Top IT Staff Augmentation Companies for AI/ML Projects (2026 Edition)

What AI staff augmentation is, and when it fits

In staff augmentation, a provider supplies engineers who work as part of your team. You keep product ownership, technical direction and the decisions about what the AI system should do. The provider recruits, employs and replaces people, and usually handles payroll, equipment and retention.

It fits best when:

• You already have a product and an engineering team, and need specific AI skills inside it.
• You know what you want to build, at least for the next few months, and someone on your side can lead the work.
• You need people faster than your own hiring can deliver, or for a period that does not justify permanent hires.

It fits less well when you are not sure yet where AI creates value. In that case, start with an assessment that maps the work and prices the opportunities before you add engineers; otherwise you pay a team to build something nobody uses. Our AI opportunity and business case work exists for that step.

The alternatives are an AI consultancy that delivers a defined project end to end, freelance platforms for short, well-scoped tasks, and permanent hiring for roles that are core to your product long term. Many companies combine them: a consultancy or an assessment to decide, augmented engineers to build, permanent hires to own the result.

The AI roles companies hire in 2026

The last row is the one most companies underestimate. In production AI systems, the model call is a small part of the code. Most of the effort goes into data access, integrations, permissions, error handling and monitoring, which is why strong backend engineers matter as much as AI specialists.

How we chose and re-checked these companies

We first published this list in 2025. For this edition we re-checked every company on its own website and, where available, its Clutch profile in October 2026: what it offers today, where it is based, and how it describes its AI work. Ratings and rate bands are given as the sources showed them on the day we checked; they change, so look again before you decide.

Changes in this edition:

• Turing has been removed. Its website now focuses on AI training data and evaluation work for AI labs and on enterprise AI, and its developer hiring page no longer describes placing developers in client teams.
• Addepto, a Warsaw-based AI and data company with a model in which its team works inside the client's team, has been added in its place.
• DO OK is described by what its website publishes today. We previously called it ethics-centric; its current site does not describe a specific responsible AI framework.
• Intelvision's entry now uses only figures from our own website, and says plainly what our AI evidence covers and what it does not.

The ten companies at a glance

Intelvision | Senior engineers inside AI product teams

Intelvision is a European software engineering company that builds software, implements AI in real business workflows and places senior engineers inside client teams. For AI projects, two kinds of evidence are published on our site, and we keep them apart.

Senior engineers in AI product teams. This is talent delivery: our engineers work inside AI products our clients own, and we do not present those products as AI systems we designed.

• A Swiss consultancy building an AI product that connects Microsoft Teams and Jira, moderates stand-ups and turns delivery data into progress updates and risks added a senior back-end developer who started three business days after the request. The stack included Python, Django, the Microsoft Graph API, the Jira API and Azure (case study).
• A global SportsTech company whose AI platform turns live sports broadcasts into automated highlights uses Intelvision engineers across two product streams, video-editing tooling and billing and platform services, with a dedicated technical project manager owning coordination. Time to engage was under two weeks (case study).

AI built for clients. For a German EdTech scale-up, our team delivered AI speech-to-text in two to three weeks as part of a wider platform modernisation (case study). For a German single-family office, two AI developers, a business analyst and a consulting lead built no-code automation agents that freed up about three to four hours a day for the principal (case study).

How it works: first candidates in 2 to 4 days; 12 to 18 days from brief to an engineer on the project across published engagements; full-time specialists from €6,500 to €9,000 a month by seniority, part-time from 40 hours a month, no upfront fees, and a free replacement if a developer does not meet expectations in the first month (pricing). Overall we report more than 100 software products delivered, 89% team retention and a 5.0 rating across 14 verified reviews on Clutch.

If you need a partner with a long list of completed AI deployments of its own, ask us early, and we will tell you plainly whether we are that partner. For engineers who join your team to find and ship AI use cases on site, see our forward deployed AI engineers; to hire individual specialists, see hire AI engineers.

STX Next | Python, AI and data engineering at scale

STX Next describes itself as a Python-native team for more than 20 years, with more than 450 specialists and more than 1,000 projects. Its AI and ML services include AI development, machine learning, enterprise RAG, OCR and intelligent document processing, and AI workshops, with work on Amazon Bedrock, Microsoft Copilot Studio and AI Foundry, and n8n. Its data practice covers data engineering and lakehouses on Snowflake, Databricks, Apache Iceberg and Microsoft Fabric. Headquartered in Poznań, it also lists a delivery centre in Mérida, Mexico, and offices in London, Eschborn and the US (STX Next). Clutch lists it as founded in 2005, with a 4.7 rating from 103 reviews and rates of $50 to $99 an hour (Clutch).

Best fit: Companies with Python stacks that need AI, document processing and data platform engineers, including complete squads with QA and product roles.

