DigiEdze AI · AI Data Engineering
AI is Only as Intelligent as the Data Beneath It
Production AI needs pipelines that deliver current, permissioned, well-described data including the unstructured majority that traditional warehouse programmes never touched.
- data pipelines
- lakehouse
- streaming
- unstructured data
- data quality
- lineage
- governance
Reference architecture
- 06Source systems — ERP · CRM · DB · SaaS · files
- 05Ingestion — batch & streaming
- 04Structured & unstructured processing
- 03Lakehouse — governed single copy
- 02Quality, lineage & catalogue
- 01Feature, embedding & index serving
What Changes When This Goes Wrong?
The data the use case needs lives in a system nobody owns and nobody documented.
Unstructured content — the majority of enterprise knowledge — was never in scope for the data platform.
Nothing propagates deletions or permission changes into the AI index, creating a compliance exposure.
AI Data Engineering Capability Grid
Ingestion for AI workloads
Batch and streaming pipelines from ERP, CRM, ITSM, databases, object stores and SaaS APIs, built for freshness requirements the use case actually has.
Unstructured data pipelines
Parsing, layout-aware chunking, OCR, table extraction and deduplication — the work that determines whether retrieval succeeds.
Lakehouse foundations
Open table formats with schema evolution, time travel and a single governed copy serving both analytics and AI.
Data quality and contracts
Validation at the boundary, with contracts that make an upstream breaking change visible before it reaches an agent.
Lineage and cataloguing
Provenance from source system to embedding, so any AI answer can be traced back to its origin.
Permission propagation
Entitlements and deletions carried through to every derived index in near real time.
How it works
This is the data foundation layer that makes enterprise data usable and trustworthy for AI.
Technology
- Open table formats
- Streaming platforms
- Document parsing
- Apache Atlas and comparable catalogues
- Data quality frameworks
- timbr.ai semantic modelling
Use cases
Where This Lands in Practice?
Retail & CPG
Product, inventory and demand signals unified into one governed layer serving both BI and agents.
Energy & Utilities
Sensor streams joined to asset records and maintenance history for reliability intelligence.
BFSI
Documents, transactions and reference data brought into one permissioned corpus for retrieval.
Outcomes
What We Measure
Data Foundation Outcomes tracks whether the underlying data is actually reliable enough to build on — current, consistently delivered, and well governed.
- Data freshness against requirement
- Pipeline reliability
- Quality rule pass rate
- Lineage coverage
Questions
AI Data Engineering, Answered Directly
Do we need a lakehouse before doing AI?
No. You need governed access to the specific data your first use cases need. Build the platform incrementally behind real use cases — a two-year foundational programme that ships no intelligence loses its funding before it finishes.
Why does unstructured data need separate treatment?
Because retrieval quality is decided at parse time. Tables flattened into prose, lost headings, undeduplicated revisions and missing permission metadata cause failures that no downstream model choice can repair.
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Enterprise RAG & Search
Access-aware retrieval over the knowledge your business runs on.
ExploreControl
AI Governance
Policy-as-code enforced at the point of action, with audit evidence.
ExploreChoose your starting point
Where Are You Today?
Enterprise AI conversations stall when the entry point is wrong. Pick the sentence that matches your situation.
We have an idea.
→ AI STRATEGY
We have a POC.
→ AI PRODUCTIONIZATION
We have an AI application.
→ LLMOPS / AGENTOPS
We need autonomous workflows.
→ AGENTIC AI
Our AI is too expensive.
→ AI FINOPS
Our AI can't reach enterprise knowledge.
→ RAG & KNOWLEDGE
We can't send data to public AI.
→ SOVEREIGN AI
We need to run AI securely.
→ AI SECURITY
Build Your AI Data Foundation
Data is siloed and fragmented in enterprise, but we lay that foundation for you. Begin your data foundation with us.
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