Representative data engineering and AI projects we have delivered. Each engagement is real — the specifics are generalised to protect client confidentiality.
Enterprise Data Platform Assessment & Modernisation
Logistics — Multi-Market Freight Operator
AWS + Snowflake + dbt + Terraform
12 weeks
A large logistics organisation was running a legacy on-premises SQL Server data warehouse that was slow, expensive, and unable to support new analytics requirements. Reporting latency was 48 hours, data quality was poor, and the engineering team spent most of their time maintaining existing systems rather than building new capabilities.
Challenges
— Legacy on-premises SQL Server warehouse at capacity
Operations teams relied on data analysts for every business question, creating 2–5 day turnaround bottlenecks. Non-technical stakeholders across 7 data domains needed instant, self-service access to insights without SQL expertise.
Challenges
— Non-technical teams waiting 2–5 days for analyst responses
— No self-service capability across 7 operational domains
— Analysts consumed by repetitive ad-hoc queries
— No automated reporting or AI-generated analysis
— Data catalog difficult to navigate without technical knowledge
Outcomes
7 operational data domains accessible via natural language
Query turnaround reduced from days to seconds
200+ automated reports replacing manual analyst work
Self-service analytics for non-technical teams
Estimated £300K+ annual value from analyst time savings
A multi-region field service organisation operated standby technician coverage across 12 regional depots. Scheduling was largely manual and reactive, leading to suboptimal workforce utilisation and increased disruption costs.
Challenges
— Manual standby scheduling across 12 regional depots
Significant improvement in standby block efficiency
Standardised outputs eliminating manual data entry
Estimated £500K+ annual value through improved utilisation
AI ReadinessData ArchitectureAnalytics Enablement
Real-Time Data Ingestion Framework (10+ Sources)
Energy — Distribution Operator
AWS Lambda + SAM + S3 + API Gateway + DynamoDB + Snowflake
12 weeks
An energy distribution company needed to ingest operational data from 10+ external and internal systems into a centralised cloud data warehouse, with varying delivery methods including APIs, SFTP, file upload, and streaming.
Challenges
— Each source had unique formats and delivery cadences
— Varying authentication methods across sources
— No scalable pattern for onboarding new sources
— Error handling inconsistent across integrations
— No metadata tracking for incremental processing
Outcomes
10+ data sources successfully ingested
New source onboarding reduced from weeks to days
99.9% pipeline reliability
Real-time and near-real-time data availability
Processing millions of records daily
Cloud Data EngineeringData ArchitecturePlatform Modernisation
Data Vault to SQL Migration (129 Files, Zero Errors)
An organisation had built a Data Vault 2.0 warehouse using dbt but needed to migrate to native Snowflake SQL for operational simplicity and reduced licensing costs — converting 129 model files across 3 layers with zero tolerance for errors.
Challenges
— 129 dbt model files requiring conversion
— Complex macro, ref, and source resolution needed
— Three architectural layers with different patterns
— Zero tolerance for conversion errors
— Execution ordering must be preserved
Outcomes
129 SQL files converted with zero errors
8 schemas across 5 data marts fully operational
Eliminated dbt licensing and maintenance overhead
Execution time optimised to 6–9 hours sequential
Complete documentation and execution guide produced
A legacy ETL platform running on EC2 was processing overnight batch jobs for multiple data domains. The platform had become costly, difficult to maintain, and a single point of failure with 13 Step Functions, 4 SQS queues, and multiple EventBridge rules as dependencies.
Ground operations teams lacked predictive visibility into vehicle and asset turnaround times, leading to reactive crew allocation when delays occurred at depots. The business needed predictions available before asset arrival.
Challenges
— No predictive visibility into turnaround times
— Reactive decision-making when delays occurred
— No model versioning or experiment tracking
— No production monitoring for ML models
— Complex data ingestion from GraphQL-based operations API
Outcomes
Turnaround time predictions available before asset arrival
Proactive crew allocation reducing delays
MLflow experiment tracking with model versioning
Production monitoring with automatic alerting via Datadog
Continuous model improvement through scheduled retraining
AI ReadinessCloud Data EngineeringAnalytics Enablement
Monthly processing of compliance certificates from multiple suppliers across EU markets required manual extraction from PDFs arriving in varying formats. The process was error-prone, time-consuming, and could not scale as compliance volumes increased.
