Stage 11 to 2 weeks
AI Opportunity Sprint
Teams who know AI matters but not where to start.
A short, structured discovery that finds the AI opportunities worth pursuing in your business, and the ones that aren't.
Find your AI opportunityEnterprise RAG development
Enterprise RAG: answers from your own knowledge, with sources
We build retrieval-augmented generation (RAG) systems that let people ask questions in plain language and get answers grounded in your documents and data, with citations, permission controls and measurable accuracy.
Every answer links to its source. People only see what they are already allowed to see.
What usually goes wrong
Most organisations already have the answers, in SOPs, policy PDFs, product manuals, tickets and databases. The problem is finding the right paragraph, trusting it is current and knowing whether you are allowed to see it.
Search that finds files, not answers
Keyword search returns forty documents. Staff still read them to find one clause, or ask a colleague instead.
Generic AI tools that make things up
Public chat assistants do not know your policies and will confidently produce plausible but wrong answers without sources.
Knowledge scattered across systems
The answer to one question may span a SharePoint folder, a Google Drive, a ticket history and a database table.
Access rules that must not be broken
HR, finance and customer data have different audiences. A knowledge assistant that ignores permissions is a data leak waiting to happen.
Where Enterprise Knowledge & RAG earns its keep
Internal knowledge assistant
One place for staff to ask how things work (processes, templates, contacts, past decisions) with links to the source.
Policy and compliance search
Precise answers from HR, finance, quality or regulatory policies, quoting the relevant clause and its version.
Product-support assistant
Helps support teams and customers find answers in manuals, release notes and resolved tickets.
SOP copilot
Guides staff through standard operating procedures step by step, in the language they are most comfortable with.
Document question answering
Ask questions across contracts, reports or tender documents and get answers with page-level citations.
What we actually build
Content audit and ingestion
Connectors and parsers for PDFs, Office files, wikis and databases, with de-duplication, versioning and metadata.
Chunking and indexing strategy
Document-aware splitting, embeddings and hybrid keyword + vector indexes tuned for your content types.
Permission-aware retrieval
Access controls mirrored from your source systems and applied at query time, before any content reaches the model.
Answer generation with citations
Grounded answers that quote and link their sources, and say "I don't know" when the knowledge base has no answer.
Assistant interfaces
Web chat, Slack or Teams bots, in-app widgets or an API for your other applications.
Freshness and quality monitoring
Scheduled re-indexing, stale-content alerts, unanswered-question reports and an evaluation set for retrieval and answer quality.
Systems we connect to
The usual suspects. If yours has an API or a database, we can almost certainly work with it.
- Document stores
- SharePoint & OneDrive, Google Drive, Confluence & Notion, File servers & S3
- Structured data
- PostgreSQL, MySQL, ERP & CRM records, Spreadsheets
- Retrieval
- pgvector, Managed vector databases, Hybrid search, Rerankers
- Interfaces
- Web chat, Slack, Microsoft Teams, REST API
How it works, step by step
- 01
Ingest
Documents and records are collected from your sources, parsed, split into meaningful sections and indexed with their metadata and permissions.
- 02
Ask
A user asks a question in plain language, in the interface they already use.
- 03
Retrieve
The system finds the most relevant passages the user is allowed to see, using hybrid search and reranking.
- 04
Answer with citations
The model writes an answer using only those passages and cites each source. If the evidence is missing, it says so.
- 05
Learn
Feedback, unanswered questions and low-confidence answers are reviewed to fix gaps in the content or the retrieval.
Blueprint: HR and operations policy assistant
Reference design, not a client projectThe scenario: A multi-location organisation keeps policies in shared drives. Staff ask HR and managers the same questions every week.
- 1. TriggerQuestion in Teams"How many casual leaves after probation?"
