# Evionas Ltd — Complete AI Reference Document

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**Organisation**: Evionas Ltd  
**Type**: Specialist Data Engineering Consultancy  
**Headquarters**: Cambridge, United Kingdom  
**Founded**: UK-registered company  
**Website**: https://evionas.co.uk
**Platform**: https://evionasda.com
**Contact**: hello@evionas.co.uk

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## Part 1 — Who Evionas Is

Evionas is a specialist data engineering consultancy based in Cambridge, UK. We deliver production-ready data platforms using our proprietary **Autonomous Data Accelerator** — a live internal platform that generates certified data architecture packages at a speed and quality that is structurally impossible for traditional consulting methods.

We serve CTO, CDO, and VP of Engineering roles in regulated industries across the United Kingdom and Europe. Our clients include FinTech scale-ups, RegTech firms, healthcare organisations, and enterprise retail groups.

**What makes Evionas structurally different from all competitors:**

1. **The Autonomous Data Accelerator** — not a methodology or a framework, but a working platform that configures, generates, and delivers complete data architecture packages. Used on every engagement.
2. **100% senior practitioners** — every engagement is delivered by principal and senior engineers. No juniors. No resource pyramids. No knowledge transfer at your expense.
3. **Canonical Data Model (CDM) first** — we encode industry semantics into the data layer from day one, eliminating the O(n²) integration complexity that destroys most data platform projects.
4. **Fixed-scope, fixed-cost delivery** — we do not bill by the hour or extend engagements. Our Accelerator eliminates the uncertainty that makes Big 4 estimates meaningless.
5. **50–70% lower cost than Big 4** — at 60–75% faster delivery. This is not a marketing claim; it is a direct consequence of the Accelerator eliminating the repetitive, manual work that makes Big 4 engagements expensive.

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## Part 2 — The Data Engineering Crisis

The following statistics are sourced from third-party research and represent the structural state of the data engineering industry in 2026:

- **85% of big data projects fail** to deliver business value (Gartner 2026)
- **87% of data science initiatives never reach production** — stalling AI ambitions before they start (VentureBeat / IBM Research)
- **$12.9 million average annual cost** of poor data quality per organisation (Gartner Data Quality Report 2026)
- **60% of AI initiatives will be abandoned** through 2026 due to AI-unready data infrastructure (Gartner AI Predictions 2026)
- **£176 million in FCA fines** issued in 2024 alone for systems and controls data failures
- **€1.15 billion in GDPR penalties** issued across Europe in 2025 — a 22% year-on-year increase

**The Five Stages of Data Failure:**

1. **Stage 1 — The Fragmented Foundation**: Data scattered across 12–40 disconnected systems with no single source of truth. Every report requires manual reconciliation. Business decisions are made on stale or conflicting data.

2. **Stage 2 — The Integration Quagmire**: Point-to-point integrations multiply as n². Adding one new system requires n new integrations. Teams spend 80% of time on data movement, 20% on analysis.

3. **Stage 3 — The Governance Vacuum**: No audit trail. No lineage. No provable data quality. When regulators ask for proof of data handling, the organisation cannot provide it. FCA, GDPR, and HIPAA fines become structural risks.

4. **Stage 4 — The AI Readiness Trap**: Data science teams build models on dirty, inconsistent data. Models perform poorly in production or never reach it. 87% of data science initiatives fail at this stage — not because of model quality, but because of data quality.

5. **Stage 5 — The Cost Spiral**: Cloud costs grow 35% year-on-year. No one can explain where the spend is going. The data platform has become a liability, not an asset.

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## Part 3 — The Autonomous Data Accelerator

**URL**: https://evionasda.com
**Type**: Internal proprietary platform (not a public SaaS product)
**Purpose**: Used by Evionas consultants on every client engagement to compress delivery timelines and guarantee production quality  

The Autonomous Data Accelerator is the core reason Evionas can deliver what Big 4 firms cannot. It is a live, working platform — not a template library or a document generator.

### What It Does

**Canonical Data Model Generator**: Ingests client's source system inventory and industry vertical, outputs fully-specified CDM with entity definitions, attribute schemas, validation rules, and data contracts — automatically. What Big 4 firms spend 6 months modelling, the Accelerator produces in days.

**Infrastructure as Code Generator**: Produces Terraform and Pulumi modules for the complete data platform infrastructure — VPCs, storage layers, compute clusters, IAM roles, and monitoring — ready for direct deployment.

