What's the difference between RAG and fine-tuning?

RAG lets the model consult an external, up-to-date data source while generating an answer; the model itself doesn't change, only the retrieved information does. Fine-tuning retrains the model on a specific dataset, permanently changing its behavior; RAG suits fast-changing information better.

Does the system update automatically when source documents change?

Update frequency is set during setup; we can build update flows that run daily, weekly, or trigger instantly whenever a document changes.

Can the model still give a wrong answer?

The risk drops significantly but never reaches zero; that's why we recommend keeping source citations in answers and checking quality with regular sampling.

Technology Services

RAG-Powered Enterprise Memory

What is RAG-Powered Enterprise Memory?

RAG (Retrieval-Augmented Generation) is an architecture that lets a large language model ground its answer in your company's current, verified data instead of its own training data. Your institutional knowledge is stored in a vector database, and the model retrieves the relevant pieces for every question and bases its answer on them.

What problem does RAG-Powered Enterprise Memory solve?

When a large language model's training data goes stale, or it simply lacks knowledge specific to your company, it either gives a vague answer or fabricates one (hallucination). RAG reduces that risk by always grounding the answer in a source you've verified.

How is RAG-Powered Enterprise Memory set up?

01Document collection: policies, product information, FAQs, and other institutional documents are identified.
02Vectorization: documents are processed to be searchable and loaded into a vector database.
03Retrieval layer: when a question comes in, the model pulls the relevant document chunks from that database.
04Update flow: how the vector database gets refreshed when source documents change is defined.

Example use cases

Finance: Current product terms

For a financial institution whose support reps struggle to recall current product terms, the RAG system pulls the correct answer from the up-to-date document set and shows its source.

HR: Policy questions

For a company where employees email in questions about leave, expenses, and policy, the RAG system answers those questions directly from the current HR documents.

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