Netguru | AI with product design and commerce focus

Netguru combines engineering, product design and strategy, and today positions itself as "AI-native commerce". Its AI offerings include AI development, AI agents, machine learning, AI commerce chatbots, and search and pricing engines, alongside cloud, data and platform engineering. It reports more than 400 people and more than 2,500 projects, from its base in Poznań (Netguru). On Clutch it holds a 4.8 rating from 73 reviews, with rates of $50 to $99 an hour (Clutch).

Best fit: Retail, marketplace and consumer products where AI features such as search, recommendations and chat must come with strong user experience design.

Proxify | A vetted network for fast individual hires

Proxify, founded in 2018 and based in Stockholm, connects companies with individual developers from a network of more than 6,000 professionals in over 35 countries. It says that only 1% of applicants, from over 20,000 applications each month, are accepted into the network. Its Clutch profile lists AI development, BI and big data, cloud consulting and custom software development as service lines, with rates of $25 to $49 an hour; no client reviews are published there yet (Clutch).

Best fit: Startups and scale-ups that need one or two AI-capable developers quickly and can manage and direct them in-house.

Lemon.io | Startup-focused matching in about a day

Lemon.io promises a matching time of about 24 hours and states an acceptance rate of 1.2%, with every candidate personally interviewed by a senior engineer before the client meets them. Its roles include AI engineers, LLM developers, ML engineers and MLOps engineers, sourced mainly from Europe, Latin America, the US and Canada. Engagements have a 160-hour minimum and a prepaid model, with no upfront recruitment fees (Lemon.io). Clutch lists it as founded in 2015 in Kyiv, with a 4.9 rating from 4 reviews and rates of $25 to $49 an hour (Clutch).

Best fit: Early-stage startups that need to test an AI feature quickly with a lean budget and a technical founder who can lead the work.

Andela | AI engineers in blended teams, plus training

Andela now calls itself "the human layer powering production AI". It places AI engineers into client systems in three tiers, builders for AI applications, integrators for AI systems and infrastructure, and scalers for AI platforms in production, and supports data readiness, fine-tuning, enterprise retrieval and production deployment. It reports 17,000 certified AI-native engineers and more than 200,000 people trained on emerging technologies, and it also sells training for clients' own engineering teams. Its site shows a G2 rating of 4.7 from 329 reviews (Andela).

Best fit: Large companies that need AI engineering capacity across time zones and want to upskill their own teams at the same time.

SoftKraft | Small teams for AI and data-heavy products

SoftKraft, based in Bielsko-Biała, Poland, offers custom AI solutions, AI agent development, data engineering and staff augmentation. It reports more than 50 IT professionals, nine years of average staff experience, more than 60 clients and ten days to launch a team (SoftKraft). Clutch lists it as founded in 2015, with a 4.9 rating from 24 reviews and rates of $50 to $99 an hour (Clutch).

Best fit: B2B SaaS and analytics companies that need a few senior engineers who can handle both the data foundation and the AI features built on it.

Dev.Pro | Outstaffing and dedicated teams at scale

Dev.Pro builds teams for clients through outstaffing and outsourced development, with more than 1,000 experts in more than 55 countries, ISO/IEC 27001 certification, and about 80% of specialists at senior or intermediate level. AI solutions are one of its domains, with TensorFlow, PyTorch, scikit-learn, Keras and OpenAI in its stack (Dev.Pro). Clutch lists headquarters in Las Vegas and locations including Kyiv, Lviv, Wrocław, São Paulo, Vila Nova de Gaia in Portugal and Plovdiv in Bulgaria, founded in 2011, with a 4.9 rating from 7 reviews and rates of $25 to $49 an hour (Clutch).

Best fit: Mid-market and enterprise companies that need larger teams combining AI, backend, DevOps and QA, with a security certification in place.

DO OK | Engineers on demand for AI integration

DO OK, based in Wrocław and operating since 2010, describes itself as AI-native and integrates AI for predictive analytics, recommendation engines and automation, using tools such as Python, LangChain, Pandas and TensorFlow. It offers engineers on demand and dedicated teams, reports 50 experts and 150 clients, builds digital health and wellbeing platforms among other products, and displays ISO 9001, 14001, 27001 and 27018 certification logos (DO OK). Clutch lists offices in Wrocław, Zielona Góra and Tallinn, a 4.8 rating from 42 reviews and rates of $50 to $99 an hour (Clutch).

Best fit: Product companies, particularly in digital health, that want to add AI features to existing web and mobile products with a small embedded team.

Addepto | AI and data specialists who work with your team

Addepto, based in Warsaw, focuses on AI consulting, machine learning and MLOps, generative AI and data engineering. One of its cooperation models is collaborative: its team joins the client's team to build a solution aligned with the business goals. It also publishes a production-first framework for AI projects that it says is based on more than 100 AI projects (Addepto). Clutch lists it as founded in 2017, with a second location in Los Angeles, a 4.9 rating from 19 reviews and rates of $50 to $99 an hour (Clutch).