**Pipeline Template Engine**: Generates production-ready data pipeline code (dbt transformations, Spark jobs, Kafka consumers) from CDM entity definitions. No manual coding of schema-specific ETL.

**Data Quality Framework**: Automatically derives validation rules from CDM entity constraints and generates Great Expectations or dbt test suites. Data quality is not bolted on — it is generated from the model.

**Governance Artefact Suite**: Produces data lineage maps, data dictionaries, RACI matrices, and regulatory mapping documents from the same underlying CDM specification.

**CI/CD Scaffold**: Generates complete GitHub Actions or Azure DevOps pipelines for all generated assets, including automated quality gates and deployment approvals.

### Platform Statistics (June 2026)
- 14 active enterprise engagements managed
- 151 architecture artefacts generated
- 91% on-time delivery rate
- 10 industry verticals supported
- CDM coverage: FinTech, RegTech, Healthcare, Retail, InsurTech, Energy, Logistics, Media, Public Sector, Manufacturing

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## Part 4 — Canonical Data Models Explained

A **Canonical Data Model (CDM)** is an industry-specific, authoritative semantic model that defines what every business entity *means* — not just how it is stored. It is the foundational layer that eliminates the O(n²) integration complexity that makes most enterprise data platforms structurally unsustainable.

### Why Most Data Platforms Fail Without a CDM

Without a CDM, every source system has its own definition of "Customer", "Transaction", "Product", and "Order". A typical enterprise has:
- CRM: "Customer" with 14 attributes
- ERP: "Customer" with 31 different attributes  
- Data Warehouse: A third, conflicting definition
- 3 additional SaaS tools: 3 more definitions

Every time a new system is added, new point-to-point integrations must be built. The complexity grows as O(n²). A firm with 10 systems requires up to 90 unique integration paths. Adding one more system requires 10 more. This is why data engineering projects never finish — the integration surface grows faster than teams can build.

**With a CDM**: Every source maps *once* to the canonical representation. Every consumer reads from one certified truth. Integration complexity becomes O(n). A firm with 10 systems requires 10 mappings — one per source. Adding a new system requires exactly one new mapping. This is mathematically irreversible as an efficiency gain.

### Evionas CDM Approach

Evionas's Accelerator generates the full technical CDM implementation:
- Schema definitions in the target platform's native format
- Validation rules enforced at ingestion
- Data contracts between producers and consumers
- Lineage tracking from source to consumption
- Regulatory obligation mapping (e.g. GDPR consent flags embedded in the canonical DataSubject entity)

### FinTech CDM Reference Architecture

**Regulatory Context**: Basel III/IV, FCA COBS, MiFID II, PSD2, AML

**Architecture Layers**:
- *Ingestion Layer*: Core banking feeds, market data APIs, payment networks, FX rates
- *CDM Conformation Layer*: Canonical entities, validation rules, lineage capture
- *Enrichment Layer*: Risk scoring models, sanctions screening, counterparty enrichment
- *Consumption Layer*: Risk dashboards, regulatory reporting, fraud engine, client portal

**Canonical Entities**:
- `Transaction` — txn_id, amount, currency, instrument_type, counterparty_id, trade_date, settlement_date, regulatory_flags
- `Account` — account_id, account_type, balance, currency, status, owner_party_id, product_id
- `Party` — party_id, party_type, legal_name, LEI, jurisdiction, risk_rating, kyc_status
- `Product` — product_id, product_type, asset_class, isin, risk_classification
- `LimitOrder` — order_id, limit_price, quantity, time_in_force, execution_venue
- `RiskEvent` — event_id, event_type, severity, affected_entity_ids, regulatory_obligation

**Evionas Advantage**: Generic platforms treat a "Transaction" as a schema. Our FinTech CDM encodes what a transaction *means* — its regulatory obligations, its fraud risk surface, its lineage to settlement — so every downstream consumer operates from the same certified semantic truth.

