In March, I sat in on a vendor review for a mid-sized logistics company. Four shops pitched. Three demoed a demand model on the client’s own CSV export and landed on the same accuracy slide.
The fourth refused to quote a number until someone explained how the data would arrive in production and who would watch the model after launch.
That fourth shop had it right. Picking a machine learning development company in 2026 is mostly a decision about who pays the retraining bill. The demo is table stakes.
I call it the retraining bill: the cost that shows up around month seven, when the data has shifted, the engineer who built the pipeline has rolled off, and the model that cleared acceptance testing is quietly wrong on a fifth of its predictions.
It is the biggest line item in most ML projects, and the one RFPs never mention. This list is ranked with that bill in mind.
The retraining bill is the line item nobody puts in the RFP
Adoption is not the problem anymore. The Stanford HAI 2026 AI Index reports that generative AI reached 53% adoption within three years, faster than the internet or personal computers at the same stage.
Return is the problem. McKinsey’s State of AI survey, published August 25, 2026, found that 44% of respondents now say AI is scaling across their enterprise, up from 38% a year earlier, while the share attributing any EBIT impact to AI sat flat at 37%.
High performers were still about 6%, and nearly three-quarters of them had redesigned workflows around AI, against a quarter of everyone else.
More models reach production, and the money is not following. My reading is that the gap sits in the months after launch, in monitoring, retraining, and the plumbing that connects a prediction to a decision. That framing is my inference, not something the survey claims.
A vendor who only sells the build phase is selling you the cheap half.
“Foundation models made custom ML obsolete” is a procurement shortcut, not an engineering fact
The version I hear most often:
“Foundation models made custom ML obsolete. You call an API now. Nobody should be paying a vendor to train a demand forecasting model from scratch in 2026.”
Try that on a SKU-level forecast across 40,000 products, a credit model that must explain each rejection to a regulator, or a defect detector on an edge camera with a 30 millisecond budget. Tabular problems still belong to gradient boosting and careful feature engineering.
Where foundation models win, in documents, chat, and summarization, the custom work moved rather than vanished, into retrieval pipelines, evaluation suites, fine-tuning, and guardrails.
The retraining bill got new names: prompt drift, deprecation notices, token spend. About 20% of McKinsey’s 2026 respondents said AI operating costs, tokens included, had constrained their use.
How I evaluated these seven companies?
I fetched every company’s site in October 2026, checked for dated activity this year, and read case studies for numbers rather than adjectives. The criteria:
- Live in 2026, with dated posts, cases, or announcements from this year
- Mid-tier, 50 to 750 people, so you talk to whoever does the work
- A named ML specialty with evidence: papers, products, or cases with figures
- Post-launch ownership as a line item: MLOps, monitoring, retraining
- Published pricing or timelines, or a willingness to state them
One more filter came from an MIT Sloan study dated September 9, 2026. Researchers followed two organizations with similar AI readiness for two years. A medical center ended up with 141 organization-wide AI solutions in use. A law firm ended up with three.
The difference was the support structure around experimentation, not the technology. Same test for vendors: what do they put around the model once it leaves the notebook?
1. LITSLINK: machine learning development services with the retraining already priced in
LITSLINK sells machine learning development services as a four-stage engagement: discovery and data assessment, model development and validation, deployment and MLOps, then monitoring and retraining on a monthly, quarterly, or drift-triggered cadence, each stage closing on a written deliverable.
The stack is Python with TensorFlow, PyTorch, scikit-learn, and XGBoost on AWS SageMaker, Azure Machine Learning, or Google Vertex AI, with MLflow and Kubernetes for serving. It quotes 10 to 12 weeks to a first production pilot when the data is in reasonable shape.
Founded in 2014 and headquartered in Palo Alto, with engineering in Ukraine and Europe, the firm claims 300-plus engineers and a 4.8 Clutch rating across 78 reviews. The proof is in the cases.
A demand forecasting model for a top US retailer was built in 7 weeks, produces dated product-level predictions at 91% accuracy, and is credited with revenue up 34% and storage capacity use up 65%.
A credit scoring platform for a US lender shipped three models behind one AWS REST API in four months, with a cutoff slider that splits a 2,000-application test batch into approve and reject pools.
Pricing is public: consulting from $15,000, a dedicated ML team from $8,000 per specialist per month, full-cycle work from $60,000 per release.
