Service · RAG Pipeline Development
RAG Development Services for Enterprise Knowledge
Answers from your knowledge, with the receipts.
Octobit8 builds retrieval-augmented generation (RAG) systems that let AI answer from your documents, databases and policies, citing the source every time and respecting who is allowed to see what.
General AI models don't know your price lists, SOPs, clinical protocols or course material. Ask them anyway and they guess, confidently. RAG fixes this by retrieving the right passages from your own content and giving them to the model at answer time. Done well, it's accurate, current and auditable. Done badly, it retrieves the wrong page and sounds just as sure. The difference is engineering.
01 / Fit
Who is this for
Common profiles that get the most out of this service.
- 01
Enterprise knowledge assistants
One search-and-answer interface across SharePoint, Google Drive, Confluence, Notion, wikis and shared drives.
- 02
Customer-facing answer engines
Grounded assistants for websites, apps and WhatsApp that answer from your official content only.
- 03
Document intelligence pipelines
Extraction and Q&A over PDFs, scanned forms, contracts, invoices and medical reports.
- 04
Structured + unstructured RAG
Systems that combine SQL queries over your databases with document retrieval in one answer.
- 05
Agentic RAG
Retrieval that plans multiple searches, compares sources and asks follow-up questions before answering.
- 06
Graph RAG
Knowledge-graph-backed retrieval for content where relationships matter, such as regulations or product catalogues.
02 / Principles
What sets it apart
Principles we apply on every engagement—so results are measurable, not just delivered.
Ingestion that respects structure
Layout-aware parsing of PDFs, tables, slides and scans with OCR, so headings and tables survive.
Smart chunking
Chunk sizes and boundaries tuned to your content type, with metadata for source, date, owner and access level.
Hybrid search
Semantic vector search combined with keyword (BM25) search, so exact codes, names and numbers are found.
Re-ranking
A second-stage re-ranker that puts the most relevant passages first.
Citations and grounding checks
Every answer links to its sources, and answers not supported by retrieved text are flagged or refused.
Access control & freshness
Document-level permissions synced from your source systems, plus incremental sync for new and changed documents.
03 / Scope
Capabilities & deliverables
Concrete workstreams we plan, execute, and hand over with documentation and dashboards.
Enterprise knowledge assistants
One search-and-answer interface across your internal content.
- SharePoint, Google Drive, Confluence, Notion, wikis
- Role-based access synced from source systems
- Unanswered-question reports
Customer-facing answer engines
Grounded assistants for websites, apps and WhatsApp.
- Answers from official content only
- Citation links on every response
- Escalation when confidence is low
Document intelligence & agentic RAG
Extraction, Q&A and multi-step retrieval over complex documents.
- Layout-aware PDF and scan parsing
- SQL + document retrieval in one answer
- Multi-search planning for complex questions
04 / Deliverables
What you get
Concrete deliverables from this engagement.
A production RAG pipeline with scheduled sync from your sources
Search and chat interface, API, or both
Evaluation dataset and accuracy report
Admin dashboard for sources, usage and unanswered questions
Documentation and handover
Additional benefits
05 / Process
How we work with you
A phased approach with clear artifacts—so stakeholders see progress weekly, not only at launch.
- 01
Content audit
We inventory your sources, formats, volumes, permissions and update frequency.
- Source inventory
- Access-control map
- Update-frequency plan
- 02
Question set
With your team we write 50–200 real questions and correct answers to measure against.
- Golden question set
- Accuracy baseline target
- 03
Pipeline build
Ingestion, indexing, retrieval and generation tuned against that question set.
- Working pipeline
- Retrieval precision report
- 04
Interface and integration
Web app, Slack or Teams bot, WhatsApp, API or embedded widget.
- Live interface
- API docs
- 05
Launch and monitor
Usage analytics, unanswered-question reports and feedback loops that tell you which content to fix.
- Analytics dashboard
- Content-gap report
06 / Industries
Use cases by industry
Where teams in our focus industries are already applying this service.
Travel & Hospitality
- Agent-desk assistant that answers from supplier contracts, visa rules and fare conditions
- Guest assistant that answers from hotel policies, menus and local guides
- Sales assistant that finds the right package from hundreds of itineraries
Healthcare
- Clinical protocol and formulary search for doctors and nurses
- Patient-record summarisation and question answering for care teams
- Policy and compliance assistant for hospital administration
EdTech
- Curriculum-grounded tutor that answers only from approved course material
- University knowledge base covering regulations, research and technology databases
- Faculty assistant that finds past papers, rubrics and lesson plans
07 / Stack
Stack & integrations
Representative tools—we meet you where your stack already lives and document every handoff.
Embeddings & vector stores
Re-ranking & frameworks
Managed options
Evaluation
08 / Considerations
Good to know
Data handling
- Enterprise model endpoints that do not train on your data
- Deployable fully inside your own cloud account
- Multilingual embedding and generation, tested per language you need
Accuracy is measured, not assumed
- Target agreed with you and measured on your own question set before launch
- Reports show exactly where content gaps are
- RAG or fine-tuning — or both — chosen based on what your content actually needs
09 / Engagement
Engagement models
Discovery & Assessment
Fixed fee, 1–2 weeks — for teams unsure whether RAG or fine-tuning fits their knowledge base.
RAG Proof of Concept
Fixed scope and price, 3 weeks, working prototype on your own documents.
Managed AI Operations
Monthly fee to monitor, evaluate and improve a live RAG system as content grows.
Starter offer
RAG Proof of Concept
3 weeks · [₹ / $ amount — confirm before publishing]
Up to [500] documents, one knowledge assistant. Includes ingestion, hybrid search, a cited-answer chat interface, and an accuracy report on 50 of your real questions.
10 / FAQ
Frequently asked questions
Will our data be used to train public models?
How accurate will it be?
Can it handle Hindi and other Indian languages?
RAG or fine-tuning?
Ready to talk specifics?
Share your goals, timelines, and stack—we will propose a scoped next step.
Often paired with