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VigilesAI
Services

Seven practices

We are a Microsoft data and analytics consultancy. That means the semantic layer, the platform underneath it, the pipelines that feed it and the governance that keeps it trustworthy, plus honest advice about which of those you actually need.
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  • Discovery is paid and fixed-price. You keep the output either way
  • We quote the smallest architecture that meets the requirement
  • Documentation and handover are deliverables, not an afterthought
6 to 14 weeks for a first production model

Power BI consultancy and development

Semantic modelling, DAX and report delivery with one agreed definition per measure.

  • One agreed definition for every KPI, enforced in the model rather than in a spreadsheet
  • Reports that open in under three seconds on real data volumes
  • Row-level security that survives an internal audit
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3 to 9 months depending on source count

Microsoft Fabric implementation

Lakehouse architecture sized against measured consumption rather than headcount.

  • A medallion lakehouse with explicit ownership and contracts between layers
  • Capacity sized against measured consumption, with throttling understood before it happens
  • Direct Lake models where they pay for themselves, import models where they do not
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8 weeks for a first domain; longer programmes run in quarterly increments

Azure data engineering and Synapse

Azure pipelines, warehousing and lakehouse engineering built to run unattended.

  • Pipelines that fail loudly, retry correctly and can be replayed for any date
  • Query performance measured and tuned against your real workload
  • Infrastructure and pipelines deployed from source control, never by hand
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4 weeks for inventory; 3 to 12 months for the full programme

BI migration to the Microsoft stack

Structured migration from legacy BI platforms, with rationalisation and a reconciled cutover.

  • A rationalised report set, typically far smaller than the one you started with
  • Business logic consolidated into a governed semantic model instead of scattered across reports
  • Documented reconciliation between old and new before any switch-off
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3 weeks assessment; 3 to 6 months to working controls

Data governance and compliance

Ownership, cataloguing, classification and access evidence, implemented in the tools teams already use.

  • A catalogue people actually search before rebuilding something that exists
  • Classification and sensitivity labels applied consistently, including on exports
  • Access that can be evidenced per dataset, per person, on demand
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2 weeks readiness; 6 to 10 weeks per pilot

AI, Copilot and advanced analytics

Copilot readiness, custom assistants and forecasting, assessed against a fixed evaluation set.

  • Semantic models that answer natural-language questions correctly, with evidence
  • Copilot deployed to a defined audience with cost and data boundaries understood
  • Forecasts and anomaly alerts measured against a baseline, not just shipped
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12-month term, reviewed quarterly, exit assistance included

Managed analytics and support

Monitoring, incident response and change delivery for Power BI, Fabric and Azure platforms.

  • Refresh and pipeline failures caught and fixed before the business notices
  • Small changes delivered in days rather than waiting for the next project
  • Capacity and licence cost reviewed every quarter against actual usage
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Engagement model

Programme structure

Every phase produces something usable on its own, so stopping after one is a legitimate decision rather than a write-off.

  1. Discovery

    2 to 3 weeks

    Decision inventory, source profiling, data quality findings and costed architecture options. Fixed fee, and the output is yours regardless of what follows.

    • Decision and KPI register
    • Source profile and quality findings
    • Architecture recommendation with costed options

  2. Foundation

    4 to 8 weeks

    Landing zone, environments, deployment pipeline and the first end to end reference implementation, which becomes the pattern everything else follows.

    • Provisioned environments
    • Reference pipeline and model
    • Written standards and naming conventions

  3. Delivery

    Quarterly increments

    Domain-by-domain delivery, each validated against the source of record and signed off before the next begins. Value lands early and repeatedly.

    • Production models and reports
    • Reconciliation evidence per domain
    • Updated documentation

  4. Operate

    Ongoing or handover

    Either a managed service with monitoring and agreed response times, or a documented handover to your team with a defined support window.

    • Runbooks and architecture documentation
    • Monitoring and alerting
    • Quarterly architecture and cost review
Next step

Arrange a technical review

A 45-minute working session with an engineer rather than a sales call. You describe the problem; we set out what we would build, in what order, and what it is likely to cost.

Book a technical reviewGet a written estimate

Answered within one working day, by the engineer who would do the work