AI Features & Automation
AI Features Users Can Trust
An AI feature inside your MVP, or an AI-first product, scoped to one clear job and tested on real or realistic data before launch, with guardrails for when it gets things wrong, the running cost per task known and data protection planned from the start.
What you get at each stage
Sized to where your product is now, and built so the next stage adds to it rather than starting again.
Idea & validation
Prove the AI does the job before you build around it
- One use case chosen, with a clear measure of a good answer
- A quick prototype on real or realistic data
- Build, buy or wait advice, with the running cost per task
- An early data protection check on what the AI will see
MVP & launch
Ship an AI feature you can test, explain and afford
- The AI feature built into your MVP as part of the Must-have scope
- An evaluation set run before every release
- Guardrails, source citations and handover to a person
- Clear AI disclosure, and EU AI Act checks if you serve EU users
Growth & scale
Keep quality up and costs down as usage grows
- Monitoring of answer quality, cost and failures in production
- Model and provider reviews as prices and options change
- A governance pack for enterprise buyers and investors
- Custom or fine-tuned models where off-the-shelf ones fall short
What is included
Use case assessment
Where AI genuinely helps your product, where it does not, and what each option would cost to run.
Prototype on your data
A focused proof of concept that you and early users can try before it goes into the full build.
AI feature build
Assistants, document extraction, search or prediction, connected to your product and workflows.
Evaluation and guardrails
Test sets, accuracy and bias checks, topic limits and a fallback to a person when the AI is unsure.
Data protection and transparency
Provider terms checked, a data protection impact assessment where needed, and clear AI disclosure to users.
Monitoring and cost control
Cost per task measured from the prototype onwards, so it can go in your budget, with alerts and quality tracking in production.
Everything in AI Features & Automation
11 services. Take one or combine several: we size each to your stage and agree the scope and a fixed price before work starts.
AI features
- AI assistants and chatbotsAssistants that answer users and support your team, grounded in your own content, with guardrails.
- Document and data extractionInformation pulled out of documents, forms and emails for a person to check.
- Search and recommendationsResults and suggestions that match what each user is looking for.
- Natural language processingSoftware that reads, sorts and summarises text at scale.
- Predictive featuresForecasts of demand, churn or risk, where there is enough data to support them.
- AutomationRepetitive steps in your product or operations handled by AI, with a person in the loop where it matters.
- Custom modelsModels trained or fine-tuned for a specific problem, when off-the-shelf ones fall short.
Getting it right
- Use case assessmentWhere AI genuinely helps your product, where it does not, and what each option costs to run.
- Evaluation and guardrailsTest sets, accuracy checks, topic limits and a fallback to a person when the AI is unsure.
- Cost controlCost per task measured from the prototype onwards, with budgets and alerts in production.
- AI governanceRules for using AI responsibly and explaining it to users and enterprise buyers.
Scope it free, then build it
Your MVP brief is the brief
The brief you write in the free MVP builder is what we quote from. It tells us who the product is for, the Must-have features, the main journeys and the budget, so work on AI Features & Automation starts from your brief, not a blank page. It fits these stages:
In your brief
The sections it builds on
- MVP scope
- User journeys and screens
- Build approach
For established companies
New products and ventures, scoped first
Established companies use the same approach to run AI pilots and automate internal work, such as answering repeat customer questions, pulling data out of invoices and forms, or drafting routine documents for a person to check. Each pilot has an agreed measure of success and moves to production only once the numbers hold up.
Tell us about your projectQuestions founders ask
Will our data be used to train someone else’s model?
Not if we can avoid it. We choose providers and settings that do not use your data for training, check this in their contract terms, and can use models hosted in the UK or EU, or on your own infrastructure, when your data needs it.
How do you stop an AI feature making things up?
We ground its answers in your own content and show sources, limit the topics it covers, and test it against real questions before each release. When it is unsure, it says so or hands over to a person. No model is perfect, so we keep monitoring answers after launch.
What will the AI cost to run?
It depends on the model, how often the feature is used and how much text it reads and writes. We measure the cost per task on the prototype, so it goes into the budget and runway in your brief before you commit to the build.
Does the EU AI Act apply to a UK startup?
It can, when the output of your AI is used in the EU, for example an assistant serving EU customers. Its duties include telling people when they are talking to an AI. UK data protection law already sets rules for significant decisions about people made solely by automated means. We flag these early; your legal adviser gives the formal view.
Who does the AI work?
Our own team, as part of your MVP build or on its own: one brief, one point of contact and no hand-offs between agencies.