Enterprise AI. Engineered. Delivered.
Technology consultancy for organisations that need AI in production, not in a pitch deck.
Trusted by enterprises running mission-critical systems — from financial services to Fortune 500.
Atlas
Our modernisation accelerator. Move forward without looking back.
// Atlas — enterprise modernisation engine
// analyse → plan → execute → verify
interface EngineConfig {
source: SystemProfile;
target: TargetPlatform;
documentation: OutputChannel;
tracking: ProjectBoard;
}
async function modernise(config: EngineConfig) {
// Phase 1: deep system analysis
const insight = await engine.analyse({
input: config.source,
output: config.documentation,
mode: "comprehensive",
});
// Phase 2: generate transformation plan
const plan = await engine.plan({
insight,
validation: "continuous",
format: "structured",
});
// Phase 3: execute transformation
const result = await engine.execute({
plan,
target: config.target,
tracking: config.tracking,
});
// Phase 4: verify against baseline
return await engine.verify({
baseline: config.source,
output: result,
onFailure: "auto-correct",
});
} Aegis
Our governance accelerator. Control without compromise.
// Aegis — enterprise governance layer
// detect → evaluate → authorise → record
interface PolicyConfig {
scope: OrganisationUnit;
approvers: Stakeholder[];
retention: "90d" | "1y" | "7y";
}
async function onRequest(req: PlatformRequest) {
// automated risk evaluation
const assessment = await evaluate({
subject: req.subject,
context: req.metadata,
policy: req.applicablePolicy,
});
// structured approval workflow
const decision = await approve({
request: req,
assessment,
workflow: [
"security",
"legal",
"compliance",
"business-owner",
],
});
if (decision.granted) {
// tamper-evident record
return record.session({
principal: req.requestor,
scope: req.organisationUnit,
subject: req.subject,
conditions: decision.conditions,
});
}
} Six practices. One objective.
AI in production. Measurable outcomes. No slide decks.
System Modernisation
Transform complex systems into modern platforms. We handle the complexity your team shouldn't have to.
AI Governance
Full visibility across AI usage. Structured controls. Audit-ready from day one.
Agent Operations
Take AI agents from demo to production. Kubernetes-native. Zero specialist dependency.
Data & Analytics
Replace your BI stack with natural language. Answers in seconds, not analyst-days.
Compliance Engineering
EU AI Act. SR 11-7. GDPR. We build the technical infrastructure compliance requires.
Continuous Intelligence
24/7 anomaly detection that delivers analysis, not alerts.
Purpose-built platforms. Proven in production.
Engineered internally. Deployed at scale. The reason our timelines look different.
System Modernisation
End-to-end transformation. Weeks, not quarters.
AI Governance
Visibility, control, and compliance across the enterprise.
Agent Operations
From prototype to production. Managed, monitored, enterprise-ready.
Enterprise Analytics
Ask questions in plain language. Get answers in seconds.
# platform.yaml — deployment manifest
apiVersion: em.io/v1
kind: Platform
metadata:
name: production-workload
namespace: enterprise
labels:
team: engineering
environment: production
spec:
mode: managed
objective:
description: "Enterprise workload"
lifecycle:
- initialise
- execute
- validate
- report
recovery: automatic
checkpointing: enabled
resources:
scaling: elastic
isolation: tenant-scoped
security:
permissions: least-privilege
network: restricted
audit: enabled
monitoring:
logs: structured
metrics: integrated
alerts: configured Outcomes, not outputs.
Measured against the baselines that matter to your board.
$1.5T in global technical debt. 70% migration failure rate. 47 parallel AI initiatives with no central visibility. 60% of analyst time lost to ad-hoc requests. These are the numbers we move.
Engineering trust, delivering results
Start small. Scale with confidence.
Every engagement starts with a proof of concept. You see the output before you commit to anything larger.
Proof of Concept
2 weeks
See results before you commit.
- Single service or module scope
- Full platform output delivered
- No disruption to current programmes
- Clear go/no-go decision point
Platform Engagement Recommended
6–12 weeks
Most clients start here.
- Full platform deployment
- Dedicated engineering lead
- Integration with your existing stack
- Complete audit trail and documentation
- Knowledge transfer to your team
Enterprise Programme
Custom
Multi-platform, multi-year engagements.
- Multiple platforms deployed together
- On-premises or private cloud
- Cross-platform data flows
- Ongoing engineering support
- Executive programme management
How are you different from Accenture, Deloitte, or McKinsey Digital?
Why not just use ChatGPT, Copilot, or Gemini for this?
We've tried AI code tools before and the output was garbage.
Our system is too complex or unique for this approach.
We'd need to share our source code. Security and compliance won't allow that.
We already have a migration plan or AI governance initiative underway.
What does a typical engagement look like?
What's the typical deal size?
Ready to stop advising and start building?
Pick one service. Two weeks. See the output. No commitment beyond the proof of concept.