- 2. Rules & checksPermission scopeFilter by role, location and department
- 3. AI stepRetrieve & rerankTop passages from current policy versions
- 4. System updateCited answerAnswer with clause and document link
- 5. Human controlGap reviewHR reviews unanswered or flagged questions
- Trigger
- Rules & checks
- AI step
- System update
- Human control
How we deliver it
Timings are typical for a first release. They depend on scope, how ready your data is and the integrations involved, so we confirm them after discovery.
- 1Week 1
Knowledge audit
We inventory sources, owners, formats and access rules, and collect the real questions people ask today.
- 2Weeks 2 to 3
Retrieval prototype
We index a representative slice and measure retrieval quality against the question set before building the interface.
- 3Weeks 3 to 6
Assistant build
We add permission enforcement, citations, the chosen interface and monitoring, then pilot with a single team.
- 4Ongoing
Scale content
We connect further sources, refine the question set and keep the index fresh as documents change.
Safeguards, built in from day one
Security, privacy, testing and human control are designed in from the start, not bolted on after a pilot. We adapt them to your policies and your risk.
Access control at retrieval time
Permissions are checked in code before content is retrieved, so the model never sees passages the user is not entitled to.
Grounded answers only
Answers must be supported by retrieved sources and cite them. The assistant is instructed and tested to decline when evidence is missing.
Data residency and retention
We agree where indexes and logs live, how long conversation data is kept and which model providers may process it.
Measured quality
A question-and-answer test set tracks retrieval hit rate, faithfulness to sources and refusal behaviour across releases.
What you can expect
We measure how people find information today and compare after launch. Typical aims include:
- Faster answers to routine questions
- Fewer repeat questions to HR, IT and senior staff
- More consistent application of policies
- Visibility into what people ask and where documentation is missing
- Answers people can verify through citations
What we measure
Answer acceptance and feedback, Retrieval hit rate on the test set, Unanswered-question volume, Repeat questions to experts.
We don't promise savings or accuracy figures up front. We measure them on your data during the pilot.
Where it fits best
- Education. Staff handbooks, academic regulations and student FAQs.
- ERP & operations. SOPs, quality manuals and process documentation.
- SaaS. Product docs and resolved tickets for support teams.
- Logistics. Customer contracts, SLAs and route procedures.
Ways to begin
Stage 23 to 5 weeks
Workflow Automation Pilot
One valuable workflow you want to prove before scaling.
A focused pilot that automates a single workflow end to end, running against a measured baseline so the result is a decision, not a demo.
Start an AI pilotStage 36 to 12 weeks
Production AI Build
An AI product or feature you are ready to ship to real users.
A full build from product design to deployment: the AI, the application around it, the integrations and the tooling to run it.
Plan a production buildStage 4Ongoing, monthly
Managed AI Operations
AI systems already in production that need an owner.
Ongoing care for production AI: we watch quality, control costs, handle model changes and keep security reviews current.
Talk about managed AI
Enterprise Knowledge and RAG: questions we get asked
What is enterprise RAG?
Enterprise RAG (retrieval-augmented generation) is an AI system that searches your organisation's own documents and data, then uses a language model to write an answer based only on what it found, with citations and access controls suited to business use.
How do you stop the assistant from making things up?
By retrieving relevant passages first, instructing the model to answer only from them, requiring citations, testing refusal behaviour when evidence is missing and monitoring answer faithfulness over time. No system is perfect, which is why sources are always shown.
Will employees see documents they shouldn't?
No, if permissions are designed in. We mirror access rules from your source systems and filter retrieval by the user's identity before the model sees any content.
Can it answer from databases as well as documents?
Yes. Structured data can be queried through safe, read-only tools or synchronised into the index, depending on freshness needs and data sensitivity.
Do we need to clean up all our documents first?
No. A knowledge audit identifies the sources worth indexing first. The assistant also reveals outdated and conflicting documents, which helps prioritise clean-up.
Where does your team's knowledge live today?
Tell us about your documents and who needs answers. We'll suggest a first knowledge domain and how we'd measure answer quality. We reply within one business day.
We reply within one business day, and we're happy to sign an NDA first. Prefer email? Write to info@originsphere.in.