Clients own the weights and pipelines, and the site says the team will recommend a rules engine when ML is the wrong tool, which is rare.
2. Addepto: the data platform shop that treats MLOps as its own service line
Addepto is headquartered in Warsaw and was founded around 2018 (CB Insights says 2018, one trade profile says 2017). Atlanta-based KMS Technology acquired it in December 2025, and it now operates as part of KMS under its own brand, with 97% of staff described as AI engineers.
The pitch is lakehouse-first: Databricks consulting, data engineering, MLOps consulting as a standalone offering, computer vision, and generative AI.
Two in-house products back that up: ContextClue, for industrial engineering documents, and ContextCheck, an open-source tool for evaluating RAG chatbots. Client logos lean automotive: Continental, Porsche, Volvo, BMW, ABB.
Fit: manufacturers and automotive suppliers already on Databricks with models stuck in notebooks. The KMS deal probably adds US-based QA and delivery capacity to a Polish engineering core. That is my inference, not something either company claims, so ask how the integration affects your account team.
3. Tooploox: computer vision from people who still publish
Tooploox is a Wrocław company, founded in 2012 by most accounts (one source says 2013), and since June 2025 a Solvd Inc. company, with merger plans for Tooploox Research and Tooploox AI linked on its site.
Its edge is research output you can check. The team presented GEPAR3D, a dental imaging method, at MICCAI 2025, had continual learning and multi-exit model work at ICML 2025, and posted in February 2026 about a NeurIPS-linked result on 1,000-layer contrastive reinforcement learning networks.
Client work matches: an ML services case for eBay, digital histopathology for Virtum, augmented reality training for healthcare staff with ETH Zurich, and stereoscopic depth perception for autonomous vehicles with Lights.
Fit: medical imaging, autonomous systems, and any product where the model is the product and an off-the-shelf detector will not clear the bar. For a tabular churn model you are overpaying here.
4. DataRoot Labs: an R&D center for physical AI and hard perception problems
DataRoot Labs was founded in 2016 and runs its R&D office in Kyiv with a representative office in Delaware. It calls itself an independent AI R&D center with three labs.
The Deep Learning Lab covers LLM, VLM, and VLA training, robotics and reinforcement learning, and multisensor perception fusing RGB, thermal, acoustic, and LiDAR data.
The Engineering Lab handles production MLOps and serving, and the Agentic Lab builds voice agents and retrieval.
Engagement comes as a research partnership, product co-building, or a dedicated R&D team. Named work includes Outrider, which builds autonomous yard trucks, and Moxie by Embodied, a companion robot. In December 2025 the company opened the DRL AI Space at Kyiv Polytechnic Institute.
Fit: robotics, sensor-heavy industrial products, and founders who need a research partner rather than a feature factory. Ask about continuity planning, as with any Ukraine-based team in 2026. The answer is usually well rehearsed.
5. Neurons Lab: production AI for regulated finance, and little else
Neurons Lab was co-founded in London in 2019 by Oleksandr Honchar, who still serves as CTO. The firm lists 50-plus AI engineers and architects across Europe and more than 100 AI implementations since 2019. It holds the AWS AI Competency in Agentic AI.
The focus is narrow on purpose: wealth management, banking, private equity, and insurance. Public work includes an executive enablement program for 50-plus senior leaders at HSBC, an LLM-based marketing content system for Visa across nine-plus markets, and an agentic assistant for relationship managers at an Asian bank.
The service menu has three items: custom AI agents, training and enablement, and continuous AI delivery, the name for the retainer that keeps the thing alive after launch.
Fit: banks and insurers that need model risk controls from day one. Not a fit for computer vision or industrial work, and the company does not pretend otherwise.
6. ScienceSoft: the vendor that writes the retraining bill into the price list
ScienceSoft dates to 1989 and is headquartered in McKinney, Texas. It claims 750-plus consultants and engineers and 4,300-plus delivered projects, with image analysis since 2013 and an AWS partnership since 2017.
What earns it a place here is candor about cost. The ML services page publishes ranges: a standalone ML component at $30,000 to $200,000, an end-to-end solution at $200,000 to $600,000, and annual support at 15% to 25% of initial development cost, which it says can reach 70% of total cost of ownership.
That last figure is the retraining bill written down by a vendor. Very few do it. Cases include a lung cancer detection app for bioAffinity Technologies delivered in two months and AI battery inspection for Cerulean over six months.
