A guide to custom AI software for business leaders
What custom AI software is, when it's worth building instead of buying, how a project usually runs, and what to ask a firm before you hire one.
Custom AI solutions
Custom software for a problem no product fits
A custom AI solution is software written for one organization's problem. The AI part might be a language model that reads incoming documents, an agent that works through a queue of tasks, or a model that forecasts demand. Around it sits ordinary engineering: connections to your systems, a usable interface, security, logging. We design it with the people who'll use it, test a working version with them early, and then build it to production standard.
When this makes sense
- 01
A process runs on manual copying and checking, and no product on the market handles it.
- 02
An AI pilot impressed everyone in the demo and then went nowhere.
- 03
Staff spend a large part of the day searching documents or moving information between systems.
- 04
A recurring decision depends on information spread across too many places to see at once.
What's involved
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Design
We plan the application around the workflow, the people using it and the systems it has to talk to, and pick AI components that suit the job.
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Prototype
An early working version, built on your data, goes to a handful of users. We change it based on what they tell us before committing to the full build.
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Production build
Agents, models, search, automation and interfaces, connected to your systems and secured to your standards.
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Testing and safeguards
We test against the measures agreed at the start and add controls, including a person's sign-off where the stakes are high.
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Launch and handover
We deploy with monitoring in place and train your team to run it. Or we keep running it with you, if that suits better.
What you get
Every project follows the same five stages, and each stage ends with something you can review.
- Application design
- Working prototype
- Production software
- Test results and runbook
Custom AI solutions: common questions
What is a custom AI solution?
Software built for one organization's problem that uses AI, such as language models, agents, document search or forecasting, where it helps. A packaged product is designed for many customers at once; a custom solution is designed around one.
What kinds of AI software do you build?
Agents and assistants, workflow automation, tools that search and extract from documents, internal applications, and forecasting or decision models. Most projects combine two or three of these.
How do you know an AI solution works?
We agree the measures before we start, test a prototype with its users on your data, and score the finished software against those measures before launch. Monitoring continues afterwards, because results can drift as data and models change.
Who owns the software?
Ownership is set out in each engagement's contract. In every case we hand over documentation and a runbook so your team can operate and change what we've built.
Further reading
What makes AI software production-ready
A checklist for taking AI software into daily use: integration, security, evaluation, safeguards, human review, monitoring, documentation and ownership.
Why AI pilots stall before production
Why so many AI pilots never reach daily use, and what tends to get them over the line: a real problem, honest data, agreed measures and proper engineering.
Got a problem in mind?
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