Governed data foundations. Agentic AI in production.
Aeroh works inside large organisations and public institutions — organising fragmented data estates and deploying AI agents that operate under governance, in production.
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Artificial intelligence delivers lasting value only where data is governed, verified and owned. We build the foundation first — then the agents that run on it.
The challenge
Data disorder stalls AI ambition.
Large organisations and public institutions operate with data dispersed across dozens of systems — inconsistent, unverified, without clear ownership or lineage.
Fragmented data estates
ERP, CRM, documentation, operational systems and manual processes — no single source of truth, no governance.
AI without foundation
Pilot programmes that hallucinate, recommendations without context, and systems that fail to scale into production.
Risk without control
No operating model, no monitoring, no defined boundaries — AI becomes an operational and regulatory liability.
Without a firm Data Foundation, there is no reliable Agentic AI. The constraint is not the model — it is the foundation.
Capabilities
Two pillars. One architecture.
From assessment and pipelines through to agents in production — one connected system, not separate engagements.
Data Foundation
Assessment of data condition, quality and AI readiness. Engineering of pipelines, models, lineage and governance.
Agentic AI Systems
Agents that understand organisational data, operate within defined boundaries, and integrate with ERP, CRM and operational systems.
Approach
Phased, controlled, measurable.
Every engagement begins with a clearly defined phase. We recommend Phase 1 or 2 — advanced capabilities and retainer arrangements follow once trust, foundation and operational context are established.
Data Foundation
- Enterprise Data Assessment
- Data Foundation Engineering
Assessment of condition, quality and readiness. Design and engineering of modern data infrastructure with governance.
Agentic AI Systems
- AI Agent Implementation
- Enterprise AI Integration
Development of reliable agents upon organised data. Integration with existing business systems.
Outcomes
Measurable business results.
What clients gain once data is governed and agents run in production — ten concrete outcomes from error prevention to faster, better-informed decisions.
Autonomous detection and prevention of errors
Continuous analysis of data and independent identification of anomalies before escalation — across orders, invoicing, processes and reporting.
Predictive prevention of disruption
Early warning of equipment failure, supply chain disturbance, liquidity pressure and operational risk.
Intelligent internal AI assistants
Agents with full knowledge of organisational history, process and data — answers, analysis and recommendations in seconds.
Real-time analysis and recommendation
Automated monitoring of key metrics, detection of risk and opportunity, generation of actionable guidance.