Hardware
One GPU server on the premises, sized for the team’s daily volume, with backups and monitored access.
Examples
See AI put to work.
How a sovereign AI system fits together, and where agents take the repeatable work in each team.
Sovereign AI · illustrative example
Client responses drafted on local hardware.
A professional services firm wants first drafts of client responses from its own documents, without those documents leaving the building.
Model
An open model selected for drafting quality and optimised for that server. Fine tuned on approved past responses only where it improves accuracy.
Agent task
Reads the client question, finds the relevant internal documents, drafts a response with sources and routes it to a named reviewer.
Business result
Measured on time to first draft and reviewer edits per response, against a target agreed up front. No per token provider charges for the drafting.
Illustrative example showing how a sovereign AI deployment fits together. Not a client deployment.
By team
Where agents take the repeatable work.
Sales Prepare for deals with less manual research.
Before
Account research, meeting preparation and CRM updates are assembled by hand across separate tools.
With AI
AI assembles account context and drafts proposals and follow ups. Sellers review the work and own relationships and negotiations.
Intended outcomes
Conversion
Better context earlier in the deal
Cycle time
Research, prep and follow up run continuously
Coverage
More accounts without more admin headcount
Marketing Reuse campaign knowledge and connect the results.
Before
Campaign briefs, content and reporting are rebuilt each cycle, with channel results spread across dashboards.
With AI
AI prepares drafts and assembles campaign evidence. Marketers approve the story, brand and claims before publication.
Intended outcomes
Pipeline
More demand activity without more headcount
Speed
Briefs, drafts and setup run continuously
Quality
Brand and claims stay consistent everywhere
Customer Support Give support teams the context to resolve requests.
Before
Teams triage tickets by hand, search separate knowledge sources and chase escalations.
With AI
AI prepares responses and assembles case context. People review sensitive replies and own difficult resolutions.
Intended outcomes
Retention
Issues fixed before they become churn
Speed
Triage in seconds, drafts ready for review
Capacity
More resolved without more headcount
Strategy Keep research ready for the next decision.
Before
Research and market signals are gathered again for each planning cycle or board question.
With AI
AI assembles current research with source links. Strategy teams assess the evidence and own recommendations.
Intended outcomes
Capacity
Broader scanning without a bigger team
Speed
Research and briefs assembled continuously
Quality
Decisions rest on current, sourced evidence
Finance Spend less time assembling financial reports.
Before
Close data, variance commentary and board packs are gathered from spreadsheets and finance systems each cycle.
With AI
AI assembles source linked reports and flags exceptions. Finance reviews the numbers and owns approvals and advice.
Intended outcomes
Decision speed
Close, variance and cash context update live
Capacity
Less time assembling the same pack
Control
Sensitive approvals stay with humans
Operations Track schedules and resolve exceptions sooner.
Before
Teams track process steps by hand, rebuild schedules and discover exceptions after delays occur.
With AI
AI tracks work and flags scheduling or service risks. Operators review exceptions and decide what action to take.
Intended outcomes
Capacity
More throughput without more coordinators
Speed
Exceptions caught and resolved continuously
Quality
SLAs held with fewer surprises
HR Reduce the administration around people decisions.
Before
Applications, onboarding tasks and case records are reviewed and tracked across separate systems.
With AI
AI assembles context and tracks routine tasks. HR reviews the evidence and retains responsibility for sensitive people decisions.
Intended outcomes
Capacity
More roles and people covered without more admin
Speed
Screening, onboarding and follow up run continuously
People up
HR moves from processing to advising
Procurement Connect supplier checks, requests and approvals.
Before
Sourcing, supplier checks and purchase approvals require repeated searches and manual coordination.
With AI
AI assembles supplier context and tracks requests. People approve supplier selection and spending.
Intended outcomes
Capacity
More sourcing covered without more buyers
Speed
Requests, quotes and approvals move continuously
Risk
Supplier exposure watched all the time
Legal Prepare contract reviews with traceable context.
Before
Contracts, renewals and matter status are reviewed and tracked manually across documents and email.
With AI
AI prepares first pass reviews and tracks obligations. Counsel reviews the work and owns advice and sensitive decisions.
Intended outcomes
Capacity
More matters covered without more lawyers
Speed
First pass review and intake happen continuously
Control
Advice and sensitive matters stay with counsel
IT Keep routine requests moving and approvals visible.
Before
Service requests, incident history and access approvals are tracked across tickets, email and asset records.
With AI
AI organises requests and assembles incident context. IT retains approval over access and system changes.
Intended outcomes
Capacity
More requests cleared without more technicians
Speed
Routine tickets resolved continuously
Control
Access and change stay with human approval
Data & Analytics Make data quality and ownership easier to manage.
Before
Data quality, source history and ownership are tracked across separate tools and team knowledge.
With AI
AI helps track quality and source history. Data owners review issues and control what can be used.
Intended outcomes
Capacity
More sources governed without more stewards
Speed
Quality and lineage stay current, not catch up work
Control
What agents may see stays governed and audited
Risk and Compliance Keep control evidence ready for review.
Before
Teams collect control evidence and track obligations manually as audits approach.
With AI
AI assembles evidence and flags gaps. Risk and compliance staff own interpretation, review and sign off.
Intended outcomes
Capacity
Continuous assurance without a bigger team
Speed
Control evidence stays audit ready, always
Risk
Emerging exposure surfaced before it bites
Depth proof
Cybersecurity, already running.
Where we come from, and where the pattern is proven.
69
builds running
10
security functions
12
other team patterns
CISO Office
Board clarity in minutes.
SOC & IR
Triage faster, escalate smarter.
GRC & Risk
Live evidence, not static packs.
Exposure
Findings to decisions.
IAM
Identity review that scales.
OT & CI
One converged picture.
Talk to us
Pick one team. Put AI to work.
Define. Deploy. Run. Engineers on the build.