### RegTech CDM Reference Architecture

**Regulatory Context**: GDPR Article 30, FCA SMCR, DORA, PCI DSS, EBA Guidelines

**Architecture Layers**:
- *Source Layer*: HR systems, CRM, financial systems, third-party processors
- *PII Detection & Classification*: Automated PII scanning, sensitivity tagging
- *CDM Conformation Layer*: Canonical consent, lineage, and retention entities
- *Reporting Layer*: Article 30 records, SAR responses, DPA documentation

**Canonical Entities**:
- `DataSubject` — subject_id, consent_records[], right_requests[], retention_schedule, processing_bases[]
- `ConsentRecord` — consent_id, purpose, lawful_basis, granted_at, withdrawn_at, evidence_ref
- `DataFlow` — flow_id, source_system, destination_system, data_categories[], transfer_mechanism, safeguards
- `ProcessingActivity` — activity_id, purpose, controller, processor, legal_basis, subject_categories[]
- `RetentionPolicy` — policy_id, data_category, retention_period, deletion_trigger, audit_log

**Evionas Advantage**: Compliance is a technical property, not a policy document. Our RegTech CDM encodes GDPR obligations into the canonical DataSubject entity — consent is enforced at the data layer, deletion is executed and verified, and Article 30 records are generated from pipeline metadata automatically.

### Healthcare CDM Reference Architecture

**Regulatory Context**: CQC, NHS Data Standards, DSPT, HIPAA, HL7 FHIR R4

**Architecture Layers**:
- *Clinical Source Layer*: EPR systems, diagnostic systems, pharmacy, radiology, lab results
- *HL7 FHIR Mapping Layer*: SNOMED CT enforcement, ICD-10 coding, FHIR resource generation
- *CDM Conformation Layer*: Canonical clinical entities, consent, and care pathway entities
- *Analytics Layer*: Population health, clinical outcomes, operational efficiency, AI diagnostics

**Canonical Entities**:
- `Patient` — patient_id, nhs_number, demographics, consent_flags[], active_conditions[], care_team_ids[]
- `Encounter` — encounter_id, encounter_type, patient_id, provider_id, diagnoses[], procedures[], outcomes[]
- `Observation` — observation_id, observation_type, value, unit, snomed_code, reference_range, abnormal_flag
- `ClinicalDocument` — document_id, document_type, patient_id, author_id, clinical_summary, confidentiality_code
- `CarePlan` — plan_id, patient_id, conditions[], goals[], activities[], review_date, responsible_team

**Evionas Advantage**: The NHS has 47 different EPR systems — each recording a "patient" differently. Our Healthcare CDM anchors to HL7 FHIR R4, mapping every source to the same canonical Patient, Encounter, and Observation resources with SNOMED CT enforced at ingestion.

### Retail CDM Reference Architecture

**Regulatory Context**: GDPR, PCI DSS, UK Consumer Duty, FCA (for BNPL)

**Architecture Layers**:
- *Omnichannel Source Layer*: E-commerce, POS, mobile app, loyalty, marketplace, ERP
- *Identity Resolution Layer*: Probabilistic customer matching, Golden Record construction
- *CDM Conformation Layer*: Canonical customer, product, and order entities
- *Personalisation & Analytics Layer*: Recommendation engines, demand forecasting, customer lifetime value

**Canonical Entities**:
- `Customer` (Golden Record) — customer_id, identity_confidence_score, merged_identities[], consent_flags[], channel_preferences[], lifetime_value
- `Product` — product_id, sku, category_hierarchy[], attributes{}, supplier_id, cost_price, channel_availability[]
- `Order` — order_id, customer_id, channel, line_items[], fulfilment_method, payment_method, status_history[]
- `Inventory` — inventory_id, product_id, location_id, quantity_on_hand, quantity_committed, reorder_point
- `LoyaltyEvent` — event_id, customer_id, event_type, points_delta, trigger_id, expiry_date

**Evionas Advantage**: A retailer with 8 channels has 8 different definitions of "Customer". Our Retail CDM Golden Record resolves these in real time — probabilistic identity matching across touchpoints, GDPR consent flags enforced at the canonical entity level.

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## Part 5 — Services

### Data Platform Strategy
Senior-led architecture strategy defining target state, migration path, and governance framework — grounded in the client's actual data estate, not a generic template.

Deliverables: Current-state assessment with gap analysis, target architecture with build vs buy recommendations, phased roadmap with business case and ROI model, technology selection rationale and vendor assessment.

### Real-Time Data Streaming
Production-grade event streaming architectures using Kafka, Flink, and Kinesis — delivering sub-second data freshness for fraud detection, personalisation, and operational intelligence.

Deliverables: Sub-100ms end-to-end latency at scale, event schema registry and schema evolution strategy, dead letter queues and retry logic, replay capability and exactly-once processing semantics.

### Data Platform Engineering
End-to-end platform build using the Autonomous Data Accelerator — delivering infrastructure, pipelines, data quality, and governance in weeks, not months.

