Add AI to your existing app
We add AI to the software you already run: retrieval over your own data, agents inside your existing stack, and the architecture that keeps it maintainable.
Call it. An AI answers. That's the demo.
Number coming soon
Verify us
- GitHub: Link coming soon
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- Press: Link coming soon
What it does
Adding AI to an existing app means putting a language model to work inside the software you already run, not replacing it. We add retrieval over your own data, agents that act through the APIs your app already has, and the architecture patterns that keep the result testable and maintainable. This is where the AI automation and custom software pillars meet.
Three patterns cover most requests: a question-answering layer over your data (retrieval-augmented generation), agents that take actions through the APIs your app already exposes, and workflow steps (classification, extraction, drafting) dropped into existing screens.
Details to followMedia pending
What it is wired into
- Named in the build plan
- Named in the build plan
How long until it is live
- 1
Timeline set in the working session
Timeline set in the working session
What we measure
- Agreed in the build plan
Every deployment carries a written ROI commitment.
How pricing works
Pricing depends on which modules you deploy, the integrations involved, and how much is custom to your operation. Most clients start with a single module and expand once it proves out. You'll get a clear, itemized quote in the working session.
Common questions
Do we have to replace our software to add AI?
No. The model works inside the software you already run: it reads your data, acts through the APIs your app already exposes, and drops into your existing screens.
What are the three patterns you use?
A question-answering layer over your own data (retrieval-augmented generation), agents that take actions through your app's APIs, and workflow steps such as classification, extraction and drafting placed into existing screens.
Will it keep working when the model changes?
That is the point of the architecture work: the model sits behind an adapter, your data stays in your systems, and the retrieval and evaluation layers are yours, so a model swap is a configuration change, not a rebuild.
Where does our data go?
It stays in your systems. Retrieval reads from your own data at answer time; nothing is copied into a third-party training set.
How do we know it is working?
Every deployment ships with a written commitment on the numbers it is meant to move, measured against a baseline we agree on before the build starts.