RAG and enterprise knowledge
Answers from your own knowledge, with the sources to prove it.
Agleam Labs builds retrieval-augmented generation systems that let staff and customers ask questions in plain language and get answers drawn from your documents, wikis, tickets, and databases. Every answer links back to its source, and access rules match the ones you already have.
- Grounded, cited answers
- Enterprise search across systems
- Permission-aware retrieval
- Vector and hybrid search
What RAG does
Make what your organisation knows easy to find.
Valuable knowledge sits scattered across policies, contracts, manuals, shared drives, and old support tickets. Retrieval-augmented generation, or RAG, finds the passages relevant to a question and gives them to a language model, which writes an answer based only on that material. The result is accurate, current, and traceable, unlike a model answering from memory.
The quality of a RAG system depends on the unglamorous work: cleaning and chunking documents, choosing embeddings, combining keyword and semantic search, re-ranking results, and respecting who is allowed to see what. That is where we spend our effort, and we measure retrieval quality directly so you can trust the answers.
What we deliver
Knowledge systems that stay accurate.
We build the full pipeline, from document ingestion to the assistant interface, and keep it in step with your content.
01 Knowledge assistants +
- Cited answers
- Internal helpdesk
- Customer support
- Follow-up questions
Chat interfaces for staff or customers that answer questions from your content, quote the source passage, and say clearly when the answer is not available.
02 Enterprise search +
- Semantic search
- Hybrid search
- Connectors
- Re-ranking
A single search across SharePoint, Google Drive, Confluence, ticketing systems, and databases, ranked by meaning rather than exact keywords.
03 Document ingestion pipelines +
- PDF parsing
- OCR
- Chunking
- Incremental sync
- Metadata
Automated extraction, cleaning, and chunking of PDFs, Office files, scanned documents, web pages, and tables, refreshed as content changes.
04 Vector database design +
- pgvector
- Pinecone
- Qdrant
- Weaviate
- Indexing
Selection, schema design, and tuning of vector stores, with metadata filters and indexing strategies suited to your data volume and query patterns.
05 Permission-aware retrieval +
- Access control
- SSO
- Row-level security
- Audit
Retrieval that honours existing document permissions, so people only see answers drawn from content they are already allowed to read.
06 Retrieval evaluation and tuning +
- Recall testing
- Answer scoring
- Embedding tuning
- Feedback loops
Test question sets, retrieval and answer scoring, and ongoing tuning of chunking, embeddings, and ranking to keep accuracy high as content grows.
07 Structured data Q&A +
- Text to SQL
- Spreadsheets
- Reports
- Read-only access
Natural-language questions over databases and spreadsheets, translated into safe queries, alongside answers from unstructured documents.
Why RAG
Trusted answers, found in seconds.
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Less time searching
Staff get a direct answer instead of opening ten documents to find one paragraph.
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Answers you can verify
Every response links to its source, so people can check before they act.
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Always up to date
Content syncs automatically, so answers reflect the latest policy or price list.
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Respects access rules
Confidential material stays visible only to the people already allowed to see it.
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Faster onboarding
New starters find answers on their own instead of interrupting colleagues.
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Sitting on knowledge that is hard to find? Book a free consultation and we will assess your content with you.
Book a free consultation →
Why Agleam Labs
RAG engineered for accuracy.
Hover or tap a card to see what it means in practice.
How we work
From scattered documents to a reliable assistant.
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01
Audit the content
We review your sources, formats, permissions, and the questions people actually ask.
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02
Build the pipeline
We set up ingestion, indexing, and retrieval, and create a test set of real questions with known answers.
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03
Tune and validate
We measure retrieval and answer quality, adjust the pipeline, and review results with your subject experts.
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04
Launch and maintain
We roll out to users, monitor unanswered questions and feedback, and keep content in sync.
Agleam Labs in numbers
- Enterprise apps delivered
- 50+
- Enterprise apps delivered
- On-time delivery
- 99.8%
- On-time delivery
- Years of senior experience
- 10–30
- Years of senior experience
- Years of DevOps experience
- 20+
- Years of DevOps experience
- Technologies in production
- 20+
- Technologies in production
- Office, worldwide clients
- 1
- Office, worldwide clients
Technology
The retrieval stack we use.
We pick vector stores, embedding models, and frameworks to fit your scale, hosting, and security needs.
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Python
- LlamaIndex
- LangChain
- pgvector
- Pinecone
- Qdrant
- Weaviate
- Elasticsearch
- OpenAI
- Claude
- Cohere Rerank
- Azure AI Search
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AWS
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Azure
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Docker
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Security
Industries
Knowledge-heavy sectors.
Organisations with large volumes of policies, procedures, and technical documentation gain the most from grounded AI.
Pharma
Compliant platforms for pharma operations, distribution, and regulated workflows.
Healthcare
Secure digital systems for data-heavy healthcare workflows and service delivery.
FinTech
Payment, lending, and transaction platforms for always-on financial operations.
Construction
Project, site, and field operations software for construction teams.
Telecom
Digital platforms, integrations, and service systems for continuous delivery.
EdTech
Learning and assessment platforms for institutions, enterprises, and growing communities.
FAQs
RAG and enterprise knowledge questions.
Straight answers about rag and enterprise knowledge with Agleam Labs.
Talk to an expert
Tell us what you are working on. A senior engineer will reply with honest advice and a suggested next step.
Book a call →What is retrieval-augmented generation? + −
RAG is a technique where the system first retrieves relevant passages from your content, then asks a language model to answer using only those passages. It keeps answers grounded in your data and lets every answer cite its source.
Why not just fine-tune a model on our documents? + −
Fine-tuning teaches style and patterns, but is poor at recalling specific facts and cannot cite sources. RAG updates instantly when documents change and shows where each answer came from.
Which document types can you handle? + −
PDFs, Word and Excel files, slides, web pages, wikis, emails, tickets, and scanned documents through OCR. We also connect to databases for structured data.
How do you handle confidential documents? + −
Retrieval respects the permissions in your source systems, data is encrypted, and the whole stack can run in your cloud or on private models if required.
What if the answer is not in our content? + −
The assistant is designed to say so rather than guess. We log these questions so you can see where your documentation has gaps.
How long does a RAG project take? + −
A knowledge assistant over a defined set of sources typically takes six to ten weeks. Enterprise search across many systems is delivered in phases, one source group at a time.
Start a project
Let's unlock your knowledge base.
- A free consultation with a senior engineer
- Clear scope, timeline, and budget
- Confidential, with an NDA on request
- Support after delivery
Prefer email?
info@agleamlabs.comGive everyone a straight answer.
Tell us where your knowledge lives and who needs it. We will propose an approach and a first estimate.