A CTO asks a reasonable question halfway through vendor selection: if we pay you to build this, who owns the fine-tuned model?
Most vendors have not been asked before. Some answer clearly. Others say it has never come up, which is itself the answer.
That question matters more in AI than in conventional software, because the valuable artefacts are not only code. They are the fine-tuned weights, the prompt library that took three months to get right, the evaluation set that proves the system works, and the cleaned training data underneath all of it. A contract that assigns code ownership and stays silent on those four things leaves your most reusable assets with your supplier.
This is one of several checks that separate firms who build production AI from firms who build impressive demonstrations. Below are 13 companies working on custom AI development, grouped by what they actually provide, with the security requirements and selection questions that decide outcomes.
Three different things get sold as custom AI
The market blurs categories, and buying across them by accident is expensive.
AI development and consulting firms build the system for you. You own a working product at the end, and the engagement looks like software delivery with a data problem attached.
Enterprise AI platforms give you tooling to build and operate models yourself. Valuable, and they are not builders. Buying a platform without the team to use it is the most common form of shelfware in this category.
Data and evaluation infrastructure providers supply labelled data, human review and benchmarking. Specialist, usually bought alongside one of the above rather than instead of it.
A fourth distinction matters within the first group: firms strong at classical machine learning and forecasting are not automatically strong at language models and retrieval, and the reverse is also true. Ask which one your problem actually is.
The 13 companies at a glance
| # | Company | Type | Best suited for |
|---|---|---|---|
| 1 | Dev Technosys | Development firm | Custom AI built into existing enterprise systems |
| 2 | Fractal Analytics | Development firm | Decision science and analytics-led AI |
| 3 | Quantiphi | Development firm | Applied ML with deep cloud partnerships |
| 4 | SoftServe | Development firm | Engineering-heavy AI across industries |
| 5 | GlobalLogic | Development firm | Product engineering with embedded AI |
| 6 | Tredence | Development firm | Supply chain and retail analytics AI |
| 7 | Turing | Development firm | Scaling AI engineering capacity quickly |
| 8 | Databricks | Platform | Data and model work on one platform |
| 9 | Dataiku | Platform | Mixed technical and business user teams |
| 10 | DataRobot | Platform | Rapid model development and governance |
| 11 | C3.ai | Platform | Industrial and asset-heavy applications |
| 12 | Palantir | Platform | Complex data integration under governance |
| 13 | Scale AI | Data infrastructure | Labelled data and model evaluation |
Positioning described below comes from each company’s own public material, so confirm current capability and references directly before committing.
AI development and consulting firms
1. Dev Technosys
Dev Technosys builds custom AI systems that operate inside existing enterprise infrastructure rather than alongside it, with permission filtering applied at the query using the source system’s own role rules. Scoping begins with data condition and the integration list, since those determine the timeline far more than model selection does. Contracts assign ownership of fine-tuned weights, prompt libraries and evaluation sets to the client. Work covers AI integration, retrieval systems and ongoing support. Dev Technosys suits organisations whose AI must act on internal data under strict entitlement rules. Engagements start from $10,000.
2. Fractal Analytics
Fractal approaches AI through decision science, framing problems around the business decision rather than the model, which is a useful discipline because a technically excellent model attached to a decision nobody makes differently delivers nothing. Its strength sits in analytics-led AI and forecasting rather than language-model products. Confirm depth on generative and retrieval work specifically if that is your requirement, since the heritage is analytical. Fractal suits organisations where the AI supports a recurring commercial decision.
3. Quantiphi
Quantiphi has substantial applied machine learning experience and deep partnerships with major cloud providers, which matters practically because much of the engineering in production AI is cloud infrastructure work. Firms with certified depth on your cloud navigate quotas, regions and managed services faster. The consideration is that close platform alignment shapes architecture toward that platform. Quantiphi suits organisations committed to a specific cloud and wanting applied ML delivered on it.
4. SoftServe
SoftServe brings large-scale software engineering capability with an AI practice layered on top, which suits builds where the AI is one component of a larger system rather than the whole product. Engineering-heavy firms handle the unglamorous parts well: pipelines, observability, deployment and the testing that makes a model maintainable. Verify data science depth separately from engineering depth. SoftServe suits substantial builds where AI sits inside a wider application.
5. GlobalLogic
GlobalLogic works in product engineering with AI embedded into products rather than delivered as standalone analytics, a useful distinction when the AI feature ships to customers and must meet product quality standards. Product-engineering firms think about user experience and failure states, which internally focused teams frequently neglect. The question to settle is who owns the data engineering beneath. GlobalLogic suits companies putting AI features into a product customers use.
6. Tredence
Tredence concentrates on analytics and AI for supply chain, retail and consumer industries, and that vertical focus brings domain knowledge that generalists price as discovery. A firm that already understands demand forecasting or assortment planning arrives with the questions rather than asking you to supply them. The limit is the same focus: outside those verticals the advantage disappears. Tredence suits retail and supply chain organisations with forecasting and optimisation problems.