Challenges
— PDFs arriving in varying formats from different suppliers
— Manual extraction error-prone and time-consuming
— Process unable to scale with increasing compliance volumes
— No structured output for downstream reporting systems
— Multiple markets with different certificate layouts
Outcomes
100+ certificates processed automatically per month
Extraction accuracy >95% across multiple formats
Processing time reduced from hours to minutes per batch
A data warehouse contained hundreds of tables with cryptic column names. Business users could not find or understand data without deep technical knowledge, creating total dependency on specialist engineers for any new reporting.
Challenges
— Cryptic column names across hundreds of tables
— Business users unable to navigate data without engineers
— No documentation of column meanings or relationships
— New analyst onboarding taking weeks
— No semantic layer for natural language query tools
Outcomes
270+ columns documented with AI-generated business descriptions
Semantic YAML model enabling natural language queries
New analyst onboarding reduced from weeks to hours
As the organisation adopted generative AI tools for customer-facing applications, there was no framework for responsible use, content safety, or output validation — creating compliance and reputational risk.
Challenges
— No content safety framework for AI-generated outputs
— No input validation or abuse prevention
— SQL injection risk in AI-to-database query features
— No audit logging for AI interactions
— No role-based access control for AI capabilities
Outcomes
Zero harmful content incidents post-implementation
Full audit trail for regulatory compliance
SQL safety validation blocking DDL/DML in read-only contexts
Token-level rate limiting and abuse prevention
Responsible AI framework adopted organisation-wide
An organisation had a mature data warehouse but no strategy for leveraging AI/ML capabilities. Leadership requested a roadmap for AI adoption grounded in existing data assets with measurable success criteria.
Challenges
— No AI strategy grounded in existing data assets
— Unclear prioritisation of AI use cases
— No technical architecture for AI integration
— Missing governance framework for AI model lifecycle
— No measurable success criteria defined
Outcomes
18-month phased AI roadmap delivered
6 validated use cases with ROI projections
Technical architecture approved by CTO
Phase 1 initiated within 2 months
Estimated £1M+ business value over 3 years
AI ReadinessData Platform AssessmentData Architecture
An industrial operator needed to capture detailed equipment telemetry from a third-party monitoring system to enable predictive maintenance. The vendor API exposed 91+ endpoints with time-series parameters at 1-second resolution.
Challenges
— Vendor API with 91+ endpoints and complex data structures
— Hourly incremental sync across thousands of assets
— State management for parallel processing
— Multiple data stream types requiring different handling
— Historical backfill needed for 180+ days
Outcomes
Real-time telemetry available within 1 hour of operation end
3 data streams fully automated with zero-touch operation
15 concurrent threads for parallel asset processing
Foundation for predictive equipment maintenance
Estimated £200K+ value in maintenance optimisation potential
Cloud Data EngineeringData ArchitecturePlatform Modernisation
FAQ
Common Questions
What industries do you work with?
We work across logistics, retail, manufacturing, energy, financial services, and media. Our solutions are designed to be industry-agnostic — the patterns we build transfer across sectors.
How long does a typical engagement last?
It depends on scope. A focused data pipeline build might take 2–4 weeks. A full platform modernisation or AI strategy typically runs 3–6 months. We always start with a scoping conversation to align expectations.
Do you work with our existing team or independently?
Both. We can embed within your team to upskill and deliver together, or operate independently and hand over a fully documented solution. Most engagements are a blend.
What cloud platforms do you support?
Primarily AWS and Snowflake, with experience across Microsoft Azure and Databricks. We design cloud-native solutions that avoid vendor lock-in where possible.
How do you approach AI readiness?
AI tools are only as good as the data underneath. We assess your current data maturity, fix foundational issues (quality, governance, accessibility), and then build AI capabilities on solid ground.
Can you help with an existing platform that isn't performing?
Absolutely. We regularly assess underperforming data platforms — identifying cost inefficiencies, reliability issues, and architectural bottlenecks — then deliver a prioritised improvement roadmap.
What does a free consultation look like?
A 30-minute conversation where we understand your challenges, discuss what's realistic, and outline potential approaches. No obligation, no sales pitch — just practical advice.
Have a similar challenge?
Book a technical discovery call to discuss your situation. We'll identify the right approach for your context.