Source-linked data preparation and assistant console

Reduce tool sprawl and manual data preparation while keeping source content under the buyer's control. For product and support teams building custom chatbots and fine-tuned models from their own websites, PDFs and videos, convert permitted websites, PDFs and videos into source-linked plain text, chatbot configurations and fine-tuned model instances. The benefit is a testable hypothesis, measured through accepted chatbot answers per preparation hour and corrections after deployment; do not assume that AI output alone produces business value.

Confirm the buyer's problem and scope, collect permitted websites, PDFs and videos, then follow this sequence: 1. Collect content from websites, PDFs and videos. 2. Produce a single plain text file from the collected content. 3. Create a chatbot from the collected data. 4. Train a language model on the user's own content. Resolve uncertain cases with qualified reviewers, approve source-linked plain text, chatbot configurations and fine-tuned model instances, and measure accepted chatbot answers per preparation hour and corrections after deployment against a documented baseline.

How the AI works

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the four stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One fixed source set and permitted content types; final accuracy and brand checks remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

Safeguards

Preserve source attribution, quotation accuracy and usage permissions. Buyers approve substantive changes and deployment scope. One fixed source set and permitted content types; final accuracy and brand checks remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.

What to build first

Pilot scope: One fixed source set and permitted content types; final accuracy and brand checks remain human. Implement one approved input format, a bounded representative case set and the first two task modules: collect content from websites, PDFs and videos; produce a single plain text file from the collected content. Support the remaining modules with operator review: create a chatbot from the collected data; train a language model on the user's own content. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

What it can connect to

Buyer-owned websites, document stores and permitted video sources. Cloud storage, design-file import/export and deployment destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

The screens in detail

Primary screens: Source intake and permissions, Editable extraction preview, Assistant and model console, Client proof and delivery. Use a thumbnail gallery for projects, a large central editing canvas, and a right-hand panel for sources, constraints and comments. Let users compare versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant source passage. Make the task-specific outcome source-linked plain text, chatbot configurations and fine-tuned model instances visible beside its evidence, review state and value baseline.