01
Scenario first
Job and data first. The model is chosen for the scenario, not the other way around.
AI for business
AI for business — in the workflow, not on a slide
We plug models in where the team already loses time: lead triage, drafts, search over a knowledge base, an assistant in the product. No promise to “replace the department”.
What we do
02
In-product integration
Assistant, text review, drafts — inside your site or cabinet.
03
Quality control
A human in the loop where mistakes are expensive. Logs and a clear failure path.
04
Automation around AI
Glue with n8n, CRM, email and messengers when a model alone is not enough.
What’s included
- Scenario map
- Model and constraint choice
- Prompts and pipeline
- Product or process integration
- Error handling
- Team handover
How we implement AI
01
Where AI belongs
Separate jobs where a model helps from jobs that need ordinary automation.
02
Pilot
One narrow scenario on real data.
03
Embed
API, UI, access, request cost.
04
Operate
Who watches quality and what happens when the model is wrong.
Related cases
Frequently asked questions
Do you ship “AI turnkey”?
We ship a scenario. The model is a tool. If a rule and an API are enough, we will not force AI in.
Do we need our own training data?
Often no: good prompts, a knowledge base and constraints are enough. Training your own model is a heavier, separate conversation.
Is this safe for client data?
It depends on policy and the provider. We define what leaves the perimeter, what stays, and who has access.