Model integration
Adding capability inside an existing product, in the screens people already use.
Already have a product or a business process that could benefit from AI? Integration is usually a better first move than building something new, because the data, the users and the workflow already exist.
This work can include model integration, AI agents, retrieval systems, internal knowledge tools, document processing, recommendations, workflow automation, API integrations, permissions, testing, and evaluation.
The measure of a good integration is that people use it without being told to, because it sits inside the tool they were already in.
Problems this solves
You may recognise some of these.
- An AI feature was added and nobody uses it, because it lives in a separate tab
- A model works in testing and behaves differently against real data
- You want AI in your product but cannot expose customer data to a third party
- Nobody can say what the AI feature costs to run per user
- The output is good most of the time and there is no process for the rest
What we do
The work itself
Workflow automation
AI assisted steps inside a process, with a person in the loop where that matters.
Permissions and data boundaries
Controlling what the model can see and do. Usually the real blocker in sensitive or regulated work.
Cost and usage instrumentation
Knowing what a feature costs before it becomes a line item nobody can explain.
Testing and evaluation
Regression cases, so a model or prompt change does not quietly degrade the product.
Process
How an engagement runs
Map the workflow as it exists
Where the time actually goes, before deciding what to automate.
Pick one step
Integrations succeed narrow and spread. They fail broad.
Wire it into the existing product
Same login, same screens, same permissions.
Measure adoption and quality
Usage is the honest signal.
Expand it or remove it
A feature nobody uses should be taken out.
Technology
What we typically build this on
The technology is chosen around the project. The business problem comes first.
- Python
- TypeScript
- Node.js
- React
- PostgreSQL
- Firebase
Related work
Software we have built
Nutrition and Meal Planning Application
easyChef Pro
A consumer health and nutrition platform combining meal planning, pantry management, grocery workflows, nutrition data and personalised recommendations.
Read the case study →B2B Nutrition Platform and API
NutriSync
The business arm of easyChef Pro. The same scoring engine sold white label, brown label, or as an API, to sports teams, healthcare providers and enterprises.
Read the case study →Questions
Things people ask before starting
- Can AI be added without rebuilding our product?
- In most cases yes. Integration work usually touches a defined surface of an existing system rather than replacing it.
- What happens to our data?
- Data boundaries are a decision made at the start: what leaves your systems, what does not, and what the model is permitted to see. It is often the constraint that determines the architecture.
- How will we know whether it is working?
- Adoption and evaluation. If people route around the feature, it is not working, however well the output reads.
Related services
Often part of the same engagement
Tell us what you are trying to build
Tell us about the business, the problem, what you have today, and what you want to accomplish. If we are not the right team, we will tell you.