Simor Data & Knowledge
Data platforms, pipelines, and knowledge infrastructure for AI systems.
The foundations — because production AI is only as reliable as the data beneath it.
50K+
Documents in production corpora
7
Pipeline quality gates per flow
<200ms
p99 retrieval latency target
01 Capabilities
What this division does
RAG data engineering
Document ingestion, chunking strategy, embedding pipeline design, and relevance benchmarking. We treat the corpus as a product — versioned, tested, and continuously improved.
Knowledge graphs
Entity extraction, relationship modelling, and graph databases that give AI systems structured context beyond flat embeddings. Particularly powerful for regulated industries with complex entity relationships.
Pipeline architecture
Batch and streaming pipelines that move data from source systems to AI inference points with quality gates, lineage tracking, and schema enforcement at every stage.
Vector infrastructure
Selection, configuration, and optimisation of vector databases — ANN index tuning, metadata filtering, hybrid search, and cost-performance tradeoffs benchmarked against your data.
02 Approach
How this division works
Data quality is the first bottleneck in every AI system. We start with profiling, lineage, and quality gates before touching models.
Chunking strategy is a design decision, not a default. We benchmark retrieval quality across multiple strategies before committing.
Every data pipeline is versioned and testable. If a regression in output quality is detected, the pipeline can be rolled back like any other piece of software.
03 Use cases
Where this division delivers
Enterprise knowledge base
50,000+ internal documents ingested, chunked with domain-aware strategy, embedded, and served through a hybrid search pipeline combining dense vector retrieval with BM25 keyword scoring.
Regulatory change monitoring
Streaming pipeline monitoring regulatory feeds, extracting obligations into a knowledge graph, and alerting compliance teams when changes affect existing control mappings.
Multi-source data fusion
Batch and streaming pipelines fusing internal CRM, ERP, and external market data into a unified knowledge layer with lineage tracking and quality scoring.
04 Lifecycle
Position in the lifecycle
Simor Group covers the full AI infrastructure lifecycle. This division operates at the Foundations stage.
05 Services
Full service list
- Data pipeline architecture (batch and streaming)
- Knowledge graph construction and modelling
- Retrieval-Augmented Generation (RAG) data preparation
- Vector database selection and optimisation
- Data quality and governance for AI
- Document intelligence and chunking strategy
- Metadata and taxonomy design
07 Engage
Work with Data & Knowledge
Tell us about your system, your timeline, and your constraints. We will come back with a scoping conversation — not a sales pitch.