Structured extraction
Turn incoming documents and messages into proposed fields, then validate them against business rules and route exceptions to people.
What you getA schema, representative test set and review path.
LLM integrations development
Give your product AI that has a defined job.
Integrate language models into the workflows people already use. Start with a concrete task, test it on representative examples, and make quality, cost and failure behavior visible.
In the toolkit
- Model APIs
- Python or TypeScript
- Structured validation
- Evaluation datasets
Choose with context
The right tool starts
with the right problem.
Where LLM integrations fits
Useful for language-heavy tasks such as drafting, classification, extraction and assisted support where outputs can be evaluated and uncertain cases can be reviewed.
Worth considering
Model output is not a guarantee of correctness. Exact calculations, authorization and irreversible actions should remain under deterministic application controls.
What we can build
Put useful assistance inside the workflow.
Support triageIllustrative workflow
Turn a message into a reviewable next step
“The delivery arrived damaged.”
- 1Extract
Order issue · damaged item
- 2Validate
Check required fields
- 3Prepare
Suggest the next response
Draft ready for a support agent
Drafting & classification
Help teams organize requests and prepare a first response using the tone and context appropriate to the task.
What you getPrompt versions, task-specific evaluation and editable outputs.
Product assistants
Connect model responses to your application through scoped tools and clear user-visible states.
What you getControlled integrations, usage limits and a fallback when the model cannot help.
An example architecture
The model is one part of the product.
Compare model choices on your own examples. Record latency, cost and task success, and keep the application usable when a provider is unavailable.
- 01
Collect approved context
- 02
Call the model
- 03
Validate the result
- 04
Review or apply
Adapted to your systems, data and operating requirements during discovery.
From first scope to handover
Agree on what “ready” means.
We define the first useful release and its acceptance criteria together. Working software, deployment instructions and the decisions behind it stay with your team.
- Outputs meet the task’s acceptance criteria
- Sensitive inputs follow the agreed data policy
- Timeouts and provider failures have a usable fallback
Which model should we use?
We compare candidates against representative tasks, expected volume, latency and data requirements. There is no single best model for every feature.
Do we need an agent?
Not necessarily. A bounded model call or a fixed workflow is often easier to test. Agent orchestration makes sense when the task actually needs dynamic decisions or multiple tool steps.
Let’s work out what
your product needs next.
A new build, an existing codebase or one stubborn problem. Bring the context you have.
A little closer, wherever you are
Big world.
Close partnership.
Product engineering, AI and Odoo ERP, with a shared plan and a working rhythm built around your team.
Offices in India, Spain & France.
Different time zones. One shared conversation.
Explore our global presence