AI Development × Project-Based Work
AI Development Agencies for Project-Based Clients (September 2026)
AI Development agencies with verified project-based client evidence — ranked by depth of documented work, then editorial quality.
Each listed agency documents project-based engagements on its own site or in a published case study; the model is never inferred from a service list.
Ranked AI Development agencies for project-based clients
Rankings updated · Newest review
Top 30 of 88 agencies with documented project-based work.
Dublin, Ireland
Client evidence: 20 documented clients overall
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.
Dublin, Ireland
Client evidence: 20 documented clients overall
Best for: Irish organizations already running Dynamics 365 or Power Platform that want Copilot and Azure AI advice from a Microsoft partner.
Berlin, Germany
Client evidence: 19 documented clients overall
Best for: German enterprises and federal or city administrations taking a machine-learning or LLM use case from feasibility to an operated system.
Preste
#4Paris, France
Client evidence: 17 documented clients overall
Best for: Hardware and industrial firms that need computer vision running on the device, not in the cloud, delivered as a 2–6 month build.
Lexunit
#5Budapest, Hungary
Client evidence: 13 documented clients overall
Best for: Industrial and legal-publishing operators buying a machine-vision or document-automation system to run on their own premises.
ontolux
#6Berlin, Germany
Client evidence: 13 documented clients overall
Best for: German public bodies, broadcasters and publishers that need tagging and search over large German-language text collections.
London, United Kingdom
Client evidence: 13 documented clients overall
Best for: Founders commissioning a first blockchain or AI product build on a startup budget who will check references before signing.
Madrid, Spain
Client evidence: 10 documented clients overall
Best for: Spanish utilities, public hospitals and large employers bringing a forecasting, clinical-data or Spanish-language modeling problem.
Munich, Germany
Client evidence: 9 documented clients overall
Best for: Enterprises adding an AI layer onto a data platform their own team will run afterward, in manufacturing, insurance or media.
Babel
#10Madrid, Spain
Client evidence: 9 documented clients overall
Best for: Spanish and Portuguese banks, insurers and public administrations wanting AI delivered by a large integrator with the data work around it.
Datatonic
#11London, United Kingdom
Client evidence: 8 documented clients overall
Best for: Enterprises standardizing ML delivery on Google Cloud or AWS, especially where a regulator has to be satisfied.
London, United Kingdom
Client evidence: 8 documented clients overall
Best for: Pharma, research and public-sector buyers with an NLP problem on unstructured documents that has to survive review.
Zühlke
#13Zurich, Switzerland
Client evidence: 8 documented clients overall
Best for: Regulated enterprises buying AI inside a system an engineering firm will also build, run and document.
Kortical
#14London, United Kingdom
Client evidence: 8 documented clients overall
Best for: Enterprises with a defined prediction or document-automation problem that want a model in production in weeks.
Hiflylabs
#15Budapest, Hungary
Client evidence: 8 documented clients overall
Best for: Banks, energy and manufacturing groups pairing an LLM or forecasting build with the data-platform work underneath it.
Lautmaler
#16Berlin, Germany
Client evidence: 8 documented clients overall
Best for: Enterprises putting voice or chat in front of real customers, where a wrong answer reaches the public.
Berlin, Germany
Client evidence: 7 documented clients overall
Best for: Product owners adding AI to an app that already has users, rather than starting from a model.
QED Software
#18Warsaw, Poland
Client evidence: 7 documented clients overall
Best for: Data-rich firms and startups with a detection, vision or forecasting problem suited to a research-led team.
Satalia
#19London, United Kingdom
Client evidence: 6 documented clients overall
Best for: Enterprises with a scheduling, routing or workforce-allocation problem big enough to justify operations-research specialists.
elunic
#20Munich, Germany
Client evidence: 6 documented clients overall
Best for: Manufacturers automating one inspection station or technical-document search on an existing line, not buying an open-ended AI program.
Datasparq
#21London, United Kingdom
Client evidence: 6 documented clients overall
Best for: Operational businesses with data already flowing that want a system built on it rather than a strategy deck.
