Architecture / Cloud / Enterprise AI

Clarity before
commitment.

Independent architecture advice for the decisions that shape your next platform, migration or AI initiative.

Discuss your decision Explore our reviews

Business priorities. Technical evidence. A clearer next step.

Enterprise & solution architectureAzure & cloud transformationIntegration & practical AI

A focused starting point

Make the next
decision count.

When the design looks finished but the questions remain, a scoped review helps you examine the evidence while there is still time to act.

01 / Architecture

Architecture Review

Identify the assumptions, trade-offs and risks that matter before a platform decision or supplier handover.

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02 / Cloud

Azure Migration Readiness

Establish what needs to be true for a migration to earn business acceptance, beyond a successful deployment.

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03 / Enterprise AI

Enterprise AI Readiness

Move one AI workflow towards a controlled pilot with clear data access, tool authority and evaluation.

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Scope, deliverables and fees are agreed before each engagement.

Experience in context

Complex systems.
Human decisions.

Professional experience across legal services, public services and research brings a practical perspective to the architecture questions your team faces.

Legal services

DocumentsControlled ingestionReview workflow

Controlled document intelligence

Architecture work involving LLM-assisted document review and contract analysis, alongside secure e-discovery ingestion.

Architecture focus: Identity, encryption, retention and the boundary between AI assistance and authorised action.

Public services

Citizen needsConnected servicesRecommendations

Connected citizen services

Architecture and delivery work on a citizen recommendation engine using decoupled services and Azure-based integration and data components.

Architecture focus: Service boundaries, accessibility, security testing and the operational needs of a public-facing service.

Research & life sciences

Research dataCloud pipelinesML workflows

Research data to ML workflows

Architecture work connecting research repositories with cloud machine learning platforms across Azure and AWS, including batch and real-time data flows.

Architecture focus: Data movement, orchestration and the handover from experimentation into repeatable ML operations.

Selected examples from Alexander Marinov’s professional experience. Illustrative workflows; no client endorsement implied.

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Free working guide / 4 pages

Find the gaps.
Then decide.

Nine architecture-readiness checks covering acceptance, interfaces, security, recovery, operations, cloud, AI, cost and decision ownership. Includes an action worksheet.

Download the free checklist

No registration required.

MGCA / Readiness

Can the design
be defended?

  1. Outcome & acceptance
  2. Boundaries & dependencies
  3. Identity & data protection
  4. Failure & recovery

Evidence. Owner. Next action.

How we work

From uncertainty
to a useful decision.

MGCA connects business requirements with integration, security and operations, bringing delivery experience across complex enterprise programmes.

01 / Understand

Agree the decision.

Define the business goal, system boundary, constraints and the people who need to accept the result.

02 / Examine

Follow the evidence.

Review assumptions, credible alternatives and dependencies. Make unresolved risks visible.

03 / Move forward

Own the next step.

Leave with prioritised recommendations, explicit trade-offs and clear actions for your team.

See how an engagement works ↗

Your next move

What decision
are you facing?

Start with your business goal, the main systems involved and your expected timescale.

Start an enquiry ↗marketing@mgca.co.uk

Please share a non-confidential summary first. Questions before you begin?