FINCONSULT GROUP
AI for the finance function
Embed AI in verifiable finance scenarios: document extraction, anomaly detection, variance narratives and knowledge work. Decisions and postings remain governed.
Discuss the task ↗The situation we start with
An impressive prototype fails on real documents, exceptions and access rights. We first define tolerated error, controls and the action after the model response.
AI with evidence, boundaries and human oversight
- Scenario map and cost of error
- Evaluation set using real data
- Human-in-the-loop and escalation paths
- Quality monitoring and decision log
After discovery, we agree the first-wave scope, outcome owner, data sources and acceptance criteria. Cost and timing depend on the actual scope.
Our approach
From the question to a decision.
- 01
Map the finance operating model
Map decisions, reports, sources, manual hand-offs, controls and the finance calendar.
Process and gap map - 02
Shape the first wave
Choose a scenario with a measurable problem, available data and an accountable owner.
Business case and pilot scope - 03
Prove it in a real cycle
Configure the process and integrations, run normal and exception cases, and reconcile the outcome.
Pilot with acceptance evidence - 04
Embed it in management
Hand over roles, rules, training, monitoring and an improvement cadence. Scale after readiness is proven.
A governed operating system