In short, choose an AI-driven software engineering company that has proven its ability to deliver in a production environment even when dealing with data as complicated as yours, incorporating evaluation and cost control right from the first sprint; access to cutting-edge models is merely a basic requirement. The advice in this guide is based on research conducted by Gartner and McKinsey as well as on the experience gained by Sombra, a global AI-driven software engineering company founded in 2013 that now has over 400 specialists in the United States, Canada, Europe and Latin America.
What makes it more difficult to select an AI engineering partner in 2026–2027?
It’s no longer difficult to get AI to function in a pilot project; the challenge lies in getting it to work in a production environment. Although many enterprises already make use of AI, a lot fewer operate AI systems that are capable of generating a return on investment. Moreover, a great many vendors are able to put together an impressive demonstration, only for them to have trouble when the system has to run every day.
The figures on this are given by McKinsey’s State of AI 2025 survey. In the 2024 edition, 78% of respondents said their organizations regularly used AI in at least one business function; a year later the figure had climbed to 88%. However, only about one third of the respondents said that their companies had begun to scale AI across the entire enterprise, and just 39% were able to cite any impact on EBIT from it.
Moreover, back in July 2024 Gartner predicted that at least 30% of generative AI projects would be dropped after the proof of concept by the end of 2025, due to poor data quality, inadequate risk controls, increasing costs, or an unclear business value. In June 2025 it went further and said over 40% of agentic AI projects would be canceled by the end of 2027. In that same report, Gartner estimated that only around 130 of the thousands of vendors offering “agentic AI” truly have agentic capabilities and refers to the rest as agent washing.
The thing you now need to check is different. Two years ago, the issue was whether a vendor understood large language models; today it is whether they are able to run one inside your ERP at a cost per query that your CFO will approve.
Qualities of a strong enterprise AI-powered software engineering partner
A strong partner should have both the ability for strategic assessment and production engineering skills. In the case of most mid-market and large enterprises, the true factors that set them apart are their expertise in data readiness, the depth of their integration capabilities, experience in governance, and a track record of getting validated use cases into production.
Different vendors start from various points and each has a typical way of failing.
| Vendor type | Where it is strong | Where it tends to break | Best fit |
| Strategy-led AI consultancy | Use-case discovery and business cases | Build work goes to another firm, so accountability splits | Leadership teams that need a roadmap before a budget |
| Conventional software engineering company with an AI practice | Integration and delivery discipline | Evaluation and LLMOps may be new territory | Adding AI features to existing products |
| AI-powered engineering partner | Discovery through production, including evaluation and cost control | Often costs more upfront than staff augmentation | Enterprises moving from pilot to scale |
| SaaS or AI platform provider | Fast results for narrow processes that follow a standard pattern | Lock-in and limited customization | Processes that already match the tool |
Hardly any vendor fits neatly into one row (most straddle two), and that’s fine; what you want to know is who on their side will still be accountable for your project three months after go-live.
Engagement model by AI maturity stage
Pay for the stage you’re actually at. If nobody has validated a use case yet, you’re looking at an assessment; once one has been validated, the next purchase is a proof of concept with success criteria agreed in writing. Paying for production engineering before either step is how AI budgets disappear.
| Where you are | Best approach | Typical outcome | Indicative duration |
| No defined use case | AI opportunity assessment | Prioritized roadmap with business cases | 2–6 weeks |
| Known use case | PoC or prototype on your real data | Validated solution with measured accuracy | 4–10 weeks |
| Validated PoC | Production rollout | Integrated, monitored production system | 3–6 months |
| System in production | Optimization and governance | Lower run cost and higher reliability | Ongoing |
Use these durations for rough planning only; the state of your data will stretch or shrink them more than anything else on this list.
Pro tip: before any work starts, get each vendor to put the PoC exit criteria on paper – target accuracy, acceptable latency, cost per transaction – along with the name of whoever signs off. A vendor who won’t commit to those numbers is, in effect, charging you for a demo.
Six criteria that separate production partners from demo builders
Assess the vendors according to the way they manage data, integration, evaluation, governance, operating costs and the use of AI in their own services, since the choice of model rarely determines the outcome, as most reputable vendors are able to attain the same frontier models via Amazon Bedrock, Microsoft Azure, Google Cloud Vertex AI or the APIs offered by the model providers themselves.