Best fit: Companies that need AI specialists with consulting experience alongside their own engineers, from proof of concept to production.

What AI staff augmentation costs

Rates for AI work vary widely with seniority and provider, while outsourcing rates overall have been easing. Accelerance reported in November 2025 that European outsourcing rates ranged from US$31 to $39 an hour for junior developers and US$64 to $76 for senior developers, down 4.4% year on year (Accelerance). The providers in this list show Clutch rate bands from $25 to $49 up to $50 to $99 an hour; Intelvision's published price for a full-time specialist is €6,500 to €9,000 a month.

Three things move the real cost more than the rate:

1. Seniority. A senior AI engineer who has shipped retrieval or agents to production can save months of trial and error. Paying junior rates for that work is rarely a saving.
2. Model and infrastructure costs. API calls, GPUs, vector databases and monitoring can exceed the engineering cost of a busy AI feature. Ask candidates how they control them.
3. Time to production. The cost of a team that delivers a working feature in two months is lower than a cheaper team that is still experimenting after six.

How to vet AI engineers

AI is the area where the gap between a good demo and a production system is widest, so CVs and keyword lists tell you little. Practical checks work better:

• Ask for a production story. What did they ship, how many users used it, what broke, and how did they find out? People who have run AI in production talk about failures, costs and monitoring.
• Test evaluation thinking. How would they measure whether a retrieval or agent feature gives correct answers? Look for test sets, automated evaluations and human review, not "it looked good in testing".
• Test data skills. Most AI problems are data problems. Give them a messy dataset or document set and ask how they would prepare it.
• Test security awareness. Prompt injection, data leakage between users, permissions at retrieval time, logging of personal data. A senior candidate raises these without being asked.
• Check how they use AI coding tools. In the 2025 Stack Overflow Developer Survey, 84% of developers used or planned to use AI tools, yet 46% said they distrust the accuracy of the output (Stack Overflow). You want engineers who use the tools and verify the output.
• Prefer seniority for ownership. Stanford's AI Index 2026 notes that employment for software developers aged 22 to 25 has fallen nearly 20% from 2024 (Stanford HAI). Routine coding is increasingly assisted; judgement, architecture and ownership are what you pay a premium for.

Our article on where to find and hire Claude certified architects and developers goes deeper into vetting engineers for LLM-based systems.

Governance, security and the EU AI Act

Augmented engineers work under your rules, so the rules need to exist. Before they start:

• Data access. Decide which data the team may use, where models run and whether any data may leave your environment. Prefer anonymised or synthetic data for development.
• Contracts. Intellectual property transfer for code, prompts, evaluation sets and fine-tuned models; a GDPR processing agreement where personal data is involved; no use of your data to train third-party models.
• Tooling policy. Which AI coding assistants and model providers are approved, with which data retention settings.
• Documentation. Record data sources, evaluation results, known limitations and human oversight for every AI feature. It is good engineering and it prepares you for regulation.

The EU AI Act sets the timeline. Prohibited practices and AI literacy obligations have applied since 2 February 2025, and obligations for general-purpose AI models since 2 August 2025. The Act became generally applicable on 2 August 2026, including transparency rules, while the AI Omnibus moved the rules for high-risk systems in sensitive use cases to 2 December 2027 and for AI in regulated products to 2 August 2028 (European Commission). If your AI system could fall into a high-risk category, such as credit decisions, hiring or education, choose engineers who have built documented, auditable systems before.

Common mistakes when augmenting a team for AI

• Hiring AI specialists before the data is ready. Without data engineering, AI engineers spend their first months cleaning data at AI rates.
• No owner on your side. Augmented engineers need someone who decides what the AI should do and when it is good enough.
• Demo-driven success criteria. Agree on measurable quality targets and an evaluation set before building, not after.
• Ignoring running costs. A feature that costs more per use than it earns is a failure, however clever the model.
• Treating AI as a separate team. AI features live inside products. Integrate the augmented engineers with your product and backend teams from day one.
• Choosing on rate alone. The spread between a team that ships and one that experiments is much larger than the spread between rate bands.

Summary: scale AI and ML with the right team

From LLM features and agents to forecasting and computer vision, demand for AI engineering keeps growing faster than companies can hire. IT staff augmentation gives you access to AI engineers, ML engineers, data engineers and MLOps specialists within weeks instead of quarters, while you keep ownership of the product.

The companies in this list cover different needs: Python and data depth (STX Next), AI with product design (Netguru), fast individual matching (Proxify, Lemon.io), global scale and training (Andela), small senior AI and data teams (SoftKraft, Addepto, DO OK) and larger dedicated teams (Dev.Pro). Intelvision fits teams that want senior engineers working inside their own process quickly.

Whichever partner you consider, vet for production experience, settle data and governance rules before the first commit, and measure the team on working features, not activity.