Organisations with fragmented private knowledge
Make policies, research, documentation, records, and expertise searchable and usable through governed interfaces.
Grounded retrieval, search, document intelligence, citations, and governed knowledge access
RAG and enterprise knowledge systems
Rokad develops retrieval-augmented generation and enterprise knowledge systems that connect AI to governed, current, source-verifiable information.
Designed for / 01
A focused delivery model for the organisations that need it.
A dependable knowledge system requires more than embeddings. Rokad designs ingestion, parsing, metadata, permissions, indexing, retrieval, reranking, context assembly, generation, citations, evaluation, feedback, and content-lifecycle operations around the organisation's information.
Products requiring source-grounded AI
Add answers, analysis, assistance, and generation that can be traced back to authorised evidence.
Teams with an underperforming RAG prototype
Improve ingestion, retrieval, metadata, permissions, context, evaluation, observability, and content operations.
Challenges / 02
The problems this service is built to solve.
Relevant information is difficult to find
Knowledge is distributed across files, systems, formats, teams, versions, and access boundaries.
Answers sound plausible but lack evidence
Retrieval quality, source authority, freshness, citations, and refusal behaviour are not measured or controlled.
The index does not respect governance
Content access, deletion, retention, versions, source permissions, and audit requirements are disconnected from retrieval.
Capabilities / 03
What Rokad can deliver.
01
Source discovery, connectors, ingestion, parsing, OCR coordination, and normalisation
02
Chunking, metadata, taxonomy, entities, version, freshness, and authority modelling
03
Keyword, vector, hybrid, graph, filtered, and reranked retrieval
04
Permission-aware search and context assembly
05
Grounded generation, citations, refusal, and evidence interfaces
06
Retrieval and answer evaluation, feedback, traces, and failure analysis
07
Content lifecycle, reindexing, deletion, monitoring, and managed operation
Solution components / 04
The system behind the visible product.
Knowledge ingestion
Connectors, extraction, parsing, structure, metadata, versioning, deduplication, quality checks, and update detection.
Retrieval system
Queries, filters, hybrid search, semantic matching, reranking, authority, freshness, permissions, and context selection.
Grounded experience
Answers, citations, excerpts, source navigation, uncertainty, clarification, feedback, and task-specific interfaces.
Knowledge operations
Source health, indexing, access, retention, evaluation, failure review, content gaps, and governance reporting.
Use cases / 05
Where this capability creates practical leverage.
Enterprise knowledge assistant
Answer employee questions across policies, procedures, documentation, research, and approved internal sources.
Customer support knowledge system
Ground support responses and agent assistance in current product, account, policy, and troubleshooting information.
Research and evidence platform
Find, compare, synthesise, and cite information across reports, papers, records, and structured datasets.
Document review workflow
Retrieve clauses, requirements, evidence, differences, precedents, and related records for a defined review task.
Architecture and integration / 06
Designed to fit the wider technology environment.
Authority and freshness
Model which sources are current, approved, superseded, jurisdiction-specific, or authoritative for each task.
Permission-aware retrieval
Carry source access rules through indexing, query, retrieval, context, citation, cache, and audit layers.
Evaluation decomposition
Measure ingestion, retrieval relevance, context sufficiency, answer grounding, citation correctness, and end-task usefulness separately.
Quality and control / 07
Production requirements are part of the build.
Measured behaviour
Representative evaluation data, quality criteria, failure modes, and release thresholds are defined before expanding production use.
Controlled actions
Permissions, policy checks, approval gates, audit trails, fallbacks, and escalation paths govern consequential AI behaviour.
Observable operation
Inputs, outputs, retrieval, tool calls, latency, cost, model versions, and quality trends are monitored appropriately.
Delivery / 08
A controlled path from requirement to operation.
Discover
Clarify the business outcome, users, workflows, constraints, dependencies, risks, and measurable acceptance criteria.
Architect
Define the system boundaries, data, integrations, security, operating model, delivery sequence, and technical decisions.
Build and validate
Deliver in controlled increments with stakeholder review, automated testing, documentation, and production-quality engineering.
Deploy and improve
Launch safely, establish observability and support, then improve the system using operational evidence and user feedback.
Typical deliverables
Knowledge-source, access, task, and risk assessment
Ingestion, metadata, index, retrieval, and permission architecture
Production search, RAG, API, and user interfaces
Source connectors, parsing, indexing, update, and deletion workflows
Retrieval and answer evaluation datasets and dashboards
Governance, monitoring, content-operation, and handover documentation
Engagement models / 09
Use the delivery structure that matches the work.
Fixed-scope delivery
A defined outcome, scope, acceptance criteria, milestones, and commercial structure for a bounded project.
Dedicated product team
A stable cross-functional team delivering an evolving roadmap with shared product and engineering ownership.
Embedded specialists
Specialist engineers working inside an existing product, technology, data, design, or operations team.
Managed evolution
Ongoing reliability, security, maintenance, feature delivery, and roadmap execution after launch.
Related capabilities / 10
Continue through the wider product and technology system.
01
Generative AI
Build the generation and user workflows around retrieved evidence.
02
AI agents
Ground tool-using agents in governed organisational knowledge.
03
AI integration
Connect knowledge capabilities with existing products and systems.
01
Software development
Custom platforms, backends, integrations, operational systems, and software modernisation.
02
Technology consulting and research
Architecture, feasibility, strategy, due diligence, vendor evaluation, and execution planning.
03
Managed technology services
Ongoing maintenance, cloud, security, reliability, support, and continuous engineering.
FAQ
RAG and enterprise knowledge systems
Scope, ownership, assumptions, delivery, security, and long-term operation are clarified before work begins.
01Why does a basic RAG prototype often perform poorly?
Why does a basic RAG prototype often perform poorly?
Common causes include weak parsing, arbitrary chunks, missing metadata, poor queries, unsuitable embeddings, no reranking, stale sources, permission gaps, excessive context, and absent evaluation.
02Can the system cite its sources?
Can the system cite its sources?
Yes. We preserve source identity and location through ingestion, retrieval, context, and output so users can inspect the evidence behind an answer.
03Can access permissions be respected?
Can access permissions be respected?
Yes. Permission-aware retrieval can enforce user, group, tenant, document, field, and source rules, provided the source systems expose reliable access information.
04How is RAG quality evaluated?
How is RAG quality evaluated?
We test ingestion quality, retrieval relevance, ranking, context sufficiency, grounding, citation correctness, refusal, latency, cost, and usefulness on representative questions and tasks.
05Can the index stay current as documents change?
Can the index stay current as documents change?
Yes. We design incremental updates, version handling, deletion, reindexing, connector health, source timestamps, and stale-content monitoring.
AI development
Make organisational knowledge usable without separating it from evidence and access control.
Rokad can assess the sources, tasks, permissions, retrieval quality, and operating model before building the system.
Contact / 05
Bring us the difficult technology problem.
Tell us what you need to build, improve, procure, deploy, or operate. We will respond with a practical next step.