Open navigation menu

Knowledge and Retrieval Systems

Make the organisation's knowledge findable, citable and usable by AI.

Every assistant, agent and search experience depends on the same thing: retrieving the right, current and permitted knowledge. Most retrieval projects fail on content quality, permissions and evaluation rather than on the model.

We build the knowledge and context layer once, as shared infrastructure, so every AI use case can reuse it.

The problem

What usually gets in the way

  • Documents are duplicated, outdated and spread across SharePoint, drives and systems.
  • RAG prototypes return confident answers from the wrong version of a policy.
  • Search ignores permissions, so sensitive content cannot be indexed.
  • Each AI project builds its own index, multiplying cost and risk.

What we deliver

Capabilities

01

Content audit and curation

Find authoritative sources, remove duplicates and set ownership and review dates.

02

Ingestion and chunking

Process documents, tables and scans (including Arabic) into retrievable units.

03

Hybrid retrieval

Combine keyword, vector and metadata search, with re-ranking for precision.

04

Knowledge graphs

Model entities and relationships where questions need connected facts.

05

Permission-aware indexing

Carry source access rights into the index and enforce them at query time.

06

Retrieval evaluation

Measure whether the right passages are found before measuring answers.

Delivered by

Services involved

The service lines combined to deliver this solution.

How it fits together

Reference architecture

A vendor-neutral outline. Components are selected for your workloads, skills and regulatory context.

  1. ConsumersAssistants and agentsEnterprise searchAPIs for applications
  2. Retrieval servicesHybrid search and re-rankingGraph queriesPermission filtering
  3. Knowledge storesVector indexSearch indexKnowledge graph
  4. Content pipelineConnectors to sourcesParsing, OCR and chunkingMetadata and freshness

How we engage

Start small, prove value, scale

  1. 01Assess

    2–4 weeks

    Audit sources, permissions and priority questions.

    • Source inventory
    • Content quality findings
    • Target design

  2. 02Pilot

    6–10 weeks

    Index priority sources with evaluation and permissions.

    • Retrieval service
    • Evaluation baseline
    • Content owner workflow

  3. 03Scale

    Ongoing

    Add sources and consumers on the shared layer.

    • Enterprise context layer
    • Freshness monitoring
    • Reuse across use cases

Outcomes

What we measure

  • Retrieval precision and recall on test questions
  • Share of answers citing current sources
  • Content with an owner and review date
  • Number of use cases reusing the layer
  • Time to find information

Where it applies

Example use cases

  • Government

    Legislation and circular search with version control.

  • Energy and Utilities

    Engineering standards and maintenance-manual retrieval.

  • Banking

    Product, policy and procedure knowledge base.

  • Healthcare

    Protocol and guideline retrieval for clinical staff.

Accelerators

Reusable assets we are building

Delivered today as services. These accelerators are being developed from that work to shorten future engagements.

In development

Connector and chunking templates

Tested ingestion for common enterprise sources and Arabic documents.

In development

Retrieval test harness

Question sets and metrics to compare retrieval designs objectively.

Technology

Platforms we design around

Vendor-neutral: examples of platforms commonly used for this solution. Names identify technologies only and do not imply a partnership.

  • Elasticsearch
  • Pinecone
  • Weaviate
  • Milvus
  • Qdrant
  • LlamaIndex
  • SharePoint
  • Neo4j
See the full technology ecosystem →

Insights

Further reading

What Leaders Need to Know About RAG

Retrieval-augmented generation does not make AI accurate. It moves the risk out of the model and into your content, your search and your permissions, which is where leaders can actually control it.

Read →

Knowledge Graphs, Vectors and GraphRAG

Graph versus vector is the wrong argument. The right one is about which questions your users ask, whether your entities already exist in systems of record, and whether permissions survive every copy of the data.

Read →

The Enterprise Context Layer

Models are now interchangeable; context is not. Organizations that build context once, as a governed shared layer with permissions attached, will reuse it across every assistant and agent. Those that wire it per project will rebuild it every time.

Read →

RAG, Fine-Tuning or a Custom LLM?

Retrieval owns facts. Tuning owns behaviour. Almost nobody should train their own model. And in 2026 the market itself has started making the decision for you.

Read →

Talk to us about Knowledge and Retrieval Systems

Start with a short assessment: we will rank the opportunities, check readiness and agree how success will be measured.

Start a conversation

Next solution

Predictive Analytics

Forecasts and risk scores that are trusted enough to act on.

Continue