Give your AI the context it needs to be right.
We design lakehouse and data-product architectures, enterprise retrieval-augmented generation (RAG), and knowledge graphs that ground AI in trusted, permission-aware information, so answers are accurate, cited and current.
Cited
Answers grounded in source documents
Permission-aware
Retrieval respects existing access rights
Measured
Retrieval and answer quality tracked continuously
What is retrieval-augmented generation (RAG)?
Retrieval-augmented generation is a technique where an AI model retrieves relevant information from your own documents and databases before answering, then cites those sources. RAG reduces hallucinations, keeps answers current without retraining the model, and respects document-level access controls when engineered correctly.
AI is only as good as the context it can reach. We build the governed data foundations, retrieval and knowledge layers that make AI accurate.
Our Data & AI Platforms capabilities.
Enterprise RAG & search
Hybrid keyword + vector retrieval, re-ranking, chunking strategy and citation UX.
Knowledge graphs & GraphRAG
Entity and relationship models that let AI reason across connected business data.
Lakehouse architecture
Modern data platforms on Databricks, Snowflake or cloud-native lakehouses with open table formats.
Data engineering & pipelines
Streaming and batch pipelines, data contracts and quality checks feeding AI and analytics.
Data governance
Catalogues, lineage, classification and access policies ready for AI consumption.
Model fine-tuning & serving
When retrieval is not enough: fine-tuning, distillation and efficient model serving.
Where it pays back first.
High-value starting points we see across industries. We will help you rank them against your own data, systems and risk appetite.
01
Enterprise knowledge assistants
Policy, product and procedure answers with citations.
02
Analytics copilots
Natural-language questions over governed metrics and semantic layers.
03
Customer 360
Unified customer context for agents, service teams and personalisation.
04
Research & compliance
Search and summarisation across contracts, filings and regulations.
A clear path to production.
Assess
Audit data sources, quality, ownership and access controls.
Architect
Design the platform, retrieval layer and governance model.
Build
Deliver pipelines, indices and data products with quality checks.
Evaluate
Measure retrieval precision and answer faithfulness continuously.
Tools we ship with.
Selected on merit for your context. We are model-, cloud- and vendor-agnostic.
- Databricks
- Snowflake
- Apache Iceberg
- dbt
- Apache Kafka
- Postgres
- pgvector
- Pinecone
- Weaviate
- Elasticsearch
- Neo4j
- Unity Catalog
- Airflow
- Spark
- vLLM
Data & AI Platforms: your questions answered.
Use RAG when answers depend on changing or private knowledge and need citations. Consider fine-tuning when you need a consistent format, tone or specialised behaviour that prompting cannot achieve. Many production systems combine both.
Retrieval is filtered by the same permissions as the source systems, sensitive fields are classified and masked, and every query is logged. Access is enforced before content reaches the model, not after.
Ready to put Data & AI Platforms to work? Start with one workflow.
Book a 45-minute strategy session with a Vidura principal. You'll leave with a shortlist of high-ROI AI use cases, the risks to manage, and a realistic path to production.