Fit: regulated mid-market enterprises in healthcare, insurance, or lending that want a US-contracted vendor with long tenure and written estimates. Not a research shop, so ask who lands on your ML team.
7. MobiDev: machine learning inside a product, where the app is most of the job
MobiDev was founded in 2009, lists Norcross, Georgia as its first US address, and keeps engineering in Łódź and Chernivtsi. It cites 8-plus years in AI consulting and says 91% of its engineers are senior or mid-level.
The ML specialty is vision for sports and fitness. The company sells human pose estimation as a named service, and a 2026 case for BeONE Sports describes an iOS app with offline video analysis that reached 400-plus athletes and 100-plus coaches within six months.
On the forecasting side, a 2026 case for ComCash moved a legacy point-of-sale system to a cloud retail ERP with AI sales forecasting, reporting 15% higher upsell revenue and a 20% higher cross-sell rate.
Fit: product companies that need a model embedded in a mobile or web app, where the model is 30% of the work and shipping is the rest. For a pure data science bench, look at entries one, three, or four.
How the seven compare on the things that matter after launch?
| Company | HQ | Founded | ML specialty | Post-launch offer | Best fit |
|---|---|---|---|---|---|
| LITSLINK | Palo Alto, CA | 2014 | Forecasting, credit scoring, NLP, CV | Retraining stage with drift triggers | Fixed estimate, fast pilot |
| Addepto | Warsaw, Poland | ~2018 | Data platforms, MLOps, CV | MLOps consulting line | Industrial teams on Databricks |
| Tooploox | Wrocław, Poland | 2012 | Computer vision, research | Project-based | Model-is-the-product work |
| DataRoot Labs | Kyiv, Ukraine | 2016 | Robotics, perception, agents | Engineering Lab for MLOps | Physical AI, R&D partnerships |
| Neurons Lab | London, UK | 2019 | Agentic AI for finance | Continuous delivery retainer | Banks and insurers |
| ScienceSoft | McKinney, TX | 1989 | Enterprise ML, CV, NLP | Support at 15% to 25% of build | Regulated mid-market |
| MobiDev | Norcross, GA | 2009 | Pose estimation, in-app forecasting | Product team continuity | ML inside apps |
One thing the table hides: Addepto and Tooploox were both acquired in 2025, so their offers will move.
How to shortlist a machine learning vendor in six steps?
- Write the retraining bill into the RFP before anyone demos. Ask each vendor to price year two, not just the build: monitoring, drift alerts, scheduled retraining, and the engineer who answers at 3 a.m. No line for this means they expect you to pay later at a worse rate.
- Ask for a monitoring dashboard from a past project, not a screenshot of one. A shop that has run models in production can show accuracy and latency over time, client name blurred. A shop that has only built models shows you a Grafana template.
- Make the vendor name a baseline before they name a model. The right answer to “what would you build” starts with checking whether a simple rule or a linear model clears the bar. LITSLINK says this on its services page. Anyone who jumps straight to a deep network for a tabular problem is selling complexity.
- Check who owns the weights, the training pipeline, and the feature store. Model files are nearly worthless without the pipeline that produced them. Get all three in the contract, plus a handover session with your own engineers.
- Score the handover plan over the headcount slide. Every firm here claims senior engineers. Few describe what happens when the engagement ends. Ask for a handover document from a previous project. Its specificity tells you more than any case study.
- Run a 10- to 12-week pilot tied to one business metric. Not accuracy. Revenue, write-offs, hours saved, stockouts avoided. Pick one, agree on measurement before the pilot starts, and make phase two conditional on it.
Where machine learning vendors are heading after 2026?
Consolidation has started. Two of the seven firms here changed hands in 2025, and I expect more mid-tier ML shops to be bought by larger engineering firms that need an AI bench. Expect account teams that shuffle mid-project.
The second shift is that agents are absorbing classic ML. McKinsey’s 2026 survey found 40% of large organizations now scaling AI agents, up from 27% the year before. A demand model that used to end at a forecast now feeds an agent that drafts the purchase order.
The retraining bill grows there, because the model drifts, the agent’s evaluation set drifts, and both need maintaining together.
The vendors who matter in 2027 are the ones already treating evaluation suites as production infrastructure, and you can tell by reading service pages rather than award badges.
Hire for month seven, and the demo will take care of itself.