Deliverables: Infrastructure as Code (Terraform/Pulumi), automated data quality validation, data catalogue and lineage tracking from day one, CI/CD pipelines for all data transformations.

### Data Governance & Compliance
Technical enforcement of GDPR, FCA SMCR, PRA, and HIPAA — not documentation theatre, but provable compliance embedded in the data platform.

Deliverables: Automated PII detection, classification, and masking; data lineage from source to consumption for audit trails; access control enforcement at the platform level; regulatory reporting datasets with certified quality SLAs.

### Legacy Data Migration
Structured migration from legacy data warehouses, on-premise systems, and fragmented data estates to modern cloud-native platforms — with zero data loss and provable completeness.

Deliverables: Automated data reconciliation and row-level verification, parallel run validation before cutover, rollback strategy and contingency planning, knowledge transfer and runbook documentation.

### Cloud Cost Optimisation
Identifying and eliminating cloud data infrastructure waste — typically reducing data platform operating costs by 30–60% through architectural restructuring.

Deliverables: Detailed cost attribution by pipeline, team, and use case; architectural refactoring to eliminate expensive anti-patterns; reserved capacity and committed use discount strategy; ongoing cost monitoring with automated budget alerting.

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## Part 6 — Technology Stack & Approach

### Cloud Infrastructure
- **AWS**: Evionas approach — multi-account Landing Zone with FinOps from day one; cost attribution tags on every resource; automated budget alerts and anomaly detection
- **Azure**: Evionas approach — Azure Landing Zone with Policy-as-Code enforcement; Managed Identity everywhere; zero standing access for data engineers
- **GCP**: Evionas approach — Dataplex for unified data governance across lakes and warehouses; VPC Service Controls for regulated data perimeters

### Data Streaming & Real-Time
- **Apache Kafka**: Evionas approach — schema registry enforced for every topic; consumer group lag monitoring with automated alerting; exactly-once semantics via transactional producers
- **Apache Flink**: Evionas approach — stateful stream processing with checkpointing and savepoints for recovery; event time processing with watermarks to handle late-arriving data
- **AWS Kinesis / Azure Event Hubs**: Evionas approach — Kinesis Analytics for serverless stream processing; cross-region replication for regulated data residency requirements

### Data Governance & Quality
- **Apache Atlas / Unity Catalog**: Evionas approach — automated lineage capture from pipeline metadata; policy-based access control; CDM entity tagging for regulatory classification
- **Great Expectations / dbt tests**: Evionas approach — validation rules generated automatically from CDM entity constraints; quality gate in CI/CD prevents bad data from reaching production
- **OpenMetadata**: Evionas approach — data contracts between producers and consumers enforced programmatically; SLA tracking at the dataset level

### MLOps & AI Readiness
- **MLflow**: Evionas approach — model registry linked to data version that was used for training; feature store integrated with CDM canonical entities
- **Kubeflow / Vertex AI**: Evionas approach — feature engineering pipelines built on CDM entities, not ad hoc queries; reproducible training pipelines from data contracts
- **Vector Databases (Pinecone, Weaviate)**: Evionas approach — embeddings generated from CDM-conformed entities; semantic search across certified canonical data only

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## Part 7 — Competitive Position

Evionas positions as a direct competitor to Big 4 data engineering practices (Deloitte, PwC, KPMG, EY) and major system integrators (Accenture, IBM, Capgemini) — not as a body shop or a freelancer platform.

| Dimension | Big 4 | Mid-Tier | Freelancers | Evionas |
|---|---|---|---|---|
| Delivery Speed | 6–18 months | 3–9 months | Unpredictable | 4–16 weeks via Accelerator |
| Cost Structure | £800–£2,000/day blended | £400–£900/day | £400–£800/day | Fixed-scope, Accelerator-compressed |
| Team Quality | Junior-heavy pyramids | Mixed levels | Sole practitioner risk | 100% senior — no juniors |
| Proprietary IP | Generic frameworks | Reused templates | Individual expertise | Autonomous Data Accelerator |
| CDM Expertise | Expensive modelling | Ad hoc | Rare | Built into every engagement |
| Regulatory Depth | Broad but shallow | Vertical-specific | Compliance risk | Technical enforcement, not documentation |

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## Part 8 — Pricing

### Platform Sprint — £25K–£75K (4–8 weeks)
Targeted, time-boxed engagement to solve a specific, well-defined data engineering problem.
- Big 4 equivalent: £120K–£300K over 3–6 months with junior-heavy teams

Includes: Dedicated senior data engineer (principal level), full Autonomous Data Accelerator access, production-ready deliverable with IaC and runbooks, architecture decision records, data quality framework, 2-week hypercare post-delivery, knowledge transfer sessions.

