Document ingestion
Connectors for file stores, wikis, databases, ticket systems and email, with incremental sync.
Grounded answers
Retrieval-Augmented Generation (RAG)
Answers drawn strictly from your approved documents, with citations and permission-aware retrieval.
What this is
RAG is the difference between a system that sounds confident and one you can audit. We build the full pipeline — ingestion, chunking, embedding, hybrid retrieval, reranking and grounded answering with source citations — and we measure retrieval quality separately from answer quality, because they fail for different reasons.
The problem
What this service addresses
If more than one of these is familiar, there is usually a case worth scoping.
- Knowledge is spread across wikis, drives, tickets and PDFs, and search finds none of it.
- An assistant answers from stale or superseded documents with no way to tell.
- Answers carry no sources, so every one has to be verified manually anyway.
- Retrieval ignores permissions and surfaces content the user should not see.
Scope
What we deliver
Every item below is a deliverable you receive, not an activity we perform.
- Document ingestion pipeline across your file stores, wikis, databases and ticket systems
- Chunking and embedding strategy tuned to your document structure
- Hybrid retrieval combining vector similarity with keyword search, plus reranking
- Permission-aware retrieval enforcing your existing access rules at query time
- Grounded answering with inline citations to the source passage
- Retrieval and answer accuracy benchmarks against a fixed question set
- Re-indexing schedule, freshness handling and an admin view for content owners
Capabilities
What the system does
Chunking and embedding
Chunk boundaries that respect document structure, with metadata retained for filtering and citation.
Vector and hybrid search
Semantic similarity combined with keyword matching, so exact identifiers and part numbers still work.
Retrieval and reranking
A reranking pass over candidates to put genuinely relevant passages ahead of merely similar ones.
Grounded answers with references
Every claim traceable to the passage it came from, linked so users can verify.
Access-aware retrieval
Your permission model applied during retrieval, not filtered after the fact.
Evaluation and monitoring
Separate measurement of retrieval recall and answer correctness, plus logging of unanswered questions.
Applied
Where it is used
Each of these is a bounded job with a measurable result, which is how we prefer to scope a first build.
01
Internal knowledge assistant
Staff ask in plain language and get a cited answer from policy, SOP and product documentation.
02
Customer-facing documentation search
Turns manuals nobody reads into an answer engine, with links to the source page.
03
Contract and document Q&A
Ask across a document set and get the clause, the page and the surrounding context.
04
Sales enablement
Accurate product, pricing and policy answers while the representative is still on the call.
05
Support agent assist
Suggests the approved answer and its source to the agent handling the ticket.
Architecture
How it is put together
An indicative architecture. The final design follows your systems, data boundary and constraints.
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01
Sources
SharePoint, Google Drive, Confluence, Notion, databases, ticket systems, PDFs and web content.
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02
Ingestion
Extraction, OCR where needed, structure detection, chunking, metadata tagging and incremental sync.
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03
Index
Vector store plus keyword index, with access-control metadata attached to every chunk.
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04
Retrieval
Query rewriting, hybrid candidate generation, permission filtering, reranking and context assembly.
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05
Answering
Grounded generation constrained to retrieved context, with citations and explicit abstention.
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06
Evaluation
Retrieval recall, citation correctness, answer accuracy and unanswered-question review.
Security & governance
Controls we build in
Access, privacy and oversight are part of the build rather than a later hardening phase.
- Permission filtering applied inside retrieval, so a user cannot reach content they lack rights to.
- Citations on every answer, enabling verification instead of trust.
- Explicit abstention when retrieved context does not support an answer.
- Document freshness tracked; superseded versions removed from the index on re-sync.
- Query and answer logging for audit, with sensitive-content handling agreed in advance.
- Tenant isolation where the system serves multiple customers or business units.
Delivery
Our approach
How we run the engagement
Discovery
Process walkthroughs, data review, constraints and a written scope you sign off before build.
Architecture
Data model, integration boundaries, hosting and the security model, agreed up front.
Proof of concept
A narrow build on your real data against an agreed quality bar, so the go-ahead is evidence-based.
Development
Two-week increments, each ending in a working deployment and a review.
Testing
Automated coverage on critical paths, evaluation sets for AI behaviour, acceptance testing with your users.
Deployment
Staged rollout into your infrastructure with monitoring and a tested rollback path.
Monitoring & support
Quality, cost and latency tracking after launch, with handover documentation or an ongoing retainer.
Proven work
Products we have built and sold
We are product builders as well as a services team. These are our own commercial platforms, not client references.
Digital sales & support agent
SIZH Virtual Agent
A virtual digital agent for sales, support and reception — trained on your business, live on WhatsApp, web, voice and email.
View product Mobility platformRideOn
A ride-hailing and fleet management platform, now with agentic dispatch, driver support and demand intelligence.
View product Multivendor marketplaceUniBooker
A configurable multivendor service-booking marketplace with agentic onboarding, matching and support.
View product
Questions
Retrieval-Augmented Generation (RAG) — FAQs
The questions we are asked most often about this service.
How accurate is RAG in practice?
It depends on your documents, and that is why we measure it. We build a question set with your experts, then report retrieval recall and answer accuracy separately. You see the numbers before go-live and decide whether the bar is met.
What if our documents contradict each other?
A common finding, and the system surfaces it rather than silently picking one. Citations show the conflict, and content owners get a report of the contradictions to resolve.
Does it respect our permissions?
Yes. Access metadata is attached at indexing time and enforced during retrieval, so the model never sees content the user cannot access.
How often is the index updated?
Incrementally, on a schedule or triggered by change events, depending on the source. Frequently changing content can sync within minutes; large archives typically run nightly.
Can it handle scanned documents?
Yes, via OCR in the ingestion pipeline. Quality depends on the scans, and we test a representative sample before committing to an approach.
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