AI & Data Solutions
Generative AI & LLM Integration
Summaries, draft replies and smarter search, added to the software your team already uses.
Discuss Your ProjectIn plain terms
This means adding AI features to the software people already use: a summary button on a long record, a draft reply, a search box that understands what you meant, or a chatbot on your website that answers from your own information. The AI is a feature inside your application, not a separate product to learn.
Does this sound familiar?
- People paste customer details into public chatbots because nothing approved exists
- A proof of concept impressed everyone, but nobody knows how to run it for real
- Nobody has worked out what the AI bill will be at full use
- Answers vary, and there is no way to tell whether the latest change made things better or worse
A typical scenario
A claims team at a small insurer
Before
Handlers open a claim with sixty notes and emails in it and spend ten minutes reading before they can decide what happens next. New handlers take months to find their feet.
After
A Summarise button shows the key facts, what has been agreed and what is still outstanding, each point linked to the note it came from. The handler checks it and gets on with the claim.
How we deliver it
From first call to running
- 1
Choose the feature
We pick one place in one application where a few seconds of AI saves minutes of work.
You get: A short specification and the data it will use
- 2
Prove it on real data
We try several models on your own records and compare quality, speed and cost.
You get: A comparison with a recommendation and the cost per thousand uses
- 3
Build it into the application
The feature goes into your web, desktop or mobile app, with fallbacks for when the model is slow or wrong.
You get: A working feature in a test version
- 4
Test with real users
A small group uses it every day and rates the results.
You get: Ratings, a list of issues and a go or no-go decision
- 5
Go live and monitor
Usage, cost and quality are tracked from the first day.
You get: A dashboard and a monthly review
What we need from you
- Access to the application code, or to the developers who look after it
- Sample records, with personal data handled as you direct
- A decision on which data may leave your environment
- A handful of users willing to test
Where it works best
- Long text that people read, summarise or reply to
- Search where people do not know the exact words
- Questions that customers ask again and again
Worth sorting out first
- Figures that must be exact, such as accounting totals: keep those in your existing software and use AI for the reading and drafting around them
- Answers nobody checks: add a person's review, or a link to the source, so every answer can be verified
- Very small volumes: start with the feature your team would use every day
How we work with you
Normally a fixed price for one feature. Model usage is billed separately at the provider's price, and further features can follow once the first is working.
Discuss Your ProjectTypical technology
Questions
Which AI provider do you use?
Whichever suits the job. We write the code so the model can be swapped later, because prices and quality shift every few months.
Can it run inside our own Azure tenancy?
Often, yes. Azure OpenAI runs inside your own subscription, which settles most questions about where data goes.
Can we have a chatbot on our website?
Yes. A website assistant that answers only from your own pages and documents, hands over to a person when it cannot help, and never makes promises you have not approved.
Will our data be used to train the AI?
Business versions of these services generally commit not to train on your data. We check the current terms for whichever service you choose and put them in writing.
Related services
RAG & Enterprise Knowledge Search
Ask a question in plain English and get an answer from your own documents, with the source alongside.
Learn more →AI Quality, Security & Governance
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Learn more →