Knowledge assistants that answer from your own content, with sources.
We build retrieval-augmented generation (RAG) systems for companies: assistants and search that answer from policies, contracts, product documentation and records, show where each answer came from, respect who is allowed to see what and are measured before launch.
What is enterprise RAG?
Enterprise RAG (retrieval-augmented generation) is an AI system that first retrieves the most relevant passages from a company’s own documents and data, then asks a language model to answer using only those passages, with the sources shown. Enterprise versions add permission-aware retrieval, connectors to business systems, evaluation sets and monitoring.
- Answers drawn only from approved company content, with sources
- Permissions enforced at retrieval, so users only see what they may
- Accuracy measured on real questions before and after launch
- Content kept current through connectors and scheduled updates
- Best for
- Teams that spend hours searching documents, wikis or tickets for answers
- Ways to start
- Discovery, defined project or ongoing team
- You see progress
- Working software every 1 to 2 weeks
When this is the right call
- Staff search several systems to answer the same questions every day.
- Support teams rewrite answers that already exist somewhere in your documentation.
- Customers expect accurate self-service answers about your products and policies.
- An internal chatbot gives confident answers nobody can trace to a source.
What changes for the business.
The results we design the work around, agreed with you before we start.
Answers drawn from your own documents, with sources people can check
Staff find the right policy, contract or product answer without searching folders
Each user sees only the content they are allowed to see
Answer quality measured on real questions before every change ships
Enterprise RAG, end to end.
Every assistant ships with source citations, permission checks and an evaluation set of real questions.
Internal knowledge assistants
Answers for staff from policies, procedures, wikis and past tickets, with sources shown.
Customer-facing assistants
Self-service answers about products and policies inside your website or app.
Document and contract search
Search and question answering across contracts, reports and long documents.
Connectors and ingestion
Content pulled from SharePoint, Google Drive, Confluence, databases and ticketing tools.
Evaluation and monitoring
Question sets, answer quality tracking and feedback loops in production.
- Document parsing and chunking strategy
- Hybrid search: vector and keyword retrieval
- Evaluation sets and accuracy testing
- Permission-aware enterprise search
- Re-ranking and source citation
- Provider abstraction to avoid lock-in
Controls on every knowledge assistant we ship.
Retrieval quality decides answer quality. These controls keep answers grounded, permitted and measurable.
Permission-aware retrieval
Document access rules applied at search time, not after the answer is written.
Source citations
Every answer links to the passages it used, so people can verify it.
Retrieval evaluation
Real questions with known good sources, used to tune chunking, search and ranking.
Grounding checks
Answers not supported by the retrieved content are flagged or declined.
Content freshness
Connectors re-index changed and deleted documents on a schedule.
Cost and latency tracking
Every query logged with its cost, speed and outcome.
Applied AI we have delivered.
A resource management platform for a growing IT services provider
An AI-powered health tracking app for patients and providers
An AI legal assistant that answers questions and drafts documents
Pick the size of the first step.
Most clients start small and expand once the first stage proves itself.
RAG readiness review
A short review of your content, systems and permissions, with a recommended first use case and a test set of real questions.
First knowledge assistant
One assistant over one body of content for one group of users, measured against an agreed answer quality.
Enterprise knowledge platform
Several content sources, user groups and assistants on shared retrieval, permissions and monitoring.
- 1
Choose the knowledge
The sources, users and questions that matter most.
- 2
Connect and index
Documents ingested with their permissions, split and embedded for search.
- 3
Build retrieval and answers
Hybrid search, re-ranking, prompts and citations behind one service.
- 4
Evaluate
Retrieval and answer quality measured on a real question set.
- 5
Operate
Feedback, unanswered questions, freshness and cost tracked in production.
Technology we use for this
- OpenAI
- Anthropic
- Gemini
- RAG
- Vector retrieval
- AI orchestration
- Python
- TypeScript
- PostgreSQL
Enterprise RAG: questions we are asked
What is enterprise RAG?
Enterprise RAG (retrieval-augmented generation) is an AI system that first retrieves the most relevant passages from a company’s own documents and data, then asks a language model to answer using only those passages, with the sources shown. Enterprise versions add permission-aware retrieval, connectors to business systems, evaluation sets and monitoring.
Is RAG better than fine-tuning a model?
For answering from company content, usually yes. RAG keeps answers current and traceable without retraining. Fine-tuning helps with style, format or narrow behaviour, and the two can be combined.
How do you keep confidential documents secure?
Permissions from the source systems are carried into the index and enforced when content is retrieved, so a question can only use documents that user may already see. Data handling follows the provider terms and hosting choices agreed with you.
Which content sources can you connect?
Common sources include SharePoint, Google Drive, Confluence, Notion, ticketing systems, databases and file stores. Where a system has no ready connector, we build one against its API.
How accurate are the answers?
Accuracy depends mostly on content quality and retrieval design. We build a set of real questions with accepted answers, measure every version against it and show sources so users can check. When no good source exists, the assistant says so rather than guessing.
Solutions and reading.
Get a proposal for enterprise RAG.
Tell us what you need. We reply with a clear next step, usually a short call and a written plan.
- 1We read your brief properly
- 2You speak with people who can discuss the business and the engineering
- 3You get a clear next step: a scoped engagement, a discovery phase or a proposal
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