Documented engagement model
AI Team Extension & Staff Augmentation Agencies in Europe (September 2026)
Agencies that document team extension engagements on their own sites or in published case studies, ranked by portfolio quality and credibility.
Each listed agency documents team extension engagements on its own site or in a published case study; the model is never inferred from a service list.
Top picks
- 1. Satalia — London · 6 documented clients
- 2. Twistag — Lisbon · €45-85/hr · 2 documented clients
- 3. Ultra Tendency — Berlin · 3 documented clients
Documented clients include
Gogo · voestalpine · European Central Bank · Melexis · AXA · Lely
Ranked agencies for team extension engagements
Rankings updated · Newest review
Top 30 of 35 agencies with documented team extension work.
Satalia
#1London, 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.
Twistag
#2Lisbon, Portugal
Client evidence: 2 documented clients overall
Best for: Operators with a document or workflow bottleneck that needs an agent auditable enough for finance or compliance to delegate to.
Berlin, Germany
Client evidence: 3 documented clients overall
Best for: Institutions buying the data platform underneath analytics, where uptime and migration risk matter more than model accuracy.
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.
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.
Vienna, 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.
Dublin, Ireland
Client evidence: 6 documented clients overall
Best for: Insurers, brokers and public bodies automating document-heavy back-office work with an ISO 42001-certified delivery partner.
10Clouds
#8Warsaw, Poland
Client evidence: 5 documented clients overall
Best for: Banks, insurers and credit funds automating a defined back-office or sales workflow on a platform already run in production.
Budapest, 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.
Xomnia
#10Amsterdam, Netherlands
Client evidence: 3 documented clients overall
Best for: Dutch enterprises whose model problem turns out to be a data-platform problem.
Neurons Lab
#11London, United Kingdom
Client evidence: 2 documented clients overall
Best for: Financial institutions that need agentic AI shipped inside regulatory constraints—training through production.
DEUS
#12Amsterdam, Netherlands
Client evidence: 3 documented clients overall
Best for: Enterprises putting voice or conversational AI in front of their own customers.
N-iX
#13London, United Kingdom
Client evidence: 4 documented clients overall
Best for: Enterprises with large in-house engineering teams that want AI adoption or ML work measured against an agreed baseline.
Boldare
#14Amsterdam, Netherlands
Client evidence: 3 documented clients overall
Best for: Consumer-facing businesses adding an OpenAI-backed chatbot or generated content to a web platform Boldare builds or runs.
Transparity
#15London, United Kingdom
Client evidence: 8 documented clients overall
Best for: UK enterprises, charities and public bodies already on Microsoft 365 and Azure that want Copilot adopted and governed.
Marionete
#16London, United Kingdom
Client evidence: 6 documented clients overall
Best for: Banks and insurers that need the data and MLOps platform under their AI built to regulated-industry standards.
LANARS
#17Oslo, Norway
Client evidence: 2 documented clients overall
Best for: Hardware startups that need firmware, apps and cloud built by one team, with model work brought in alongside.
Datamole
#18Prague, Czech Republic
Client evidence: 4 documented clients overall
Best for: Equipment manufacturers turning machine telemetry into a product feature their own service or R&D teams will keep running.
Sonalake
#19Dublin, Ireland
Client evidence: 4 documented clients overall
Best for: Telecom operators and data-heavy product companies adding forecasting or anomaly detection to a platform they already run.
Valletta, Malta
Client evidence: 5 documented clients overall
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.
Softblues
#21London, United Kingdom
Client evidence: 5 documented clients overall
Best for: Early-stage B2B product teams and smaller UK or Irish firms scoping a first voice agent, agent pipeline or Claude rollout.
Brights
#22Warsaw, Poland
Client evidence: 5 documented clients overall
Best for: Product companies adding an AI assistant or agent feature to a web or mobile platform built with an outsourced team.
Lexunit
#23Budapest, 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.
Berlin, Germany
Client evidence: 3 documented clients overall
Best for: Founders putting a model inside a consumer health, sports or wellbeing product.
peoly
#25Prague, Czech Republic
Client evidence: 4 documented clients overall
Best for: Manufacturers and platform teams buying camera-side vision, or an ML team embedded alongside engineers they already have.
