Choosing AI integration services is not just a matter of comparing day rates and delivery dates. The important questions are usually hiding underneath: which systems will the work touch, what data will move through them, how will you know the result is reliable, and who will look after it afterwards?
A useful proposal should make those questions easier to answer. It should describe the proposed work clearly, identify assumptions and separate what can be scoped now from what needs discovery.
Here’s a checklist for comparing proposals from software development studios and AI integration specialists in the UK.
1. The outcome and the first useful version
A proposal should begin with the job the integration is meant to perform, not the model or platform being used.
Look for a clear description of:
- The business process being improved.
- The people who will use or supervise it.
- The systems involved.
- The action the software will take, if any.
- What the first release will and will not do.
For example, “use AI to improve customer service” is too broad to price responsibly. “Classify incoming enquiries, suggest a response for a member of staff to review, and record the outcome in the CRM” gives everyone something more concrete to discuss.
The proposal should also explain what success looks like. That might include response time, completion rate, reduction in manual handling or the quality of suggested outputs. If a measure cannot be agreed yet, the proposal should say that it will be defined during discovery rather than quietly treating it as a promise.
2. System access and integration boundaries
AI rarely works in isolation. It usually needs controlled access to an existing application, database, document store, CRM, inbox or internal workflow.
Ask the supplier to list:
- Each system they expect to connect to.
- The proposed method of connection, such as an API, secure file transfer or an existing integration.
- The information that must be read or written.
- Whether the system supports the required access.
- Any environments needed for development, testing and production.
- Who will provide credentials, documentation and technical contacts.
This is an area where confident-looking estimates can become less certain. An integration may be straightforward if a well-documented API exists, but more involved if the system has limited permissions, inconsistent data or a supplier-controlled interface.
A good proposal names those assumptions. It should not imply that an undocumented third-party system is a known quantity. Where access or capability needs checking, look for a discovery task, technical spike or explicit dependency.
3. Data handling and privacy
The proposal should explain what data enters the AI workflow and where it goes. This is particularly important when the process involves personal, confidential or commercially sensitive information.
Look for answers to questions such as:
- What data is collected, transformed, stored or deleted?
- Is data sent to a third-party AI provider?
- Is it used to train or improve a provider’s models?
- Where is data processed and stored?
- How long are prompts, outputs, logs and uploaded documents retained?
- How are data minimisation, access controls and deletion handled?
- Which party is responsible for each part of the processing arrangement?
A proposal does not replace your organisation’s legal, security or data protection review. It should, however, give those teams enough detail to assess the approach. If the supplier cannot yet answer because the architecture has not been selected, that uncertainty should be visible.
For UK organisations, you may also need to consider your existing data protection policies, contracts and any transfer or supplier-assurance requirements. Treat those as part of the project conversation, not paperwork to discover at the end.
4. Evaluation before release
AI outputs can vary. A proposal should explain how the team will test the workflow before it is trusted with real work.
Ask whether the plan includes:
- A representative test set based on your own examples.
- Expected answers or acceptance criteria where practical.
- Checks for accuracy, completeness, unsafe outputs and unsuitable recommendations.
- Tests for different document types, edge cases and missing information.
- A human review step for decisions that should not be automated outright.
- A process for recording failures and improving the workflow.
The evaluation method should match the task. A document classification workflow may be measured differently from a customer-facing assistant or a system that extracts figures for a finance process.
Be wary of proposals that use a single accuracy figure without explaining how it will be calculated. A small demonstration can show that something is possible; it does not necessarily show that it is dependable in daily use.
5. Permissions and human control
The proposal should be clear about what the AI can see and what it can do.
At minimum, ask for a description of:
- User roles and permissions.
- The data each role can access.
- Actions the workflow may take automatically.
- Actions requiring approval.
- How the system handles uncertainty or a failed step.
- How access is withdrawn when someone leaves or changes role.
A sensible first release often keeps a person in the loop for consequential actions. That might mean reviewing an email before it is sent, approving a record change or checking extracted information before it enters another system.
The goal is not to make every process manual forever. It is to introduce automation with a clear route to safe, evidence-based expansion.
6. Running costs and third-party dependencies
The build price is only one part of the cost of AI integration services. Ask the proposal to separate one-off delivery from ongoing operation.
Possible running costs include:
- AI model usage, often affected by volume and input size.
- Hosting, databases, file storage and queues.
- Monitoring, logging and alerting.
- Third-party software licences.
- Support, maintenance and security updates.
- Additional usage or feature charges from connected systems.
A useful proposal states what assumptions the estimate uses, such as approximate transaction volumes or document sizes. If those figures are not known, it should explain how costs will be measured and controlled.
Also ask what happens if a third-party provider changes its pricing, API or model behaviour. The answer may involve a review process, an abstraction layer, a fallback route or a change budget. There is no universal solution, but there should be a plan.
7. Monitoring and support
An AI workflow needs more than a successful launch. You need to know when it is unavailable, producing poor results or encountering a new type of input.
The proposal should cover:
- Technical monitoring for failures, delays and unavailable services.
- Business monitoring for volume, completion and review rates.
- How outputs or events are logged without exposing unnecessary sensitive data.
- Who receives alerts and how they respond.
- How quality is reviewed after launch.
- The expected support route and response arrangements.
Ask whether monitoring is included in the initial delivery or left as a future option. A dashboard is not automatically useful; it should help someone decide what action to take.
8. Handover, documentation and ownership
A proposal should explain how the work becomes maintainable by your team or a future supplier.
Check for:
- Technical and user documentation.
- Source code and configuration ownership.
- Environment and deployment information.
- A record of prompts, rules, model settings and evaluation results.
- Credentials and secrets transferred through a suitable process.
- Training or walkthroughs for the people who will operate the workflow.
- A defined point at which handover is considered complete.
If the proposal includes ongoing support, clarify what remains with the studio and what your team is expected to handle.
What should be fixed now, and what can wait?
A proposal can usually scope the intended outcome, broad architecture, known integrations, delivery stages, responsibilities and estimate assumptions upfront.
The detail may depend on discovery when the work involves unfamiliar systems, sensitive data, uncertain data quality, complex permissions or a new AI capability. Discovery should produce useful outputs: a tested integration route, a refined scope, an evaluation plan, identified risks and a more dependable delivery estimate.
That is not a weakness in the proposal. It is a more honest way to deal with unknowns than hiding them inside a neat-looking fixed price.
When comparing AI integration services, favour the proposal that makes the work, risks and responsibilities easiest to understand. The best fit is not necessarily the one with the biggest feature list. It is the one that gives you a credible path from a useful first workflow to software your team can operate with confidence.
If you’re weighing up an AI workflow or an integration with an existing app, talk to Atlas-James. We’ll help separate the promising part from the part that needs a closer look.