02.02AI
AI integration
AI integration from Hot Rocket Software, a team of software, website and digital marketing specialists based in the UK, with 19 years in IT. Add AI features to software you already have: summarising, classifying, extracting data from documents, drafting replies and natural-language search.
02.02.0
What is AI integration?
AI integration is adding language-model features to software you already have, or are building: summarising records, extracting data from documents and emails, classifying incoming requests, drafting replies and natural-language search. We wire the model into your system with guardrails, evaluation tests and cost controls, so it behaves the same on day ninety as on day one. Adding AI to the software a business already uses is everyday work for our team, and you talk directly to the people doing it.
02.02.1
Who is it for?
- Software products adding AI features their customers ask for
- Businesses processing documents, forms or emails by hand
- Teams with an internal system that would be faster to search in plain English
02.02.2
From a small business to a large organisation
The same team takes on a single tool for a small business and long-running work for large companies and the public sector. Here is what each end looks like.
For a small business
AI added to a tool you already use, such as drafting replies or summarising enquiries, with a person checking the result.
For large companies and the public sector
AI built into core systems with access controls, logging, evaluation and data kept where your policies say it must stay.
- Private or UK-hosted model deployments
- Evaluation and quality monitoring
- Integration with document and case systems
- Security and DPIA support
02.02.3
What you get
- 01The AI feature, integrated into your existing app or API
- 02Prompt and tool design, versioned alongside the code
- 03An evaluation set so changes are tested before they ship
- 04Cost, latency and error monitoring
- 05Fallbacks for when the model is unsure or unavailable
02.02.4
How we approach it
Discover
Collect real examples of the task, good and bad.
Prototype
Test models against the examples before building.
Build
Integrate with guardrails, logging and human review.
Launch
Roll out gradually and measure accuracy.
Support
Re-test when models change; tune on feedback.
02.02.5
Typical stack
02.02.6
Questions
Which AI model will you use?
Whichever does the job best at an acceptable cost, tested against your examples. We design so the model can be swapped later.
Is our data used to train the model?
We use API terms and settings under which providers do not train on your data, or self-hosted models where that is required. We document which applies.
How do you stop AI making things up?
By grounding answers in your own data, constraining the output, testing against an evaluation set, and sending low-confidence cases to a person.
T+99Next step
Talk it through with us
A free 30-minute first chat, with no obligation. Tell us what is getting in the way; we tell you honestly what we would do, roughly what it would take, and whether it is worth doing at all.