01MODERNISE
Rebuild the data foundation.
Migrate legacy warehouses, build pipelines and model the data your business actually runs on — on the platform that fits, not a prescribed one.
Modern data platform
DATA & AI SERVICES
Enterprise Data Modernization for AI-Ready Foundations
Modernise your data foundations, connect fragmented enterprise systems and prepare your information for intelligent applications. Orcaworks combines data engineering, platform modernisation and agentic AI development to turn data investments into practical AI outcomes.
STARTING POINTS
Your data journey. Your starting point.
Engagements begin from your environment, not a fixed package — whether that means a full platform migration, improving what you already run, or connecting AI-ready data to applications.
Modernise the foundation first.
Migrate from legacy databases and warehouses to modern cloud data platforms — architecture, pipelines and modelling rebuilt for what comes next.
DATA ENGINEERING & MODERNISATION
Modern data foundations. Built for what comes next.
Legacy data warehouse migration, platform implementation, ingestion and transformation, architecture and modelling, integration, performance and cost optimisation, governance and quality — delivered as engineering services.
engineering servicesCloud data platform implementation and modernisation.
Migrate
Warehouse migrationWorkload consolidationSchema re-architecture
Engineer
Ingestion & transformationData modellingPlatform integration
Operate
Performance & costGovernance & qualityAI-readiness
engineering servicesLakehouse implementation and modernisation.
Migrate
Warehouse & lake migrationPipeline re-platformingWorkload modernisation
Engineer
Lakehouse architectureIngestion & transformationData modelling
Operate
Performance & costGovernance & qualityML & AI workloads
AI READINESS
A modern data platform is the foundation. AI needs context.
AI applications need more than raw access to data — accessible and reliable information, business relationships, unstructured knowledge, secure retrieval and the ability to interact with operational systems.
- Data preparation, structure and quality
- Document processing and unstructured knowledge
- Enterprise context and relationships
- Governed access and integration
INTEGRATION
Connect data, knowledge and the systems where work happens.
Integration across warehouses and lakehouses, enterprise databases, ERP and CRM systems, document repositories, SaaS platforms and APIs — spanning both retrieval for context and actions in operational systems.
read path
Retrieve for context
Agents read what they need to reason — warehouse tables, documents, records and knowledge.
Data warehouseDocument repositoriesEnterprise databasesKnowledge bases
systems → context → agents
write path
Act in operational systems
Approved actions execute where work happens — updates, approvals and records, under governance.
ERP & CRM updatesApprovals & ticketsOperational APIsNotifications
agents → action → systems
warehouses · databases · erp & crm · documents · saas · apis — connected, not copied
FROM DATA TO AI
Don't stop at modernising your data. Put it to work.
A data engineering engagement can end at a better platform — or extend into working agentic applications that run on the context it creates.
02PREPARE & CONNECT
Turn data into usable context.
Structure, govern and connect the information — entities, relationships and documents — that agents need to understand the work.
Governed enterprise context
03BUILD
Assemble the application.
Agents, workflows and a human workspace come together into a working application — built on the context you just connected.
Working agentic application
04OPERATE & IMPROVE
Run it under governance.
The application executes with human approval and a full execution trace — and production evidence feeds the next deliberate improvement.
Production execution · traceability
ENGAGEMENT
From your current environment to your next AI application.
The scope follows your existing environment and intended outcome — an assessment, a defined modernisation project, AI-readiness work or a specific agentic application.
Aentry point
Data landscape & readiness assessment
Understand where your data stands, what it can support and what needs to change before AI workloads.
Bentry point
A defined migration or modernisation project
A scoped engineering engagement — legacy to Snowflake, Databricks or another modern platform.
Centry point
AI-readiness & integration work
Prepare an existing platform and connect the systems where work happens — retrieval and action paths.
Dentry point
A specific agentic application
Start from a concrete workflow and build the application on the data and systems you already have.
GET STARTED
Make your data ready for what comes next.
Start where you are — a modernisation project, an AI-readiness effort or a specific application.
Data Platform Modernisation
Migration, data engineering and platform modernisation projects — on the platform that fits.
Data to Agentic AI
Prepare data and systems for AI applications — or go straight to building one.