1. Data readiness comes before model selection
When Gartner surveyed 1,203 data management leaders in July 2024, 63% of them admitted that their organizations either didn’t have the data management practices AI requires or couldn’t say for sure whether they had them; on that basis, Gartner forecast that through 2026 organizations would abandon 60% of AI projects lacking support from AI-ready data.
Find out from each vendor what they plan to do with your data during the first two weeks; a satisfactory response will be based on profiling actual records, while a poor one will consist of reading your documentation.
2. Integration depth
Can the AI feature read from your work-order system and write back to your CRM? If it can’t, the nice chat window in front of it won’t help much. Ask the team for an architecture diagram from a project they’ve done before, and look at how they handled authentication, how row-level permissions were enforced, what happened at rate limits, and how they got around the legacy APIs.
3. Evaluation and observability from sprint one
Find out what metrics they will use after the launch. Experienced teams monitor the p95 latency, the token cost per query, trace coverage, the rate at which humans have to override and the error rate by component. They also create offline test sets from actual user questions before making any adjustments.
4. Governance you can show an auditor
The NIST AI Risk Management Framework, along with ISO/IEC 42001, gives your risk people and the vendor’s team terminology they both understand. The practical test is easier to carry out: can the vendor show you the audit logs and the per-call lineage from a live system, and explain exactly where a human can override the model?
5. Cost engineering for inference
At an enterprise level, token expenditure turns into a budget item. Ask them about model routing, semantic caching, prompt compression and batch processing for jobs that are not urgent. Also find out when the team last reduced a client’s running cost and by how much.
6. How they use AI in their own engineering
Nowadays almost all people are using AI when writing code. In Google’s 2025 DORA report, nearly 5,000 technology professionals were surveyed and it was found that 90% of them use AI in their work, while over 80% believe that it has made them more productive. However, 30% of the respondents stated that they have little or no trust in the code that is generated. The researchers’ conclusion was that AI amplifies the existing state of a team, with good practices improving further and poor ones breaking down faster than before.
So, if a vendor promises that AI coding tools will speed delivery up, it is reasonable to ask in what way the code gets reviewed and tested before it is handed over; where that step is weak, you will simply be getting the defects at a higher speed.
What does production AI actually look like?
In production, most of the AI work comes down to context engineering and measurement, and the figures from one recent engagement show how much that matters.
The AI search feature was developed by the Sombra software engineering team for a large technology company that provides services in the fields of construction, infrastructure, utilities and geospatial applications. Each work order included hundreds of attributes and internal codes and the number of records in the results reached tens of thousands; when the system was rolled out to tens of thousands of users, token expenditure and latency became items in the budget.
They began by looking at user behavior rather than starting with documentation. The analysis was carried out on 36,000 historical logs of user queries and the logs were grouped according to intent and user type. The few-shot examples were taken directly from actual interactions.
A dedicated context manager determines what each agent sees and the format varies according to the model: on Amazon Bedrock Claude Opus agents are given structured JSON, Claude Sonnet agents receive semantically organized Markdown, and Claude Haiku agents are provided with compact summaries.
In Sombra’s use case, the prompt size decreased by more than 80% and response performance was improved by a factor of 2 to 3. Time-to-insight got up to three times faster, and 30% of the non-technical users were able to work with complex construction data without having to rebuild the queries by hand.
The key point for buyers is that the 80% reduction in prompt length was the result of changes made to the context. It would not have been achieved by switching to a newer model. So when you ask a vendor where their latest improvement came from, listen for specifics (which logs, which prompt change, what it saved), and be wary of answers that start and end with “we upgraded the model”.
Sombra has an AI Lab for exactly this stage (use cases get tried out there before any delivery team picks them up). Viacheslav Brui, who heads Sombra’s Data and AI Competence Service, rarely names the model as the hard part of enterprise AI. The difficult work, according to Brui, begins with data access, integration, evaluation and governance.
Warning signs in vendor proposals
A few patterns that should make you slow down (or walk away):
- A demo that only ever runs on sample data, with no plan to try it on yours; clean samples hide the problems that tend to sink projects in month three.
- A fixed-price production quote prior to discovery, since it’s impossible to price an integration without having seen it.
- Agents proposed for everything. Gartner’s Anushree Verma has pointed out that many use cases sold as agentic don’t need an agentic implementation at all.
- A proposal that ends at go-live, which leaves every post-launch problem with your team.
