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Mid-Market Enterprise

Fix the foundation first,
then the AI becomes possible

Fourteen source systems into one governed lakehouse in sixteen weeks — owned and operated by the in-house team from handover.

  • Professional services
  • Operations
  • Finance
  • Compliance
  • Microsoft Fabric
  • Power BI

The pattern is remarkably consistent: fourteen systems that do not speak to each other, four different definitions of revenue, a monthly reporting cycle that takes four days of manual work and is stale on arrival, and a management team that has stopped believing any number a data system produces. Two AI projects have usually been attempted already, and both stalled for the same reason nobody named at the time: the data underneath them was never ready.

We audit the estate before designing anything, build a medallion architecture with a tested transformation layer, agree the definitions that end the four-revenue-numbers problem, and hand the whole thing over with documentation and training. The AI comes after that, and it works, because by then there is something underneath it worth building on.

Capabilities

What we build for mid-market enterprise

Each capability below is delivered through named assets that already exist. Figures are delivered results unless marked otherwise.

Data estate audit

A full inventory of sources, quality issues and competing business definitions before any architecture is drawn.

  • Every source system catalogued with its quality profile
  • Competing definitions surfaced and reconciled explicitly
  • 3-week fixed-scope AI-Ready Blueprint

Medallion lakehouse build

Bronze ingestion, Silver cleaned and conformed, Gold business-ready, then consumption.

  • 14 source systems into 1 governed platform
  • 16 weeks from legacy sprawl to production
  • Each layer purpose-built, documented and independently testable

Unified semantic layer

Agreed definitions of revenue, customer, project and cost, respected by every downstream system.

  • One definition per business term, documented and versioned
  • Ends the four-different-revenue-numbers problem permanently
  • 3× faster reporting cycle, monthly to weekly

Automated data quality

A dbt transformation layer with tests running on every refresh, catching degradation early.

  • 180+ automated tests on every pipeline refresh
  • Version-controlled, reviewable transformations
  • Failures surface at the pipeline, not in a board pack

Executive reporting

Live Power BI dashboards on governed data rather than a monthly manual assembly.

  • Live refresh replacing a four-day manual cycle
  • Every figure traceable to source through full lineage
  • Built on agreed definitions, so numbers reconcile

Knowledge transfer and handover

Documentation, runbooks and hands-on training built into the engagement, not sold afterwards.

  • 100% in-house ownership at handover
  • Full dbt documentation and workspace runbooks delivered
  • No standing dependency on Marzal Labs to operate or extend

Named assets

The inventory we draw on

Twenty-one products, diagnostics, agent patterns, platform components and governance frameworks. Filter by category.

  • PolicyCompareIQClause-level policy and slip comparison
  • ClassifierIQAsset classification with a firm-specific memory loop
  • D&O AnalyzerIQEight-vector D&O underwriting research
  • TriageIQSubmission intake scoring and routing — 2026
  • AI-Ready BlueprintThree-week fixed-scope data estate diagnostic
  • AI Readiness AssessmentData, technology, capability and governance scoring
  • Data & AI Roadmap DesignSequenced, costed initiative plan with owners
  • AI Operating Model DesignReviewer roles, escalation paths, HITL workflow design
  • Technology Stack AdvisoryPlatform selection against your estate and constraints
  • AI Agent Architecture & DesignTask boundaries, tool permissions, failure modes
  • Document Processing AgentsMulti-format ingestion across email, PDF and ACORD
  • Underwriting & Triage AgentsAppetite scoring, capacity checks, context packs
  • Claims Processing AgentsClassification, routing and loss-narrative extraction
  • RAG Knowledge BasesRetrieval over your wordings, guidance and precedent
  • Modern Data Platform DesignMedallion architecture on Fabric, Databricks or Snowflake
  • Data Governance FrameworkOwnership, lineage and automated quality rules
  • Data Quality Programmes180+ automated tests running on every pipeline refresh
  • Underwriting & Claims AnalyticsSemantic layer and agreed portfolio definitions
  • Databricks Partnership ServicesLakehouse delivery under the Bronze partnership
  • AI Compliance & GovernanceFCA, Lloyd’s, EU AI Act and GDPR in one register
  • ROI & Value FrameworkBaselines, KPIs and attribution set before build

Partnerships

The platforms behind the delivery

Databricks Bronze partner, with production delivery on every platform named here.

  • Microsoft AzureCloud, HITL pipelines, Azure OpenAI
  • Azure AI FoundryModel catalogue and agent tooling
  • DatabricksBronze partner · Delta Lake and ML
  • Google CloudCloud platform and data services
  • Anthropic ClaudeDocument reasoning
  • OpenAIExtraction and analysis
  • Microsoft FabricUnified analytics and OneLake
  • SnowflakeData cloud analytics
  • LangChainAgent orchestration
  • dbtTransformation and testing

Get one set of numbers the whole business trusts, then build the AI on top of it.

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