Answers from your own knowledge, with the source for every claim.
Retrieval-augmented generation (RAG) means the AI looks up relevant passages from your documents first, then writes its answer using only what it found — and shows you where each part came from. We build these systems with permission-aware search, fresh content and measured answer quality.
- Source inventory and access map
- Ingestion and indexing pipeline
- Retrieval layer
- Answer generation with citations
- Evaluation and feedback
Where this helps
What we deliver
How it works
- 01
Collect real questions
We gather questions people actually ask, and the documents an expert would use to answer them.
- 02
Map sources and permissions
Content owners, access rules and update frequency are documented before anything is indexed.
- 03
Build the pipeline
Ingestion, indexing, retrieval and answer generation are built and tested on the question set.
- 04
Tune retrieval quality
Most answer errors are retrieval errors. We iterate on chunking, metadata, ranking and filters until the right sources come back.
- 05
Launch and maintain
Release to a pilot group, track unanswered and poorly rated questions, and hand content gaps back to their owners.
Design decisions we make with you
Permission-aware retrieval
Users only get answers from documents they are allowed to open. Permissions are checked at query time, not copied once and forgotten.
Freshness
How quickly an edited or deleted document must disappear from answers, and how the pipeline detects changes.
Search approach
Semantic search alone misses exact codes and names; keyword search alone misses paraphrases. We usually combine both.
Where the index lives
A vector database, your existing search engine or Postgres — chosen by scale, security and what your team can run.
When to refuse
The system should say it cannot find an answer rather than guess. We tune that threshold with your team.
Applications
Related capabilities
- Enterprise Knowledge AssistantAnswer staff questions from your own policies, procedures and records, with sources shown and access rules respected.
- Customer Support AgentsResolve routine customer inquiries from your policies and live account data, taking approved actions and handing everything else to your team.
- AI ChatbotsText chat assistants for customer support, internal help desks and product experiences, grounded in your knowledge and connected to your systems.
- Generative AI & LLM DevelopmentApplications built on large language models that are grounded in your data, tested against real cases and costed before launch.
- AI IntegrationConnect AI models and assistants to your CRM, ERP, databases, knowledge stores and APIs — with identity, logging and fallbacks that production systems need.
- Enterprise & Private AIAI deployed with the data isolation, access control and operational ownership your security and compliance teams require.
Questions buyers ask
The system first searches your documents for passages relevant to the question, then gives those passages to a language model and asks it to answer using only that material. Because the answer is built from retrieved text, it can cite its sources and stay current when documents change.
No, if permissions are designed in. We carry access rules from your source systems into the index and filter results for each user at query time.
Not all of them. We start with the sources that answer the most common questions. The evaluation and feedback loop then shows exactly which content is missing or outdated.
RAG is the knowledge layer. It can sit behind a text chatbot, a voice agent, a search page or an internal tool.
Discuss this capability with an engineer.
Tell us about the workflow or product. We reply with questions, a suggested first step and who would work on it.