- Vague ownership of prompts, evaluation datasets, custom connectors and fine-tuned weights; IP terms for all of these belong in the master agreement.
- Case studies without numbers, or ones where no system is still running in production.
How to run a 30-day vendor evaluation
Choose three or four vendors, provide them with the same brief together with a sample of your actual data, and assess them based on a set of weighted criteria; in that way, four weeks will be sufficient to see how each team approaches the problem under realistic conditions.
- In Week 1, each vendor should be given the same two-page summary which includes a description of the business problem, details about the systems concerned, an example of the data and any constraints such as the hosting region or the regulatory scope.
- In weeks one to two you should carry out detailed technical studies with the engineers who will actually be working on your account; sales architects may take part but the delivery leads should be doing most of the speaking.
- In weeks 2 to 3 have a short paid spike carried out, for instance over five to ten working days on anonymized data; it will tell you more than any response to an RFP.
- Week 3 is for reference calls, and only with clients whose systems have been live six months or more; ask what broke after launch and how long it took the vendor to fix it.
- By week 4 you score every vendor with the template below, and someone from security plus the business owner score alongside IT.
| Criterion | Weight | What a 5 out of 5 looks like |
| Data readiness approach | 20% | Profiles your real data in week one and names specific quality risks |
| Production track record | 20% | References with systems live for 6+ months, backed by metrics |
| Integration capability | 15% | Shows past architecture for authentication and permissions on legacy APIs |
| Evaluation/observability | 15% | Targets for accuracy, latency, cost per query and escalation agreed before the build starts |
| Governance/compliance | 15% | Maps your use case to its EU AI Act risk tier; can show audit logging |
| Commercial clarity | 15% | Pricing in stages; IP and data ownership spelled out |
FAQ
It creates and develops software that is based on AI, then connects this software to the systems that you currently use and makes sure that it continues to function in a live environment. Typical activities include use-case discovery, data engineering, retrieval-augmented generation (RAG), the development of agents, evaluation and LLMOps. The main difference from a pure consultancy is that there is accountability for the actual code, since the team which recommends an approach is also responsible for making it work. Many of these companies make use of AI tools in their own development process as well, and this helps to speed up delivery, provided that the review practices are strong.
The cost is mainly determined by the condition of your data and the number of systems concerned, which is why any price given before investigating the situation is just an estimate. Pricing on most enterprise projects goes in stages: a fixed-fee assessment first, then a scoped proof of concept, and only after that production delivery, paid by milestone or on time and materials. Budget for the running costs as well – model inference, monitoring, security reviews, and another round of evaluation each time the underlying models change. With high usage volumes, a major portion of the total cost of ownership is made up of these recurring costs, and for this reason each proposal ought to cover how prompts and routing will be optimized.
Begin by looking at evidence of successful delivery, that is, references from clients whose systems have been live for six months or more and who can back them up with measurable data. Next, carry out a brief paid spike to test how the vendor performs with your own data, as this will reveal any data or integration problems at an early stage. Evaluate all the shortlisted vendors using the same set of weighted criteria, the criteria covering data readiness, integration, evaluation, governance and commercial terms. Lastly, arrange a meeting with the delivery engineers, since the final outcome will depend to a large extent on the decisions they make.
A focused assessment will generally be completed within a few weeks, whereas a proof of concept that is carried out on real data usually takes one to two months. To get a single validated use case moved into a production system that is integrated and monitored, three to six months is a typical period. The greater part of this variation can be put down to how ready the data is and how complex the integration turns out to be, and in the case of regulated use cases additional time has to be set aside for documentation and risk assessment. Where a program involves several use cases, it tends to perform better when they are released one after another.
In the majority of enterprises the result is a hybrid model. What a partner contributes is speed, together with skills that are difficult to hire quickly, evaluation design and LLMOps being two examples. Decisions on the product and the long-term roadmap are best owned by your internal team, since that is where your knowledge of the domain resides. Knowledge transfer should be written into the contract from the first day, so that your engineers are in a position to run and extend the system once the engagement has come to an end.
An AI-powered software engineering company that is right for you will be in a position to show you production systems that are live, along with an explanation of what it costs to run them, and it will also be able to map your use case against whichever AI Act dates will be applicable at the time of your launch.
The next step can be modest. Take the use case with the cleanest data and the most motivated business owner, write it up as a two-page brief, and get it to three or four vendors before the quarter is out; the scorecard above then gives you a fair way to compare what comes back.