How do we add AI to our existing product?
You don't have to make your product "AI-first" to use AI well. Often most of the value lies in one targeted improvement of an existing workflow. So the first question is not how to add AI, but where.
Does this sound familiar?
This often comes up when competitors launch AI features or users start asking what AI could mean in the product.
There are several possible AI features
Summarising, generating, searching, agents and automation all seem interesting.
You don't want to build a gimmick
The feature has to demonstrably solve an existing user problem better.
The feature needs product data
AI has to be able to safely use information that is already in the software.
The existing architecture sets limits
You want to avoid one AI feature becoming a separate technical island.
Where do you want to be?
You want to know which AI feature delivers enough user value and technically fits the product that already exists.
A sharp product use case
You know which user can solve which problem better with AI.
A fitting technical architecture
The new functionality connects to existing data, auth and business logic.
A realistic implementation route
You know what needs testing first and what can then be built production-ready.
Why does this happen?
Adding AI to existing software is both a product choice and an architecture choice.
See how we approach thisThe technology draws too much attention
An interesting AI possibility is not yet a useful product feature.
Existing data and permissions count
A feature has to fit the same user and security context as the rest of the product.
New failure modes are added
Variable quality, latency and model costs have to become part of the product experience.
AI Reality Check
In an AI Reality Check we first determine which AI use case has enough value and technical feasibility and how it fits into the existing product. An AI Prototype Sprint can then prove the core before a larger Build & Integrate implementation follows.
View this offer€1,950 excl. VAT
Fixed price · one process or up to three AI ideas
- Result
- A reasoned technical direction and build / buy / don't build advice
- Duration
- About two weeks
- Next step
- Build, validate with a prototype first, or deliberately stop
Why 9to5 for this?
9to5 software likes working in existing products and codebases. So AI doesn't have to stay a separate experiment next to the real product.
View casesProduct and technology together
We assess both user value and technical consequences.
Existing software remains the starting point
Auth, data and business logic are reused where possible.
From exploration to real implementation
If the use case holds up, the same engineers can also build the hard technical follow-up.
What else fits your situation?
AI Prototype Sprint · €6,950 excl. VAT
From one concrete AI idea to a working end-to-end prototype that lets you actually test the key technical assumptions.
View offerBuild & Integrate · Fixed price after scoping
You know what needs to be built. We build it professionally, connect it to your existing software and make sure it can be handed over production-ready.
View offerIs our AI idea technically feasible?
You have a concrete AI idea and want to know whether it can really be built with the available models, data, systems and constraints.
View use caseCan AI perform this task reliably enough?
You can see that AI can in principle perform the task, but want to know whether the quality is predictable enough for serious use.
View use case