Vention
#26London, United Kingdom
Client evidence: 10 documented clients overall
Best for: Venture-backed product companies adding engineers to an existing team, with AI as one feature inside the software work.
Old St Labs
#27London, United Kingdom
Client evidence: 29 documented clients overall
Best for: Founders and small UK firms building a first web or mobile product with an LLM feature inside it.
KVL
#28Rotterdam, Netherlands
Client evidence: 7 documented clients overall
Best for: Dutch insurers, pension providers and care organizations building a Microsoft data platform and an in-house data team.
Stepwise
#29Warsaw, Poland
Client evidence: 11 documented clients overall
Best for: Early-stage B2B software companies that need a cloud and data engineering team, with AI as one part of the product build.
R-Szoft
#30Budapest, Hungary
Client evidence: 3 documented clients overall
Best for: Hungarian companies that need a software house for business-system builds, with on-premise AI document automation added.
About this list
Sometimes the right engagement is not a project but people: machine-learning engineers, data engineers or applied scientists working inside your organization for months or years, on your backlog and under your product leadership. Every agency on this page documents that working model on its own site or in a published case study, and the chip is never inferred from portfolio tone. The evidence bar is deliberately different from the rest of this register, because embedded work only holds up when the agency has built its operations around it: stable allocation, people senior enough to work without agency-side supervision, and pricing that survives month twelve.
The documented engagements show what the model looks like at scale. N-iX has run a dedicated data team for the in-flight connectivity operator Gogo since January 2017, covering big data, data science and BI operations on AWS. craftworks' data scientists have worked inside voestalpine High Performance Metals' own data teams at its production sites since September 2020, quoted by the client's Head of Data Science and AI. Ultra Tendency states that it is wholly responsible for the architecture, implementation and testing of SPACE, the statistical production platform the European Central Bank runs across the eurozone's national central banks. In The Pocket describes a blended-team modernization of Melexis' chip-test software, and Marionete's MLOps factory for AXA was adopted across the insurer's business units.
When does extension beat a scoped build? When the work is continuous rather than shaped: a data platform that needs a resident owner, a machine-learning team that is two engineers short, a scale-up that cannot hire fast enough. Central European studios on this page, in Prague, Warsaw and Budapest, built much of their model on exactly this engagement shape. Ask three things before signing: who exactly is allocated, what the replacement policy is when the fit is wrong, and what the minimum commitment is. Scores rank portfolio quality and credibility; placement is never sold.
Intro by Gabor Kiss, curator · How we rank
Expert Insight
Why the engagement model matters
Embedded work is an operating model, not a service line: an agency that documents it has already solved allocation, seniority and continuity, the three things that break staff augmentation.
The chip's evidence bar is self-description or a documented case study, so every listing here chose this model deliberately rather than matching a query.
Extension trades scoping overhead for velocity: no re-briefing, no phase gates, and the engineer is in your standup by week two.
Nearshore studios on this page built their model on this engagement shape; compare their published rates with a fully loaded local hire before deciding.
Frequently asked questions
An explicitly documented embedded working model: the agency describes joining or embedding with client teams on its own site, or a published case study documents the engagement structure, such as a dedicated data team run for years inside a client. It is never inferred from portfolio tone. The chip exists only where the agency itself evidences the model, a stricter bar than a 'dedicated teams' line in a service list.
Monthly pricing derives from the hourly bands on each profile and on our rate index. Against a senior hire's fully loaded cost plus months of recruiting, extension wins on speed and flexibility; hiring tends to win past the eighteen-month mark. Most teams use extension to bridge exactly that gap, and the profiles here show which agencies publish a rate at all.
A retainer buys ongoing agency capacity on the agency's side; team extension puts named engineers inside your team, working your backlog under your leadership. Extension suits continuous product or platform work; retainers suit recurring but bounded needs such as model monitoring, advisory or a stream of scoped deliverables. Some agencies document both models on their profiles, and the retainer page lists the ones that document that model.
Agencies operating this model typically staff within two to four weeks. The practical questions to ask are seniority guarantees, the replacement policy if the fit is wrong, and the minimum commitment, with three months common. That is still months faster than a senior machine-learning hire in any European market.