AI doesn't cure data chaos
Most corporate AI pilots are limited not by the model, but by the data: documents are in different repositories, directories are divergent, contracts are not classified, access does not correspond to roles, reporting is collected manually, and data owners are not identified.
Therefore, a correct AI project begins with a map of sources: what data is needed, who is the owner, what quality rules are, where are the personal data, what information security restrictions and what answers should the future assistant give.
Data foundation for AI
Sources
ERP, 1C, SAP, DWH, BI, EDMS, documents, applications, knowledge bases, meetings, API and external directories.
Quality
Duplicates, completeness, versions, dates, classification, reference books, normalization and relevance rules.
Rights
Linking AI responses to real user rights in source systems.
Control
Logs, response quality metrics, sources, errors, request costs and user feedback.
Let's discuss your environment
Describe the task, current systems, constraints, and expected results. We will offer a practical first step: diagnostics, pilot, audit, roadmap or project team.
