Business and IT requirements
Moving an AI prototype into operations depends on data access, quality and updates. Duplicated, undocumented or unavailable data complicates production deployment.
Data Intelligence provides data engineering for AI systems: ingestion, preparation, enrichment and updates. It includes pipeline monitoring and traceability alongside the work of data science teams.
Our services
- AI-ready data pipelinesIngestion, cleansing, deduplication, normalisation and enrichment, designed for a stated refresh frequency rather than a one-off extract.
- Retrieval pipelines for assistantsDocument acquisition, chunking, metadata and access-control propagation for retrieval-augmented generation, including the permissions model, which is where most pilots quietly fail.
- Freshness and correctness engineeringExplicit service levels on data age and error rate, measured continuously, because every downstream AI claim inherits both.
- Model and agent observabilityMonitoring input drift, output quality signals and agent actions, closing the loop back to the pipeline that produced the input.
- AI governance enablementDocumentation, lineage and control evidence for AI systems, aligned with the transparency and data governance expectations of the AI Act.
Our scope covers ingestion, retrieval and update pipelines for AI data, with defined service levels.
Typical project needs
- An AI pilot that will not scale for lack of reliable data
- An assistant rollout returning inconsistent answers
- A retrieval project stuck on permissions or document freshness
- Legal or risk questions about AI data provenance
- Business users bypassing IT to build their own extracts
Technologies and standards
- IBM Watson
- Microsoft 365 and Copilot integration
- Databricks, Microsoft Fabric, Snowflake
- Apache Kafka, Airflow
- Denodo data virtualisation
- Python, REST APIs, DB2, Oracle, MongoDB
Assessment and scoping
A 5 to 15 day assessment gives you a map of this scope, a gap analysis and a costed plan.
Proof
Project references
Satisco LAB, internal
An assistant over 2,000 pages of technical documentation
Satisco LAB built an AI assistant using over 2,000 pages of IBM documentation. This internal project tests corpus preparation, updates and response quality.
Retrieval · Curated corpus · Watson AI · Daily internal use
Engagement models
Assessment — 5 to 15 days, fixed price. Flow cartography, gap analysis, costed plan. A short document written to be signed by a decision-maker.
Build — A bounded project. Design, development, testing and cut-over on a defined perimeter.
Run — Recurring. Operations, monitoring, evolution and on-call cover on your critical flows.
Assess your integration architecture
A 5 to 15 day assessment gives you a flow map, a gap analysis and a costed plan. Short, fixed price, written to be signed by a decision-maker.