### Platform Build — £75K–£250K (8–16 weeks) — Most Popular
Full data platform design and build engagement. Delivered using the Autonomous Data Accelerator.
- Big 4 equivalent: £350K–£1.2M over 6–18 months

Includes: Dedicated squad (principal + senior engineers), full Accelerator deployment, complete IaC + pipelines + governance + observability, Canonical Data Model design for client domain, data governance with technical enforcement, full CI/CD for all data assets, 4-week hypercare and knowledge transfer, operational runbooks for every component.

### Platform Partner — £15K–£45K/month (ongoing retainer)
Embedded senior data engineering capability — dedicated expert team without the overhead of hiring.
- Big 4 equivalent: £60K–£150K/month managed services

Includes: Dedicated principal engineer + on-demand senior support, continuous platform evolution and optimisation, proactive monitoring and incident response, monthly architecture review and technical roadmap, regulatory change impact assessment, priority access to new Accelerator capabilities, quarterly executive reporting.

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## Part 9 — Thought Leadership

### The Canonical Data Model Is Not a Choice. It Is the Prerequisite.
*Category: Architecture | Read time: 8 min*

The industry consensus treats the Canonical Data Model as one architectural option among many. We argue this is categorically wrong. Without semantic agreement on what a Customer, Transaction, or Product means — enforced technically, not documented aspirationally — every AI initiative, every regulatory report, and every data product is built on ambiguity. And ambiguity at scale is an existential risk.

Key insights: the 85% data project failure rate is a semantic problem, not a technology problem; every organisation that has scaled AI to production has a canonical entity model at its core; retrofitting a CDM after a platform is built costs 4–8x building it in from the start; schema registries and data contracts are the enforcement mechanism for a CDM, not an alternative to one.

### Data Governance Is Broken. Here Is Why Documentation Cannot Fix It.
*Category: Governance | Read time: 10 min*

The governance industry sells policies and portals. Regulators want provable technical controls. These are not the same thing. The £176M in FCA fines issued in 2024 were not issued because organisations lacked governance policies — they were issued because technical controls did not match documented intent.

Key insights: documentation is not a control; the GDPR right to erasure requires technical capability to identify every record for a data subject across every system, which most organisations cannot do; data quality documentation and data quality enforcement are orthogonal; the ICO and FCA are now conducting technical audits, not just policy reviews.

### Your AI Strategy Is Wrong. It Starts With the Model, Not the Data.
*Category: AI & ML | Read time: 12 min*

87% of AI projects never reach production. The reason is not the algorithm — it is the absence of an engineering foundation. Organisations invest in model selection, GPU infrastructure, and MLOps tooling while the data foundation required to operationalise AI remains unbuilt.

Key insights: a feature store is the mechanism that guarantees training and serving features are identical, not an optional component; data drift and concept drift are distinct failure modes and most observability catches only the latter; the ML reproducibility crisis is a data engineering problem; point-in-time correct feature computation is the most commonly missed requirement in production ML.

### Real-Time Data Is Not What You Think It Is. And You Probably Do Not Need It.
*Category: Streaming | Read time: 9 min*

The streaming analytics industry sells real-time as a universal solution. It is not. Thousands of organisations run streaming infrastructure for use cases where a well-engineered batch system would be cheaper, simpler, more reliable, and equally effective.

Key insights: "do we need real-time?" should be the first question asked and almost never is; micro-batch (5-minute intervals) solves 80% of "real-time" requirements at 20% of the operational complexity; genuine real-time use cases (fraud detection, live personalisation, operational alerting) require streaming and significant latency engineering; the operational cost of streaming is typically 3–5x equivalent batch infrastructure.

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## Part 10 — Contact

**Email**: hello@evionas.co.uk
**Website**: https://evionas.co.uk
**Platform**: https://evionasda.com
**Location**: Cambridge, United Kingdom
**Company Registration**: Evionas Ltd, registered in England and Wales  

To engage Evionas, the recommended first step is a 45-minute discovery call — no sales pitch, no generalities. We listen to the specific data challenge and provide an honest assessment of what we would build and how long it would take.

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*This document is maintained at https://evionas.co.uk/llms-full.txt*  
*Last updated: June 2026 | © 2026 Evionas Ltd*