Pillar 02 · Applied AI
AI that ships to production, inside the systems you already run.
Most AI pilots never leave the sandbox because nobody owned the integration, the governance or the operations. We own all three.
At a glance
Where teams get stuck
Three situations we get called into.
“Our pilot worked. Then nothing happened.”
The demo impressed everyone, but it runs on a laptop with sample data. Nobody owns integration, security or what happens when it is wrong.
“A document-heavy process is eating our team’s week.”
Onboarding packs, KYC files, claims and contracts, read and re-keyed by hand, with every delay landing on a client.
“Compliance won’t sign off.”
Nobody can say what data the model touches, who approved its output or how to turn it off. So the project stalls.
What we do
Four services, one accountable team.
AI Architecture & Readiness
Where AI fits, what data it needs and what it must never touch. A target design and roadmap before any model is chosen.
You get
- Ranked use cases with a go / not-yet for each
- Data and risk review
- Target design and delivery plan
Agentic Workflow Automation
Multi-agent systems for document-heavy processes such as onboarding, KYC, claims and contracts, with human approval gates where liability sits.
You get
- A working workflow on your own documents
- Approval steps with named owners
- A logged record of every automated decision
Enterprise AI Platforms
A shared AI platform on Azure, so every team builds on the same foundations: model routing, prompt and version control, observability and cost management.
You get
- One governed entry point for all AI use
- Usage and cost visibility per team
- A platform your own engineers can run
Responsible AI & Governance
Data sovereignty, audit trails, evaluation and policy controls, so the compliance team signs off rather than shuts it down.
You get
- Data boundaries enforced in the system, not in a policy PDF
- Evaluation results before go-live
- An off-switch and an escalation path
Where we apply it
Typical problems, and what the solution is built to do.
Client onboarding and KYC
Onboarding takes weeks and lives in email.
Agents that collect, classify and check documents, with a person approving before anything reaches core systems.
- Document capture, classification and extraction
- Screening results routed for human adjudication
- One view of where every case is stuck
Intake and conflict checks
New matters wait on checks nobody can see.
Structured intake with parties captured as data, conflict search with a retained result, and explicit approval routing.
- Party and related-entity matching
- A clearance record that can be reconstructed later
- Named accountability at each approval
Claims and document workflows
Manual claims processing takes weeks.
AI-assisted document analysis with human-in-the-loop review.
- Automated data extraction from claim documents
- Anomaly flagging for fraud review
- Audit trail for every automated decision
Knowledge search over your own documents
The answer exists somewhere, and nobody can find it.
Assistants that answer only from approved content, within each person’s existing access rights.
- Retrieval scoped to what the user is permitted to see
- Answers that cite their source documents
- Logged prompts and outputs for accountability
How it runs
From first question to running system.
Readiness
We agree the problem, check the data and risks, and say plainly whether AI is the right tool.
Pilot
A working version on your own data, with accuracy measured against a target you set.
Production
Integration with your systems, security hardening, approvals and monitoring.
Run
We operate and improve it, or hand over to your team with a run guide.
Why the two pillars belong together
AI is only as good as the platform underneath it.
Clean data, secure cloud, reliable pipelines and modern applications are the prerequisites for AI that works. Because we build those too, our AI work doesn’t stop at the demo. It lands in production, on infrastructure we understand.
See IT servicesGuardrails
What we won’t do with AI.
Train on your data
No client data trains models or leaves the boundary you define.
Ship a black box
Where a decision must be explained, we don’t deploy something that can’t explain it.
Remove the human
Where liability sits with a person, a person approves.
Sell AI you don’t need
If a simpler fix solves the problem, we will tell you.