From governed data to intelligent business workflows.
Enterprise AI is not a standalone technology layer. It sits within an ecosystem of data, business processes, enterprise systems and governance. Datacogence brings these components together to design AI solutions that can move from proof of concept to production.
Solution modules
Five capabilities, one proposition.
Each solution stands alone, but they are designed to compose — agents on top of contracts, contracts on top of governance, governance on top of a real organisational model.
Agentic AI for Financial Services
Financial institutions operate where decisions depend on fragmented information, complex regulations and tightly controlled processes. We build agents that navigate those processes while preserving human oversight, governance and auditability.
02Custom Agentic Solutions
Every enterprise has workflows involving search, document interpretation, system-hopping, rule application and decisions. We design agents around the workflow rather than forcing the workflow into a generic AI product.
03Data Contracts
Explicit agreements between data producers and consumers covering ownership, semantics, schema, quality, availability, lineage, versioning and controls. Contracts turn data from an informal dependency into an engineered product.
04Data Governance
Catalogues, lineage, business glossaries, data quality, master data management and organisational metamodels. Governance should enable AI, not become another layer of bureaucracy around it.
05Data & AI Foundations
The information architecture beneath operational AI — what can be trusted, who owns it, what it means, which actions are authorised and how they can be audited.
06AI Governance & Responsible AI
The policies, controls and architecture required to use AI responsibly where data, decisions and accountability matter.
Platforms
Enterprise platforms. Practical architecture.
We work across leading cloud, data and AI ecosystems to help organisations select, integrate and operationalise the technologies appropriate to their environment.
Next step
Design an AI solution that can operate in production.
Start with the business problem. We will work backwards to the data, architecture and controls.