Enterprise knowledge assistants
One place for staff to ask questions across wikis, drives, ticket history and shared folders.
Answers from your own knowledge, with the source attached
Retrieval-augmented generation (RAG) connects a language model to your approved content, so answers come from your policies, manuals, contracts and records rather than from the open internet. We build RAG applications that respect access rights, cite their sources and are measured for accuracy before they go live.
Retrieval pipelineIllustrative
- Sources
- PDF · Wiki · Drive · SQL
- Search
- Hybrid + rerank
- Access
- Permission-aware
- Measured by
- Groundedness score
Overview
Why grounding matters
A general model does not know your pricing rules, your HR policy or last quarter's contract changes.
RAG gives it that knowledge at the moment of the question and tells it to stay within it. The result is an assistant your team can trust, because every answer links back to the paragraph it came from.
Capabilities
What we build
Each engagement is shaped to your use case. These are the building blocks we combine most often.
Capability
Capability
Document Q&A
Upload contracts, reports or manuals and ask questions, compare versions or extract clauses.
Capability
Hybrid search
Keyword and semantic search combined with reranking for higher precision on technical language.
Capability
Permission-aware retrieval
Users only see answers drawn from documents they are already allowed to open.
Capability
Accuracy evaluation
Test sets, groundedness scoring and regression checks every time content or prompts change.
Capability
Multilingual retrieval
Ask in Hindi, Kannada, Arabic or English and retrieve from content written in another language.
Use cases
Where teams use it
Work this service handles well. We help you pick the one with the clearest return to start.
Browse use cases by industry- HR policy assistant for leave, travel and benefits questions
- Product documentation search for support teams
- Contract clause lookup and obligation tracking
- SOP and clinical protocol reference for care teams
- Sales enablement answers from case studies and pricing notes
- Research summaries across reports and filings
Technology
Tools we work with
We pick the stack for your requirements and existing systems. A sample of what we use regularly.
Vector stores
pgvectorPineconeQdrantWeaviateOpenSearch
Retrieval
LlamaIndexLangChainCohere RerankBM25 hybrid search
Ingestion
UnstructuredAzure Document IntelligenceAWS TextractOCR
Models
ClaudeGPTGeminiLlamaopen-source embedding models
Process
From first call to production
The same proven path for every project, sized to your scope.
01
Discover
Map the opportunity, the people involved and the outcome we will measure.
Output · Workshop02
Define
Review data, workflows and systems, then agree architecture and scope.
Output · Plan and estimate03
Prototype
Test the AI approach on your real data before building the full product.
Output · Working proof04
Build and integrate
Develop in short sprints and connect to your systems, with demos every sprint.
Output · Production release05
Launch and improve
Go live, monitor quality and cost, and improve based on real usage.
Output · Ongoing supportFAQ
Common questions
Can't find what you need? Ask us directly and we'll reply within one business day.
Ask a questionWill a RAG assistant make things up?
It can still make mistakes, so we design against it. The model is instructed to answer only from retrieved passages, show citations and say when it does not know. We measure groundedness on a test set before launch and keep monitoring it afterwards.
Which content can it use?
Most document formats work: PDF, Word, Excel, slides, web pages, wikis, emails and scanned files with OCR. Structured data from SQL databases or APIs can be included alongside documents.
How do you keep our data private?
Content stays in your cloud account or a private environment we set up for you. We use model providers that do not train on your data, apply your existing access permissions and encrypt data in transit and at rest.
Related services
Often combined with
AI Chatbot Development
Website, WhatsApp and in-app assistants that resolve questions and hand off to your team with context.
ExploreGenerative AI Development
Content, document and creative generation tuned to your brand, data and review process.
ExploreAI Automation Services
End-to-end workflows that read documents, make routine decisions and update your systems.
ExploreStart a conversation
Tell us where your team's knowledge lives, and we will show you what it can answer.
Book a free consultation with our AI team. You'll leave with a clear view of feasibility, approach and next steps.