How you deploy AI determines whether it stalls or delivers
We help companies move ‘above the line’ to unlock ROI from AI
Companies focusing on below-the-line use cases
Most companies still focus on 'below the line' use cases: AI as bolt-on tools rather than a change to how work gets done. Chatbots, point solutions, and AI features on top of existing systems are useful, can even lift efficiency, but are never going to move the needle on business value or free up real capacity
Where the return on investment is from AI
We think most of the value will be made above the line: end-to-end workflows that cut across teams and systems. That takes a shift in mindset, from AI as a tool that automates one or two steps in a process you still run, to agents running whole workflows in production and calling on people for validation and control
Shared context is the first step to cross the line
Crossing the line means moving from single-player to multi-player AI, where AI systems become centrally orchestrated in an organisation. A shared context layer, centralising explicit & implicit knowledge readable by agents & humans, is the first step on this journey. It then becomes the foundation on which end-to-end workflow automations can be built
From evidence to adoption
A prioritised list of workflows to automate, ranked on evidence
Your company’s context, as the foundation for your AI agents & systems
Everything below this line happens again for every workflow
One foundation for AI across your business
On top of your Context Platform, we build AI agents and systems aimed at automating complex, end-to-end workflows. Each one draws on the same company knowledge, connections, and permissions, so every new build starts from what is already there
We choose models for each use case, balancing quality, cost, and privacy The system is built around your business, not one model provider
Your Context Platform holds your knowledge bases, business rules, and connections Each build starts with your business already in context, rather than starting from zero
Permissions define who can use each system, what information it can access, and which actions it can take You decide where AI can act independently and where people must approve
We select the right level of automation with AI agents and systems – balancing control, human judgement, and efficiency The result is AI that carries out work, not just answers questions
Context Platform Governed AI agents and systems for the workflows that matter
Turn fragmented financial work into controlled action on cash and performance
Prioritise overdue accounts using value, age, dispute history, and customer context. Prepare the next action, route disputes to the right owner, and track commitments through to resolution
Coordinate inputs, flag missing evidence and anomalies, prepare reconciliations, and draft management commentary with a clear review trail
Protect margin before the deal is signed
Check proposed pricing, discounts, and terms against commercial policy and target margins. Route exceptions and prepare the evidence needed for approval
Bring together requirements, previous answers, company evidence, and commercial input. Coordinate contributors and prepare a tailored response for review
Resolve the exceptions that stop work moving
Identify work that has stalled, bring together the relevant information, determine the likely cause, and coordinate the next action across teams
Turn incoming demand, capacity, and operating constraints into a prioritised plan. Prepare the information each team needs to complete the work
Move customer cases from first contact to accountable resolution
Classify the request, assemble the customer history, determine the next action, prepare the response, and update the relevant systems after approval
Gather the supporting evidence, check the case against policy, prepare a proposed resolution, and route exceptions for human judgement
Control spend before the next supplier decision
Identify mismatches between purchase orders, invoices, and receipts. Gather the missing information, contact the appropriate owner, and track the exception until it is resolved
Compare supplier offers, identify obligations and renewal points, surface material commercial differences, and prepare the negotiation briefing
What we believe about deployment
Business value is not created when a tool is delivered. It is created when people use it in real work. That is why users shape the build from day one
If the infrastructure is not there, we say so and build it first, pragmatically, starting from your first use case
We use whichever model is best for the job, and that changes fast. The model is a component, not the foundation - you can swap it at any time
After go-live, we keep the system healthy by monitoring it and fixing what breaks. Your team uses it, approves what it proposes, and decides what it may do
Inference in your own cloud account, at cost. When applicable, EU AI Act documentation is produced as part of the build
With every use case, the measures of success are agreed before the pilot. After go-live, we report adoption and value against them