Enterprise AI Assistant Project: Process, Timeline and Cost Components
How does an enterprise AI assistant project run? Phases and typical timelines, one-off and recurring cost components, build vs. buy, and why projects fail.
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Enterprise Knowledge Assistants (RAG)
Retrieval-Augmented Generation (RAG) grounds AI answers in your own approved sources. BrotherhoodIO builds end-to-end knowledge assistants with document processing, vector search, permission-aware retrieval and source citations — so employees and customers get accurate, traceable answers in seconds.
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Service area: Ankara, all of Türkiye and international clients (remote).
What we deliver
- Ingestion of PDF, Word, email, wiki and database sources
- Vector and hybrid search (pgvector, Elasticsearch, Qdrant, etc.)
- Permission-aware answers per user role
- Source citations and hallucination controls
- Teams, Slack, web and mobile channel integration
- Automated evaluation sets for answer quality
Outcomes you can expect
- Instant access to institutional knowledge
- Fewer repetitive support requests
- Auditable answers
Frequently asked questions
What is the difference between RAG and fine-tuning?
RAG retrieves relevant documents at answer time without retraining, keeping knowledge current and citable. Fine-tuning changes a model’s behavior or style.
What if the assistant gives a wrong answer?
Answers are grounded in and cite your sources; when no source is found the assistant is configured to say so instead of guessing.
Does our data leave our environment?
It doesn’t have to — fully on-premise or private-cloud deployments are available.
Guides on this topic
What Is RAG? A Guide to Using Enterprise Data Securely with LLMs
What is RAG (Retrieval-Augmented Generation) and how does it work? A guide to using enterprise data securely with LLMs, with citations and access control.
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