SNAP turns the material you choose to save - pages, screenshots, PDFs, decks, and uploaded files - into a research workspace that can reason across your own trusted sources.
Instead of searching the open internet again, you ask questions against the sources you have already verified, captured, and organized. The result is search that understands context, not just keywords.
Grounded
SNAP is designed to answer from the material you captured or uploaded, not from random articles that happen to rank well online. That source boundary helps address one of AI's biggest transparency problems: knowing what an answer is based on.
Contextual
Research questions are rarely exact-match searches. Reasoning Search connects meaning, themes, summaries, and source context.
Conversational
Research becomes a conversation. Ask follow-up questions, refine your angle, and let SNAP bring the most relevant source material back into view.
An advanced AI model reviews captured pages, screenshots, PDFs, and uploads to produce useful summaries, topics, and signals for later research.
SNAP builds a structured memory of what each source is about, so important documents can be found by meaning, theme, and relationship.
When you ask a question, SNAP looks for the most relevant source material and uses a reasoning model to connect the right evidence to your inquiry.
The chat experience is built around grounded answers and source context, so users can trace conclusions back to the documents they chose to keep.
Most search tools are optimized for discovery. Research teams also need trust, recall, and reasoning over known material.
SNAP focuses on the library you build. That means the system works from your selected sources instead of treating the entire internet as equally reliable, giving teams a clearer line from answer to evidence.
By combining analysis, organization, and reasoning-based retrieval, SNAP can surface relevant documents even when your question does not use the same words as the source.
This approach follows Silverberry's Responsible AI by Design framework: build AI experiences that are useful, explainable, and grounded in the context users can inspect.
Collect papers, articles, citations, lecture material, and notes, then ask grounded questions across the body of work.
Turn readings, course resources, screenshots, and uploaded documents into a searchable study memory that supports learning and synthesis.
Capture market signals, competitor pages, reports, customer evidence, and trend material for analysis, forecasting, and executive briefs.
SNAP helps teams turn saved information into research-grade memory: organized, source-grounded, and ready for decision work.