AI-Enabled Product Development

AI features that do a real job inside your product.

Large language models can draft, summarise, classify, extract, and answer questions, but they only create value when they are part of a clear workflow with the right data, guardrails, and human review. We design and build AI-enabled features and products end to end: choosing the use case, prototyping on real examples, measuring quality, and running it reliably in production.

Best for: Startups and growing businesses that want useful, measurable AI features in a product or internal workflow, built with the same engineering discipline as the rest of the system.

Signs you need this

Problems this solves

Teams spend hours reading, sorting, or summarising documents, emails, or support tickets.
Useful knowledge is spread across files and tools, and people cannot find answers quickly.
An AI prototype works in a demo but is not reliable, measurable, or safe enough for production.
It is unclear which AI use case is worth building first, or what it will cost to run.
Customer data and privacy requirements make it hard to use AI tools directly.

What you receive

Typical deliverables

Use-case assessment covering expected value, risks, and running costs
Working prototype tested on real examples
Evaluation set and quality metrics for the AI feature
Production integration with your product, data, and permissions
Guardrails: input and output checks, human review steps, and fallbacks
Monitoring for quality, latency, and cost after launch
Documentation and handover

Examples

What this can look like

Illustrations of the kind of work this service covers, not descriptions of client projects.

Document Q&A assistant

An assistant that answers questions from an organisation’s own policies, manuals, or contracts, cites the source passages, and respects existing access rules.

Inbox and ticket triage

A workflow that reads incoming emails or support tickets, classifies them, extracts key fields, and drafts a reply for a person to review and send.

AI features inside a SaaS product

Summaries, smart search, content drafting, or data extraction added to an existing product, with usage limits, cost tracking, and quality checks.

Process

How the work runs

1
Choose one high-value use case and define what a good result looks like.
2
Prototype on real examples and measure quality before building further.
3
Design the workflow around the model: data access, prompts, tools, review steps, and fallbacks.
4
Integrate into the product with permissions, logging, and cost controls.
5
Launch to a small group, monitor results, and improve with real feedback.

The details

Before you start

Responsibilities, technical and security points, timelines, and ways of working together.

Your role in the project

Share representative examples (documents, tickets, or records) to build and test against.
Decide who reviews AI output and what quality bar is acceptable.
Confirm data-handling and privacy requirements for the content involved.
Provide a domain expert who can judge the quality of answers.

Technology considerations

The model is chosen per use case, balancing quality, speed, cost, and data requirements, and kept replaceable.
Retrieval over your own content is usually more reliable than fine-tuning for company knowledge.
Structured outputs and tool use make AI responses easier to validate and act on.
Evaluation sets turn quality from a feeling into a number that can be tracked over time.
Usage limits, caching, and model choice keep running costs predictable.

Security considerations

Sensitive data is minimised, masked, or excluded before it reaches a model where required.
Access controls apply to AI answers in the same way they apply to the underlying data.
Prompts and outputs are logged in a privacy-conscious way for debugging and audit.
Prompt-injection and data-leakage risks are tested before launch.
People stay in the loop for decisions with legal, financial, or safety impact.

What affects the timeline

Duration depends on the use case, the state of the source data, the depth of integration, and the quality bar. A focused prototype on real examples is usually quick to produce; a production rollout takes longer because it needs evaluation, guardrails, permissions, and monitoring. We start with one use case and expand once it proves its value.

Ways to work together

Discovery and Architecture

Suitable for unclear or complex requirements.

Workflow analysis
Requirements
Scope
Architecture
Data model
Risks
Implementation roadmap
Estimate

Focused MVP

Suitable for a tightly scoped first release.

Core user journeys
Production-ready foundation
Deployment
Analytics
Handover

Project Delivery

Suitable for defined software initiatives.

Iterative releases
Testing
Deployment
Documentation
Support

An estimate needs a clear problem description, target users, key workflows, constraints, and integration points. Pricing and timelines are only given for an agreed, engagement-specific scope.

FAQ

Frequently asked questions

Which AI models do you work with?

We choose the model for each use case based on quality, speed, cost, and data requirements, and design the integration so the model can be changed later without rebuilding the feature.

Will our data be used to train AI models?

We design integrations around provider settings and plans that do not use your business data for model training, and we document how data moves through the system. The exact terms depend on the provider and plan you choose.

How do you know an AI feature is good enough to launch?

We build an evaluation set from real examples before launch and measure accuracy, failure cases, and cost. The feature ships when it meets the agreed bar, and monitoring continues after launch.

Have an AI use case in mind?

Describe the task you want AI to help with and the data involved. We will help you decide whether it is worth building and how to start.