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.

Prometheus Group
DCN
MindBoard
Prisma Tech
Axiom
Synergy
TechFlow
Nexus AI
Prometheus Group
DCN
MindBoard
Prisma Tech
Axiom
Synergy
TechFlow
Nexus AI
Prometheus Group
DCN
MindBoard
Prisma Tech
Axiom
Synergy
TechFlow
Nexus AI

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,
    });
  }
}
What we do

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.

Our accelerators

Purpose-built platforms. Proven in production.

Engineered internally. Deployed at scale. The reason our timelines look different.

Atlas

System Modernisation

End-to-end transformation. Weeks, not quarters.

Aegis

AI Governance

Visibility, control, and compliance across the enterprise.

Kiro Agent

Agent Operations

From prototype to production. Managed, monitored, enterprise-ready.

ConverSQL

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
Impact

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.

Faster discovery
Cost reduction
Self-service rate
Shadow AI surfaced
What our clients say

Engineering trust, delivering results

Richard Callahan

CTO, Meridian Capital Group

"Atlas did in 5 weeks what our consultants couldn't do in 14 months. The output spoke for itself — our team finally had clarity on the path forward."

Anita Rao

VP Engineering, Cloudbridge Systems

"Kiro Agent took our AI agents from impressive demos to production workloads. Our DevOps team manages them with standard tooling now — no AI specialists required."

Thomas Eriksson

Chief AI Officer, Nordic Telecom

"When our board asked how many AI tools were in use across the company, nobody could answer. Aegis gave us complete visibility in two weeks — it was 4x what we thought."

Laura Fitzpatrick

Head of Analytics, Cargoway Logistics

"ConverSQL cut our time-to-insight from days to seconds. Our analysts went from answering ad-hoc SQL requests to actually doing strategic work for the first time."

Michael Okonkwo

CISO, Asclepius Health

"The audit trail Aegis provides is exactly what our compliance team needed. When regulators ask about our AI usage, we have a complete, defensible record."

Catherine Nguyen

Director of Platform Engineering, Vectral Energy

"We tried building our own agent infrastructure. Six months in, we switched to Kiro and had production agents running in a week. Should have started here."

Engagement models

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
FAQ

Frequently Asked Questions

The questions we hear most — and the honest answers.

How are you different from Accenture, Deloitte, or McKinsey Digital?
They advise. We build. Consultancies bring people and process. We bring proprietary platforms and an engineering team that has already solved this problem in production. The difference is speed and accountability — we deliver output, not hours.
Why not just use ChatGPT, Copilot, or Gemini for this?
Those are general-purpose AI assistants. Our platforms are production systems engineered for specific, high-stakes enterprise problems. The difference is the same as the difference between a general practitioner and a surgeon. These platforms have the specialisation, failure handling, audit trails, and enterprise system integration that general AI tools don't have and weren't designed to have.
We've tried AI code tools before and the output was garbage.
Those tools work at the surface level. Atlas works at the system level — understanding the full picture before making any changes. Every step validates against what came before. That's why the output holds up in production.
Our system is too complex or unique for this approach.
That's exactly the environment these platforms were designed for. Simple, well-documented systems don't need Atlas — teams can handle those themselves. Our platform was built for systems where the complexity has outgrown the documentation. The harder the problem, the more our approach outperforms manual alternatives.
We'd need to share our source code. Security and compliance won't allow that.
We can run our platforms entirely within your environment — on-premises or in your private cloud. Nothing leaves your infrastructure. We've done this for financial services clients who have the same constraint.
We already have a migration plan or AI governance initiative underway.
We can run a parallel proof of concept on a single service or module — 2 weeks, no disruption to your current programme. If our platform produces better results faster than your current approach, you have a decision to make. If it doesn't, you've lost nothing.
What does a typical engagement look like?
Every engagement starts with a proof of concept — 2 weeks, scoped to a single service, module, or data source. You see exactly what the output looks like before committing to anything larger. From there, a full platform engagement typically runs 6 to 12 weeks. Enterprise programmes with multiple platforms are custom-scoped.
What's the typical deal size?
Engagements start at $50K for a scoped proof of concept and scale up to $100K+ for full platform deployments depending on complexity and scope. Every engagement begins with a defined PoC so you see the output before committing further.

Ready to stop advising and start building?

Pick one service. Two weeks. See the output. No commitment beyond the proof of concept.