Source-linked team AI assistant and admin console

Consolidate AI chat, multi-model access, team collaboration, data analysis and document chat into one owned assistant. For teams and individuals needing AI chat assistance for text generation, data analysis and collaboration, convert permitted model access, team prompts, connected data sources and document sets into source-linked assistant answers and admin-reviewed agent runs. The benefit is a testable hypothesis, measured through accepted assistant answers per reviewer hour and corrections after approval; do not assume that AI output alone produces business value.

Confirm the buyer's problem and scope, collect permitted model access, team prompts, connected data sources and document sets, then follow this sequence: 1. Provide conversational AI chat for questions and text generation. 2. Switch between multiple language models including GPT-4, Claude and Llama. 3. Adjust tone and style of generated text. Resolve uncertain cases with qualified reviewers, approve source-linked assistant answers and admin-reviewed agent runs, and measure accepted assistant answers per reviewer hour and corrections after approval against a documented baseline.

How the AI works

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three 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 approved model set and connector scope; final factual, legal and security 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. Named owners approve substantive changes and external actions. One approved model set and connector scope; final factual, legal and security 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 approved model set and connector scope; final factual, legal and security checks remain human. Implement one approved input format, a bounded representative case set and the first two task modules: provide conversational AI chat for questions and text generation; switch between multiple language models including GPT-4, Claude and Llama. Support the third module with operator review: adjust tone and style of generated text. 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

Team-owned documents, authorized data sources and permitted model APIs. Cloud storage, identity providers, chat and analytics 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: Assistant chat workspace, Agent and connector console, Admin and audit view. Use a left sidebar for conversations, agents and prompt library, a large central chat and document canvas, and a right-hand panel for sources, model choice, tone and review state. Let users compare model answers side by side. Display draft, changes requested and approved states. Provide a shared team history with comments anchored to the relevant message or document. Make the task-specific outcome source-linked assistant answers and admin-reviewed agent runs visible beside its evidence, review state and value baseline.