Generative AI × Startups
Generative AI Agencies for Startup Clients (October 2026)
Generative AI agencies with verified startup client evidence — ranked by depth of documented work, then editorial quality.
Each listed agency evidences at least 2 documented startup clients. Combined, the agencies below document 123 client engagements.
Ranked Generative AI agencies for startup clients
Rankings updated · Newest review
Dublin, Ireland
Client evidence: 20 documented clients overall · Early-stage
Best for: Irish and European SMEs and founders buying a whole product build, with AI as one layer inside it rather than the whole engagement.
London, United Kingdom
Client evidence: 13 documented clients overall · Early-stage
Best for: Founders commissioning a first blockchain or AI product build on a startup budget who will check references before signing.
Modulai
#3Stockholm, Sweden
Client evidence: 12 documented clients overall
Best for: Product teams that need a model shipped into production, from clinical retrieval to real-time vision on constrained hardware.
Stepwise
#4Warsaw, Poland
Client evidence: 11 documented clients overall · Early-stage
Best for: Early-stage B2B software companies that need a cloud and data engineering team, with AI as one part of the product build.
Tallinn, Estonia
Client evidence: 10 documented clients overall · Early-stage
Best for: Seed-stage founders who want an LLM feature or a data-extraction pipeline built into a product by the same team that ships the app.
InData Labs
Verified: the agency confirmed ownership from a company email address, and the listing passed editorial review#6Vilnius, Lithuania
Client evidence: 7 documented clients overall · Early-stage
Best for: Product teams with a defined forecasting, fraud or analytics problem who want an established ML team on fixed-price or dedicated terms.
Warsaw, Poland
Client evidence: 7 documented clients overall · Early-stage
Best for: Data-rich firms and startups with a detection, vision or forecasting problem suited to a research-led team.
Vstorm
#8Wrocław, Poland
Client evidence: 6 documented clients overall · Scale-ups
Best for: Mid-market operators putting an agent inside a product or a clinical workflow, where output has to be validated before it ships.
Lyon, France
Client evidence: 6 documented clients overall · Scale-ups
Best for: French mid-market firms adding a document-analysis or automation layer to a SaaS or internal platform they already operate.
Opinov8
#10London, United Kingdom
Client evidence: 6 documented clients overall · Early-stage
Best for: Teams adding cloud, data or modernization engineers who want AI coding tools applied to that delivery.
Tooploox
#11Wrocław, Poland
Client evidence: 5 documented clients overall · Scale-ups
Best for: Product teams needing an ML problem researched and built from scratch—no dataset, no proven approach—not an off-the-shelf integration.
Valletta, Malta
Client evidence: 5 documented clients overall · Early-stage
Best for: Small firms and startups that want one embedded engineer to wire an LLM or agent into their existing tools on a modest budget.
Rotterdam, Netherlands
Client evidence: 5 documented clients overall · Early-stage
Best for: Scale-ups adding an AI feature to a product they already run, where the model is bought and the surrounding app must be built.
Imobisoft
#14London, United Kingdom
Client evidence: 4 documented clients overall · Early-stage
Best for: Health and industrial companies taking a risk-scoring or compliance-reporting workflow from paper to a regulated digital platform.
Qemie
#15Frankfurt, Germany
Client evidence: 4 documented clients overall · Early-stage
Best for: Small B2B teams replacing spreadsheet-and-CRM sales admin with automated pipelines their own staff can run.
Altar.io
#16Lisbon, Portugal
Client evidence: 2 documented clients overall · Early-stage
Best for: Funded founders building a regulated fintech or commerce product who want engineering that carries them to the next raise.
About this list
Generative work at startup scale usually means one of two things: a feature that reads documents and returns structure, or a feature that writes something a user sees. This page leans to the first. Dashbouquet publishes the clearest model-selection record here, evaluating Llama 3, Mixtral, GPT-4 and VILA for BetterYou's therapist dashboard, alongside an extraction platform pulling menus from 10,000-plus venues in 12 languages at 100 GB a day. Yield Studio's Greensight module reads ESG documents and fills questionnaires inside Greenscope's platform, cutting report editing to under 30 minutes. Vstorm's Mixam order agent is the entry running at consumer scale—10,000-plus daily users and around 100,000 orders a month—and its clinical triage system, scored against 591 nurse-validated scenarios, publishes what still fails alongside what works. Tooploox and Modulai carry the research bench: a chief scientist and a head of R&D who are both professors in one case, a multi-agent retrieval system for clinical trial evidence built to stay traceable in the other. Lizard Global's generative exposure sits at the application layer around models the client already licenses. Valletta Software Development publishes the deepest self-hosting write-up here—a Qwen3-32B model on two on-premises GPUs serving about 42,000 requests a day for an insurance group, with a blind evaluation over 600 tasks—though that client, like the one behind its GPT-4o support assistant, is not named, and its named startup work is smaller.
Read the evidence lines before shortlisting, because two things get conflated here constantly. A documented model bake-off proves a team can choose; a published usage or accuracy figure proves the choice survived contact with users, and several of the generative engagements on this page publish the former without the latter. Imobisoft's documented delivery is risk scoring and compliance reporting rather than content generation, which is worth knowing before the first call. Ask each candidate for a generative feature in front of users today, and what it does when the model returns something wrong.
Intro by Gabor Kiss, curator · How we rank
Expert Insight
Why startup fit matters for Generative AI
Startup budgets buy scoped builds, not programs—the agencies here document work shipped in weeks to months, priced for companies that measure runway.
At small agencies the case-study authors are the delivery team, which removes the seniority lottery that makes large firms risky for small engagements.
Code ownership decides whether the engagement was an asset or a dependency: every documented build here describes a system the client runs, and that question belongs in your first call.
Frequently asked questions
16 agencies in our directory combine verified startup client evidence with documented Generative AI work. The current top-ranked are Square Root Solutions, Pixelette Technologies, Modulai — ordered by depth of documented client evidence, then our editorial scoring (portfolio quality, credibility, completeness); placement is never paid.
Published rates across this page's agencies run €20–130 per hour (median ~€65). Project totals depend on scope — the rate index at /rates breaks the computed bands down by region, country and team size.
Each listed agency has at least two documented, named startup clients — published case studies or engagement descriptions our research actually read, with source URLs stored per client. Logo walls without published evidence carry no weight, which is what separates this list from self-declared directories.
6 of the agencies on this page document an embedded / team-extension working model. See the team-extension page for the full verified list, or check the engagement chips on individual profiles.