TotalMind AI builds source-grounded AI systems for private knowledge archives.
We create retrieval-grounded assistants and internal search tools that answer from trusted client-owned datasets, with citation-first responses, provenance tracking, and evaluation-driven iteration.
The robot bumper rules require pool-noodle padding fully enclosed in fabric, mounted within the bumper zone defined in the manual.
AI systems that answer with evidence, not guesses.
TotalMind AI helps organizations turn dense archives, manuals, course materials, editorial content, and internal documents into reliable AI assistants. Each system is designed to retrieve the right source material, generate grounded answers, and show where the answer came from.
Retrieval-Grounded Assistants
Build AI assistants that answer from approved datasets instead of relying on general model memory.
Citation-First Responses
Return excerpts, page-level references, linked sources, and evidence packages so users can verify every answer.
Evaluation-Driven Iteration
Improve answer quality, latency, and cost through structured testing across retrieval settings, models, and response strategies.
Grounded answers from real archives.
Virtual Dr. D
A Socratic AI mentor trained on Professor Sanjiv Dugal's teachings — bringing his "Fishbowl" method online with retrieval-grounded answers in his own voice.
FIRST Robotics Rules
Searches ~200 pages of competition manual content, returning cited excerpts and linked diagrams.
Private knowledge is only valuable when people can actually use it.
Organizations often have years of valuable knowledge buried in PDFs, manuals, course posts, archives, and internal documents. TotalMind AI makes that knowledge searchable, conversational, and verifiable. The goal is not to replace expertise, but to make expert knowledge easier to access with evidence attached.
Have an archive worth making searchable?
We work with publishers, educators, research groups, and operations teams to build assistants grounded in their own materials.