Service
AI Consulting for Business
A survey of the processes the organization already runs, selection of those with enough volume and rule structure to automate, and a proof of concept measured against the current process — with acceptance criteria defined before it starts. The recommendation may well be not to apply AI: when the gain does not pay for operating the model, saying so is the deliverable.
What is delivered
What is delivered
Evidence of delivery
Per-process feasibility report, measured proof-of-concept result and usage policy.
- Mapping of candidate processes, with volume, decision rule and current cost
- Feasibility analysis per process: available data, integration effort and risk
- Proof of concept measured against the current process, under agreed acceptance criteria
- Integration architecture with the systems the organization already runs
- Usage policy, personal data handling and automated decision logging (LGPD)
- Operating plan: who monitors it, how it is corrected and when it is switched off
Scope
What's included
Each track is contracted on its own or combined. Whatever does not fit your operation stays out of scope — and out of the price.
Readiness assessment
The state of data, integration and team. Answers whether the organization has a base to automate at all — before discussing which model.
Use case discovery and prioritization
Candidate processes surveyed with volume, decision rule and current cost, ranked by gain over effort.
Measured proof of concept
A test against the real process, with acceptance criteria agreed before it starts. Failing is a valid — and cheap — outcome.
Process automation and agents
Automation of the routines that passed, integrated into the system the organization already uses — not into a parallel portal.
Copilot over your own data
An assistant answering from the company documents and history (RAG), citing the source on every answer — not a generic chat.
Assisted service desk
Triage and first response on inbound channels, with human escalation defined up front rather than discovered in a complaint.
Data and integration
Preparing the base, the connectors and the history without which any model answers well in a demo and badly in production.
Governance and data protection
Usage policy, personal data handling, automated decision logging and an explicit line between what the machine decides and what a person decides.
Model operation
Who monitors it, how it is corrected, when it is retrained and under what condition it is switched off. An ownerless model degrades quietly.
Team enablement
Training for whoever will operate and review it, so that vendor dependency is not the outcome of the project.
Next step