC page shows 0 open roles) [jobs]
Technical detail
- Slug
- foaster
- Lens hash
- 179096c479
- Pass
- deep
- Stale
- no