The next phase of finance is not only about doing existing work faster. It is about using better-connected data, automation and AI to understand changing numbers sooner, improve forecasting and working capital, strengthen decision-making and find new ways to improve performance and profitability.
Enterprise Knowledge & Work
Enterprise Knowledge Intelligence
Make approved organisational knowledge usable within applications, search, analysis and AI-assisted work.
Internal Workflow Automation
Connect requests, information, approvals and actions across the systems involved in internal work.
Knowledge-Connected Applications
Build applications that bring relevant enterprise information into the task, request or decision where it is needed.
AI-Assisted & Agentic Workflows
Enable AI to retrieve context, use approved tools and execute bounded steps where the requirement supports greater autonomy.
A working example
From Employee Request to Controlled Action
A reference scenario showing how enterprise knowledge, business systems and AI can work together to understand a request, retrieve approved context and support or execute defined actions — with permissions, policy and human review applied where required.
The Architecture Is Only Part of the Outcome
A workflow like the one above depends on more than giving AI access to enterprise knowledge. What it can retrieve, understand and act upon has to reflect the people, information, systems and controls involved in the work.
01 — Knowledge & Context - Is the information current, authoritative and sufficient for the task — and can the system distinguish relevant context from everything else available?
02 — Identity & Access - What can this person, role or workflow legitimately see and use? AI should not create a new route around existing access boundaries.
03 — Retrieval & Grounding - How should relevant knowledge and system information be retrieved so responses and actions are grounded in approved enterprise sources?
04 — Tools & Actions - Which APIs, functions and systems can the workflow use, and what actions should those tools actually be permitted to perform?
05 — Autonomy & Human Judgement - Should AI assist, recommend, execute a defined step or coordinate multiple steps? The level of autonomy should follow the requirement and consequence.
06 — Audit & Observability - Can the organisation understand what information was used, what action was taken, why it happened and where human intervention occurred?
Built Around the Technology Involved
We can work with existing enterprise systems, knowledge sources, identity environments and appropriate AI model ecosystems — selecting the engineering approach around the work, information and level of action required.
Enterprise Systems & Knowledge
Document Repositories · Knowledge Bases · Intranets · HRIS · Service Systems · Business Applications · Databases
AI & Model Ecosystems
OpenAI · Anthropic Claude · Google Gemini · appropriate open or specialised models
Knowledge & Integration
APIs · Webhooks · Search · Retrieval / RAG · Vector Search · Structured Data · Documents · Events
Engineering & Control
Applications · Workflow Orchestration · Tool / Function Calling · Authentication · Permissions · Guardrails · Observability
The technologies shown are examples. The architecture and technology choices depend on the systems, information, workload, security requirements and level of autonomy involved.