AI integration services for the software you already run
Bring AI capability into existing products and internal systems without a rebuild — starting where the value is clearest and the risk is lowest.
one narrow workflow first · no rebuild
- Opportunity assessment
- Pilot implementation
- API and model integration
- Best when
- You run software that works and want AI in one narrow workflow first.
- Not a fit when
- You are looking for a rebuild, or the data is not accessible yet.
- First thing you get
- A feasibility assessment and one measured pilot, typically four to eight weeks.
What is AI integration?
AI integration means adding AI capabilities, such as search over your documents, classification, summarisation or an assistant, to software you already run, through APIs and services around it rather than a rebuild. Your existing system stays the source of truth. We start with the use case where the value is clearest and the risk lowest, then extend.
Most organisations do not need a new AI product. They need their existing systems to do a few specific things better, and they need it done without destabilising software that currently works.
We start narrow on purpose: one workflow, measurable, low blast radius. It proves the value, teaches us where your data actually lives, and gives you something concrete to point at internally before committing to anything larger.
What this includes
- 01
Opportunity assessment
Identifying which workflows are genuinely worth automating and which are not.
- 02
Pilot implementation
One narrow use case built and measured before any broader commitment.
- 03
API and model integration
Connecting AI capability into existing applications through clean boundaries.
- 04
Data preparation
Getting your data into a state where AI can actually use it — usually the real work.
- 05
Rollout and enablement
Scaling from pilot to production with your team trained to operate it.
- 06
Ongoing tuning
Monitoring and improvement after launch, when real usage reveals what the pilot could not.
Situations we see most often
You have an idea but no internal AI experience
We assess feasibility honestly, including telling you when AI is the wrong tool for the problem.
Your product needs an AI feature to stay competitive
Adding capability to a live product without disrupting what already works.
A pilot needs to become production
Taking a proof of concept and giving it the reliability, monitoring, and cost control production requires.
Your data is not ready
Often the real blocker. We do the preparation work rather than pretending it away.
What to know before you start
How do you find the right first AI use case?
With a short opportunity assessment: we look at where people spend time on repetitive work, where data already exists, and where an occasional error is caught rather than costly. The best first use case is high-volume, currently manual and low-risk.
We also say plainly when AI is not the answer. A well-designed form, a scheduled job or a fixed integration solves a meaningful share of the problems brought to us, for less.
What happens in an AI pilot?
One workflow is built on your real data and measured: accuracy, time saved, cost per task and the cases it gets wrong. A pilot typically takes four to eight weeks, mostly decided by how quickly data can be accessed.
The pilot ends with evidence and a recommendation to scale, adjust or stop, which is the point. A pilot that cannot fail is not testing anything.
How is AI added without destabilising existing software?
By keeping the AI at a clear boundary: it talks to your systems through APIs, webhooks or queues, and your existing software stays the source of truth. Cost caps, monitoring and fallbacks mean that if the AI service is slow or unavailable, the core system keeps working as before.
That boundary also makes it easy to change models or providers later without touching the rest of your software.
What if our data is not ready for AI?
That is common, and it is usually the real first project. We assess where the data lives, how clean and accessible it is, and what minimum preparation the first use case needs, rather than a large data programme before anything useful happens.
How does a pilot become production?
By hardening what the pilot proved: proper integration with authentication and error handling, monitoring and cost caps, fallbacks for when the AI service is unavailable, documentation, and training for the people who will use it.
Rollout is staged, starting with one team or site, so issues surface at a scale where they are easy to fix.
What drives the cost of AI integration?
Integration cost depends far more on your systems and data than on the AI. These factors move it most.
| Factor | Effect | Why it matters |
|---|---|---|
| Data accessibility | High | Data locked in systems without APIs, or scattered across spreadsheets, is usually the longest part of the work. |
| Systems to integrate | High | Each system the AI reads from or writes to needs a clean, monitored integration. |
| Accuracy and risk | Medium | Higher-stakes workflows need more evaluation, human review and fallbacks. |
| Rollout and enablement | Medium | Training staff and adjusting processes is part of making the pilot stick. |
| Ongoing tuning | Low | Models and data change; periodic re-evaluation and tuning keep quality steady. |
What a typical engagement looks like
- 011–3 weeks
Discover
Process map
- 022–4 weeks
Design
Interactive prototype
- 03two-week cycles
Build
Working software each cycle
- 04ongoing
Ship & support
Automated pipeline
What we build it with
- Boundaries
- REST APIsWebhooksEvent queues
- Models
- ClaudeGPTModel routing
- Operations
- Cost capsMonitoringFallbacks
Questions we get asked
01Where should we start?
With a workflow that is high-volume, currently manual, and low-risk if it occasionally gets something wrong. That combination proves value quickly without betting anything important on an unproven system.
02Will you tell us if AI is not the right answer?
Yes. A meaningful share of the problems brought to us are better solved with a well-designed form, a scheduled job, or a fixed integration. We would rather say so than sell an unnecessary AI project.
03How long does a pilot take?
Typically four to eight weeks depending on how accessible your data is. Data access is usually the long pole, not the AI work.
04Do we need our own AI team?
No. We build, integrate and run the first use cases, and document them so your team can operate and extend them. Many clients grow internal capability alongside us.
05Can you integrate AI into software someone else built?
Yes. We work through the system's APIs or add an integration layer, so the original software does not need rewriting.
06Which AI providers do you work with?
Anthropic, OpenAI and open-weight models, chosen per use case. Integrations are built so the provider can be changed without reworking your systems.
07What does an opportunity assessment cost?
It is a short, fixed-scope piece of work, priced up front, that ends with a ranked list of use cases and a recommended first pilot. You are free to take that plan to anyone.
08Can AI be added to off-the-shelf software we use?
Often, yes, through its APIs, webhooks or exports. Where a product exposes none, we look for an integration point around it, such as email or a shared database, before suggesting anything larger.
Adjacent capabilities
Tell us what you are building.
Share the problem and we will come back with an approach, a rough shape of the work, and what it would take to deliver it.