Bismart
#22Barcelona, Spain
Client evidence: 6 documented clients overall
Best for: Organizations whose AI plans depend on fixing the data layer first, especially in healthcare, public services and tourism.
MS iHub
#23Ljubljana, Slovenia
Client evidence: 6 documented clients overall
Best for: Manufacturers and online retailers commissioning a first scoped AI feature or MVP from a small Ljubljana team.
Mercury Labs
#24London, United Kingdom
Client evidence: 6 documented clients overall
Best for: UK public bodies and publicly funded research programs building AI tools that keep expert reviewers in charge.
Unit8
#25Lausanne, Switzerland
Client evidence: 5 documented clients overall
Best for: Industrial and financial enterprises building on Palantir Foundry, or weighing whether they should.
Visium
#26Zurich, Switzerland
Client evidence: 5 documented clients overall
Best for: Pharma and life-sciences teams that need the method described in full when the client name cannot be.
Helsinki, Finland
Client evidence: 5 documented clients overall
Best for: Regulated Finnish and Nordic enterprises that need an AI system governed, certified and operated, not just delivered.
Ghent, Belgium
Client evidence: 5 documented clients overall
Best for: Enterprises adding AI to a product or support workflow they already run, with the delivery team embedded alongside their own.
Sparkbit
#29Warsaw, Poland
Client evidence: 5 documented clients overall
Best for: Early-stage product companies that need the ML core of their product built, tested and handed over with the IP.
craftworks
#30Vienna, Austria
Client evidence: 5 documented clients overall
Best for: Industrial operators who need a vision or maintenance system running on their own lines and, where required, on their own infrastructure.
About this list
A development project leaves something behind, and the documented work here sorts by what that is. Datatonic's Vodafone AI Booster, an 18-month build live in more than eight markets, left reusable MLOps templates released as open source. Fast Data Science's Clinical Trial Risk Tool for the Gates Foundation left a public deployment, an MIT-licensed codebase and a paper in Gates Open Research. Merantix Momentum's rail-noise-barrier detector for DZSF was delivered as an open-source plugin, its KRASS Optik recommender left an automated retraining loop running since 2021, and its Bundeskanzleramt proof of concept was handed over for integration. Preste's 17 project cards name client, stack and build time, from a Jetson pipeline inside Greenbig's recycling machine to a 120 FPS model for Tecbak, and no card says whether the system still runs. b.telligent's KUKA assistant publishes latency and nothing else, and Babel's GACM and ENAIRE pages still carry placeholder results.
The sorting question for a development project is what the client can run without you. Ask for the handover package: the retraining pipeline, the evaluation set, the monitoring, the documentation, and whether the model runs outside the firm's platform. Then ask whether it still runs, because a build time is not a production status: the curator asks Preste for references whose systems are running today, notes that KRASS and Boehringer Ingelheim run as open-ended Merantix Momentum engagements with retraining costs unpublished, and that Instituto de Ingeniería del Conocimiento's SERMAS and La Princesa work are pilots.
Intro by Gabor Kiss, curator · How we rank
Expert Insight
Why project-based fit matters for AI Development
A fixed scope protects both sides only when the acceptance criteria are written: ask for them before the rate, because a project without them becomes a retainer nobody agreed to.
The handover decides whether the project was an asset or a dependency: pipelines, evaluation sets and retraining procedures belong in the deliverables list.
The pilot-to-production gap is the main risk of project work in AI: the case studies here that describe operations and measured results are the ones that show an agency has crossed it.
Frequently asked questions
88 agencies in our directory combine verified project-based client evidence with documented AI Development work. The current top-ranked are Square Root Solutions, Storm Technology, Merantix Momentum — 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 €20–260 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.
Every agency here documents project-based work on its own site or in a published case study, describing the engagement structure rather than a service-list claim. We never infer the model from portfolio tone, so this list only contains agencies that operate project-based engagements deliberately.
18 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.