Internal knowledge
Search policies, instructions, project material, and technical documentation.
RAG · AI ASSISTANTS
RAG and AI assistant development
We build assistants that work with your documents, knowledge bases, and business systems. The scope goes beyond a chat UI: access control, ingestion, updates, retrieval, cited answers, evaluation, observability, and integrations all matter.
01 · OUTCOME
What a reliable RAG system includes
Answer quality depends on more than the language model. We validate sources, permissions, retrieval, context, instructions, and a measurable set of test questions.
- Audit of sources, formats, freshness, and access rules
- Indexing, retrieval, reranking, and context assembly
- Answers with source links and explicit uncertainty states
- Web UI or API, roles, history, and integrations
- Evaluation set and monitoring for quality, cost, and latency
02 · USE CASES
AI assistant use cases
Customer support
Prepare grounded responses and hand difficult or sensitive cases to a person.
Professional copilot
Assemble context, draft documents, and provide guidance inside an existing workflow.
AI inside a product
Embed contextual search and answers into an existing SaaS platform, portal, or application.
03 · PROCESS
How we launch a RAG system
We begin with a test set of real questions. This makes approaches comparable and prevents a polished demo from becoming the only quality signal.
- 01
Sources and permissions
Define documents, systems, data owners, update frequency, and the access model.
- 02
Evaluation set
Collect questions, expected answer properties, supporting sources, and unacceptable errors.
- 03
RAG and interface
Implement ingestion, retrieval, models, prompts, citations, and the user journey.
- 04
Evaluation and operations
Measure quality, cost, and latency, then add monitoring and an improvement process.
04 · FAQ
RAG and AI assistant FAQ
Does RAG eliminate hallucinations?
No. RAG can improve grounding, but errors remain possible. Test sets, citations, narrow scope, and human review are needed for critical decisions.
Can document permissions be preserved?
Yes. Access rules must be enforced during retrieval so users receive context only from sources they are authorised to view.
Can it use PDFs, a Wiki, CRM, and databases?
Often yes, but each source needs separate validation. Structure, extraction quality, freshness, permissions, and update mechanisms affect the result.
Can the system run in a private environment?
It depends on the infrastructure and model choice. We evaluate APIs and open-source options against data, cost, and quality requirements.
START A PROJECT
Test an assistant on your data
Share sample sources and 10–20 real questions. That creates a practical basis for technical diagnosis and evaluation.