Choosing an AI development partner in London is one of the most consequential decisions a business makes when investing in AI. The London market has hundreds of agencies claiming AI capability. A significant proportion have expertise in producing compelling demonstrations and much less experience delivering reliable systems that perform under real business conditions over months and years. The 12 questions below are not courtesy questions. They are diagnostic questions. The answers reveal which firms have genuine production AI experience and which are building their capability on your budget.
Before approaching any AI development partner, prepare a one-page brief covering: the specific business problem you are trying to solve (not the technology you think you need), the data you have available, the success criteria (what measurable improvement constitutes a successful project), the timeline and budget range, and the internal owner who will manage the relationship and evaluate outputs. Firms that ask for this information in the first conversation are working professionally. Firms that jump straight to proposals without asking are likely pattern-matching to a generic solution.
Not a case study on their website. A client you can call, ask specific questions about delivery timelines, post-launch performance, and how the firm handled problems when they arose. If they hesitate or offer only written references, understand why. A firm with a strong production track record has clients willing to take reference calls.
AI systems in production should improve over time as they process real data. A firm that knows the answer to this question monitors its deployed systems. A firm that does not have the answer treats deployment as the end of the engagement rather than the beginning of the operational phase. The answer also tells you whether they set measurable performance targets.
Any AI development firm working in language model applications should have a clear, specific answer to this question: RAG for grounding responses, confidence thresholds for escalation, human review gates for high-stakes outputs, and accuracy monitoring post-launch. Vague answers about AI being good at this these days indicate limited production experience. A specific, process-oriented answer indicates they have encountered this problem in real systems and solved it.
The answer should be a document: a requirements specification, a technical architecture document, or a detailed project scope that both parties sign before development begins. If the discovery process produces a verbal agreement or a brief email summary, expect scope creep, misaligned expectations, and disputes over what was agreed. A thorough discovery phase is a predictor of delivery quality.
Ask to meet the team, not just the business development contact. Find out which developers will work on your project, what systems they have built previously, and whether they have domain knowledge relevant to your sector. Junior developers building your system under light oversight is a common practice in agencies that win work on senior expertise and deliver on junior cost. You have the right to know who will actually build what you are paying for.
AI systems require ongoing maintenance: model retraining as your data changes, updates when integrated systems change their APIs, and monitoring for accuracy drift. A firm that treats the project as complete at launch is not the right partner for a system you intend to operate for two or more years. Understand their support model, the SLA for issue resolution, and the cost of ongoing maintenance before you sign.
Changes to requirements are inevitable in software development. A professional firm has a clear change request process: the change is documented, estimated, and agreed in writing before it is built. A firm that absorbs changes without a formal process is either pricing for them (you are paying for them indirectly through a higher base quote) or building resentment that surfaces as reduced quality in the later stages of the project.
A firm that answers this question specifically, including data format requirements, minimum volume expectations, quality criteria, and data cleaning support, has real experience preparing data for AI projects. A firm that says they will figure it out as they go has not encountered the data quality problems that derail most AI projects. Data preparation is 30% to 50% of the total project effort. A partner who takes it seriously from the first conversation will deliver a better system.
AI testing is different from standard software testing. Alongside functional tests, it requires accuracy testing across representative samples of the production data distribution, adversarial testing for edge cases that produce incorrect outputs, and regression testing when the model is updated. A firm with a mature approach to AI testing can describe this process. A firm without one cannot.
Before development begins, agree on specific, measurable benchmarks: minimum accuracy rate, maximum response time, minimum handle rate for the defined task scope. Then ask what the contract says about what happens if those benchmarks are not met. A confident, capable firm will agree to defined benchmarks and have a clear position on remediation. A firm that avoids defining benchmarks is avoiding accountability for delivering them.
You should own all code written specifically for your project, all custom-trained model weights, and all data used to train those models. Any open-source libraries or pre-trained models used in the build are subject to their respective licences, which you should review. If the firm is reticent about IP ownership, that is a significant warning sign. Ensure the contract specifies ownership explicitly before signing.
This question has no correct answer. It is designed to elicit honest reflection on past projects. A firm that answers this question thoughtfully, describing specific challenges they encountered and specific improvements they made as a result, has learned from real project experience. A firm that gives a generic positive answer has either no relevant past experience or is not willing to be honest about it.
For a scoped single-process AI automation: Β£15,000 to Β£50,000 for development. For a multi-component AI system with several integrations: Β£50,000 to Β£150,000. For an enterprise AI programme across multiple use cases: Β£150,000+. Rates below these ranges typically indicate junior teams, offshore delivery, or significantly reduced scope. Get three quotes with detailed scope specifications, not three quotes on the same vague brief.
Choose based on the specific expertise required for your project. A firm with deep experience in NLP and LLM integration is the right choice for a language-model-powered application. A firm with strong data engineering expertise is the right choice for a machine learning prediction system. A general software agency with a recently added AI capability is rarely the right choice for either. Ask specifically how long they have been building AI systems in production, not how long they have been a software agency.
If you would like to discuss your AI project requirements with our team, see our AI and Machine Learning Solutions service or AI Projects page to understand how we approach AI development for London businesses.
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