Why this matters now
Doing this once creates a route every future tool can reuse. New AI features, new suppliers, and new workflows all plug into the same connections instead of starting from scratch.
01
Connect AI to approved information and return its output to the exact case, task, or decision where someone needs it.
02
Shared connections, permissions, and monitoring support every approved AI tool instead of a one-off integration each time.
03
Because the connection stays stable, you can swap AI models, vendors, or hardware later without rebuilding the systems your teams depend on.
04
Rules, approvals, and workflow steps become visible settings your own people can adjust — no-code, without waiting on engineering.
The record of truth still lives in systems built decades ago
The urgency
01 · Context
AI sees approved records along with clear definitions, identifiers, and quality checks — not a spreadsheet export.
02 · Workflow
Output lands in the case, task, or review point where someone can act on it.
03 · Agents
AI can only use the actions you approve, with its own identity, permissions, and sign-off points.
04 · Runtime
AI runs separately from your core software, so models and computing power can change without disruption.
How it fits together
The AI-enablement architecture
Existing system → governed AI use
01 · Authoritative sources
Case systems
State · records · actions
Registries
Entities · identifiers
Document stores
Files · evidence · history
02 · Modernisation boundary
APIs & events
Data contracts
Workload identity
Tool permissions
Observability
Error & retry control
03 · AI orchestration
Retrieval & context
Model routing
Agent policy
Approval gates
Evidence trace
04 · Model runtime
CPU · GPU
or accelerator
Model serving
Memory & storage
Latency & throughput
Runtime telemetry
05 · Operational workflow
Case or task
AI-supported step
Authorised action
A clear path from trusted record to AI output and approved action
The connection layer
AI only sees what it is allowed to see, and can only do what it is allowed to do.
Set it up once and reuse it for every future AI tool or supplier.
Approvals and audit trails stay visible to the people responsible.
Change systems, models, or hardware later without rebuilding workflows.
Teams can also adjust the moving parts themselves. Rules, steps, and approvals are settings rather than code, so operational changes do not need a development project.
Your systems stay in place; the connection is what changes
Readiness explorer
Choose a capability to explore.
01
Trusted data AI can use
02
Reliable connections
03
Visible workflow status
04
Clear permissions for AI
05
Full visibility and audit
06
Right computing power
01 · Readiness capability
Trusted data AI can use
Let AI use your data with the meaning behind it.
AI receives approved records with consistent definitions, identifiers, origin, and quality checks — so what it reads means the same thing it means to your teams.
How this shows up in everyday work
Authoritative source
Government records
Net0 modernisation
Controlled data services
AI layer
Model receives context
Operational use
Relevant, grounded output
Net0 builds
Canonical definitions
Record linkage
Quality controls
Data lineage
Retrieval patterns
Permission-aware access
Enabled outcome
AI can work with real operational records while their source, meaning, and permitted use stay clear.
Computing power
Hardware follows the job:
How big the model is
How many people use it at once
How fast answers must come back
Where the data is allowed to live
A full setup also covers memory, storage, networking, monitoring, security, capacity, and recovery — so the service stays dependable as demand grows.
Matching the job to the setup
Validate before production
Authoritative system
Keeps owning its records and functions, and connects through the controlled layer.
Governed model endpoint
CPU
Fine when the model is modest and response times and volumes are tested and comfortable.
GPU
Used for bigger models, many users at once, high volume, and training or tuning.
NPU
Purpose-built chips can suit specific models, low power, or keeping data on site.
AI assistants
Actions
A defined set of things it is allowed to do
Identity
Its own login, so activity is never anonymous
Limits
Authority that stops where you decide it stops
Progress
Visible status, so nothing runs out of sight
Approvals
People sign off before anything final happens
Record
A trail of everything attempted or changed
How authority works
Permission is set per action. An assistant may read one system, prepare a change in another, and still need a person to approve it.
Bounded agent request path
01
The purpose, the case, and the allowed outcome are clear before anything runs.
02
Only approved records are reachable, and only through the controlled connection.
03
Every available action has clear inputs, validation, and rules for what to do if it fails.
04
Identity, purpose, permissions, and any sign-off requirement are verified first.
05
Safeguards make sure a repeated request cannot cause a duplicate or unexpected effect.
06
Sources, output, action taken, system response, approval, and final status stay traceable.
Everyday data
Approved records, definitions, workflow status, and origin reach AI through one controlled path.
Real work
Suggestions, extracted details, drafts, and assistant results return to the correct case or task.
Safe automation
Each one has its own identity, permitted actions, permission checks, approval points, and full activity trail.
Future-proofing
You can change AI provider, model, or computing setup later while your core systems keep running as they are.
No-code control
Steps, rules, approvals, and thresholds become settings people can adjust, instead of engineering tickets.
Confidence
Teams can trace what was accessed, what AI produced, what it did, who approved it, and what changed as a result.
Start with one system and a decision owner
Tell us which established system matters most and who signs off on change. We will map the records, the governed interfaces, and the approval path from there.
Talk to our team