7. Turing
Turing supplies AI engineering capacity at speed, which addresses a specific and common situation: you know what to build, you have technical leadership, and you cannot hire fast enough. Capacity models work when you can direct the work and fail when you cannot. Treat it as augmentation rather than delivery, and keep architecture decisions in house. Turing suits teams with strong internal leadership needing hands rather than direction.
Enterprise AI platforms
8. Databricks
Databricks brings data engineering and model work onto one platform, which addresses the real bottleneck in most AI projects. Model quality depends on the data and retrieval layer far more than on model choice, and platforms that treat both as one problem reduce the handover losses between a data team and a modelling team. It assumes engineering capability on your side. Databricks suits organisations with data teams who will operate the platform themselves.
9. Dataiku
Dataiku is built for mixed teams where analysts and data scientists work on the same projects, with a visual layer over code. For organisations wanting business users involved rather than only engineers, that accessibility is the proposition. The caution is governance: platforms that make building easy also make proliferation easy, and ungoverned models multiply quickly. Dataiku suits organisations democratising AI work across non-specialist teams.
10. DataRobot
DataRobot focuses on accelerating model development and bringing governance alongside it, which suits regulated environments needing documentation of how a model was built and validated. Automation shortens the path from data to deployable model. The trade-off is less control over the modelling approach, which matters when the problem is unusual. DataRobot suits organisations needing many models built quickly with auditable process.
11. C3.ai
C3.ai targets industrial and asset-heavy applications, including predictive maintenance and operational optimisation, with prebuilt applications for those patterns. For organisations whose requirement matches a pattern the platform already encodes, that removes substantial build work. Where the requirement is unusual, the prebuilt advantage becomes a constraint. C3.ai suits manufacturing, energy and utilities organisations with conventional industrial AI needs.
12. Palantir
Palantir’s strength is integrating messy data across many systems under heavy governance, which is frequently the actual obstacle rather than the modelling. Organisations whose data sits in dozens of incompatible systems get more value from that integration than from any model. It is an opinionated platform with a working style that suits some organisations considerably better than others. Palantir suits large organisations with severe data fragmentation and strict governance requirements.
Data and evaluation infrastructure
13. Scale AI
Scale AI provides labelled data, human review and model evaluation, addressing the part of custom AI that teams consistently underestimate. Without a reliable evaluation set, questions about whether a system is improving become matters of opinion, and opinions lose budget arguments. Human review capacity also matters where generative AI output needs checking before it reaches users. Scale AI suits organisations needing training data or a defensible evaluation process rather than a build partner.
Security and ownership: what to settle before signing
Custom AI builds carry requirements that conventional software projects do not.
Who owns the artefacts. Fine-tuned weights, prompt libraries, evaluation sets and cleaned training data. Name all four in the contract. Code ownership alone is insufficient.
Where data goes during development. Which model providers see your data, in which region, retained for how long, and whether it trains anything. For European and Californian data this is a lawful basis question rather than a preference.
Permission filtering before retrieval. If the system retrieves broadly and then suppresses what the user should not see, the data has already crossed the boundary and a differently phrased question will surface it. Filtering belongs inside the query.
Production data in development environments. Using real customer records to test is among the most common avoidable exposures. Require de-identification as a condition rather than a recommendation.
Prompt injection through your own content. A document, a ticket or a record can carry instructions written to manipulate a system reading it. Where the system can act rather than only answer, that becomes an attack path, so all retrieved content is untrusted input.
Model supply chain. If the build uses third-party or open-weight models, establish their licence terms, their provenance and what happens if the provider changes pricing or withdraws a version.
Evaluation before launch, monitoring after. A golden set of inputs with expected outputs, run repeatedly, with acceptable variance defined in advance. Then drift monitoring, since model behaviour and your data both change. Where a system was built quickly and now handles sensitive data, an AI application audit costs less than finding the gaps during an incident.
Running cost at full adoption. Model calls are billed per interaction, so cost rises as the system succeeds. Model it at expected volume during scoping.
How to choose
Decide whether you are buying a build, a platform or capacity. Those are three purchases and the wrong one is expensive in a different way each time.
Then ask what the vendor would need to see in your data before quoting. Specific answers come from people who have done this. A quote produced without looking at your data is a quote for a project nobody has scoped.
Then ask what their last rescued project looked like. Firms that have taken over stalled work have seen the failure modes, and a willingness to discuss AI rescue work honestly tells you more than a portfolio of launches.
Finally hand every shortlisted firm the same brief: your systems, your role structure, your data condition and the decision the AI should support. Compare specificity, not enthusiasm.
Conclusion
The thirteen companies here solve different problems, and the ranking matters far less than the category. A platform without a team to run it sits unused. A capacity partner without internal leadership drifts. A vertical specialist outside its vertical is a generalist charging specialist rates.
Before you contact anybody, write down three things: the decision this AI should change, the state of the data it will run on, and who will own it in eighteen months. If the third question has no answer, fix that first. It is the one that determines whether this becomes a system or a pilot.