Wikis & Documentation
ConfluenceNotionSharePointInternal wikis
Enterprise Knowledge Retrieval
Secure Internal RAG Systems for Your Teams
Give employees one place to ask a question and get a trusted, source-cited answer from your own documents, without opening five systems to find it. Internal RAG Systems
Permission-aware answers from your own data
Hybrid retrieval, source citations, and access controls built in, so teams trust every answer.
Grounded answers with source citations
Permission-aware retrieval every employee can trust
Connectors to the systems your knowledge already lives in
Retrieval that scales with your document volume
Overview
Enterprise knowledge retrieval is a secure internal AI assistant, built on retrieval-augmented generation (RAG), that lets employees query your company databases, wikis, knowledge bases, and private documents in plain language.
KriraAI designs and builds these internal RAG systems for IT, security, and software teams, returning permission-aware answers drawn only from your trusted sources.
The system indexes your content into a vector database, retrieves the passages most relevant to each question using hybrid search, and passes those passages to a language model that answers with citations back to the original document. Because the model answers from retrieved evidence rather than memory, employees stop stitching answers together from scattered files, tickets, and Slack threads.
McKinsey estimates that employees spend about 1.8 hours per day, roughly 9.3 hours per week, searching for and gathering information, so the business outcome is straightforward: faster decisions and less rework across the teams that depend on internal knowledge.
What KriraAI's Internal RAG Systems Give Your Teams
Each capability below is built into the delivery, not sold as an add-on. Together they define what a KriraAI enterprise knowledge retrieval build actually does for the people who use it every day.
Grounded answers with source citations
Every answer is generated from passages retrieved out of your own content, and each response links back to the exact document, page, or ticket it came from. We use hybrid retrieval, combining dense vector search with keyword (BM25) matching, plus a cross-encoder reranking step so the most relevant passages reach the model first.
Built into delivery
Enterprise-ready
Source-cited answers
Permission-aware retrieval every employee can trust
The assistant respects the access rules you already enforce, so a user only ever sees answers built from content they are allowed to open. We propagate access control lists from source systems such as SharePoint, Confluence, and Google Drive, and enforce role-based and document-level permissions at retrieval time.
Built into delivery
Enterprise-ready
Source-cited answers
Connectors to the systems your knowledge already lives in
Your knowledge is spread across wikis, drives, ticketing tools, and databases, so the retrieval layer connects to all of them through APIs and secure connectors rather than asking teams to migrate content. Content stays where it is, and the index updates as source documents change.
Built into delivery
Enterprise-ready
Source-cited answers
Retrieval that scales with your document volume
As your content grows, the system keeps answers fast through incremental indexing, so only new or changed documents are re-embedded rather than the whole corpus. We build on production vector databases such as pgvector, Qdrant, Weaviate, or Milvus, chosen to fit your data volume and latency targets.
Built into delivery
Enterprise-ready
Source-cited answers
Rollout, training, and adoption support
A retrieval system only pays off if people use it, so we deliver it with usage documentation, admin controls, and structured onboarding for the teams that will query it daily. Adoption is measured through query logs, so you can see which teams and topics the assistant is actually serving.
Built into delivery
Enterprise-ready
Source-cited answers
Security and compliance built for regulated data
The system is architected for sensitive internal data, with role-based access control, encryption in transit and at rest, and a timestamped audit trail of every query and retrieved source. Deployment can run inside your own cloud VPC or on-premises.
Built into delivery
Enterprise-ready
Source-cited answers
How It Works
The build follows a fixed sequence from source audit to a tuned, production system your team owns.
1
Discovery and source mapping
We audit your knowledge sources, document the existing permission model, and prioritize the systems and content types the first release will cover.
2
Ingestion and indexing
Connectors pull content from each source, parsing and OCR handle unstructured files, and documents are chunked, embedded, and written into the vector database with their metadata and access rules attached.
3
Retrieval and generation pipeline
Each question triggers hybrid search and reranking to select the strongest passages, which are passed to the language model with guardrails that require citations and constrain answers to retrieved evidence.
4
Permission and security wiring
We connect single sign-on (SAML or OIDC), propagate source-system access controls into the retrieval filter, and deploy the system inside your VPC or on-premises environment.
5
Evaluation, tuning, and handover
We measure retrieval and answer quality with a defined evaluation set using tooling such as RAGAS and LangSmith, tune chunking and reranking against the results, and hand over an owner-ready system with a reindexing cadence.
You bring the knowledge sources; we map, index, and secure them.
Book a Scoping SessionSecurity and Compliance Built for Regulated Data
The system is architected for sensitive internal data from day one, not retrofitted after launch.
- Role-based access control with document-level permission filtering at retrieval time
- Encryption in transit and at rest across ingestion pipelines and vector stores
- Timestamped audit trail of every query and retrieved source
- Designed to recognized standards including SOC 2, ISO 27001, and ISO 42001
- DPDP Act 2023 alignment for India-based data, with GDPR considerations where applicable
- VPC or on-premises deployment with open-weight models (Llama, Mistral, Qwen) when data residency requires it
Data residency and audit requirements are scoped during discovery so the architecture matches your compliance obligations from the first release.
Connectors to Your Knowledge Sources
We ingest from the systems your teams already use, with document parsing and OCR for PDFs, scans, and images.
Files & Collaboration
Google DriveOneDriveSlackEmail archives
Engineering & Operations
JiraGit repositoriesRunbooksTicketing history
Data Stores
PostgreSQL / pgvectorSQL databasesNoSQL databasesCustom APIs
Content stays where it is. Connectors keep the index updated as source documents change, without asking teams to migrate knowledge.
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Learn moreFAQs
An internal RAG system is a private AI assistant that answers employee questions using retrieval-augmented generation over your own company content. It retrieves relevant passages from your documents, databases, and wikis, then uses a language model to write an answer grounded in those passages, with citations. Unlike a public chatbot, it runs on your data, inside your access controls, and does not expose internal content externally.
Enterprise knowledge retrieval returns a direct, synthesized answer with sources, while traditional enterprise search returns a list of links the employee still has to open and read. A RAG-based system understands the meaning of a question through vector search rather than only matching keywords, so it finds relevant content even when the wording differs. The result is an answer to the question asked, not a set of documents to sift through.
Yes. KriraAI's internal RAG systems enforce your permissions at retrieval time, so the assistant only builds answers from content a given user is already allowed to access. We propagate access control lists from source systems and apply role-based and document-level filtering before any passage reaches the model. A user cannot retrieve or see content they could not open directly in the source system.
Yes. The system can be deployed inside your own cloud VPC or on-premises, and can run on open-weight model families so proprietary content stays off third-party inference endpoints. KriraAI designs data handling to align with the DPDP Act 2023 for India-based data, alongside standards such as SOC 2 and ISO 27001. Data residency and audit requirements are set during the discovery phase.
The assistant answers only from passages retrieved out of your content, and guardrails require it to cite the source of each claim. Because generation is grounded in retrieved evidence rather than the model's training data, fabricated details are constrained, and when no relevant passage is found the system is configured to say so rather than guess. Retrieval quality is measured with an evaluation set so weak answers can be traced to a source or ranking gap and fixed.
Timeline depends on the number of source systems, content volume, and security requirements, all of which are scoped in the discovery phase. A focused first release covering a defined set of sources typically reaches a working, evaluated system faster than an enterprise-wide rollout across every repository. KriraAI sequences delivery so one team can start querying a production system before all sources are connected.