Imagine you have a vast dataset about an entity or a group of entities or an unknown entity. Taltosh assembles a digital twin of your scoped data and lets you chat with it. You can ask complex questions that otherwise would not be searchable using traditional indexing. Ask for intents, passive references, fears, goals or even explore group slang in seconds
Taltosh uses RAG and on-premise LLM model architecture to let you chat directly with your data, fielding questions in natural language and pulling answers from your data’s digital twin. It’s efficient, working even on limited hardware, making deep data insights accessible without heavy computational demands. This streamlined approach ensures quick, relevant responses, empowering users to extract key information and make informed decisions with ease.
Digital Twin & Data Interrogation examples
Category
Unknown entity
Prompt examples
- “Provide a summary focusing solely on borrowed vehicles within the dataset.”
- “Identify all unique individuals present within the data.”
- “Aggregate instances of negative sentiment expressed throughout the data.”
- “Gather references, both direct and indirect, to expressions akin to ‘stash’.”
- “Isolate distinct conversations that involve weaponry.”
Category
Single entity
Prompt examples
- “Detail your principal interests.”
- “Outline your potential actions in the given scenario.”
- “Describe your primary financial pursuits.”
- “Reveal any alternate identities you may possess.”
- “Summarize discussions involving your main associates.”
Category
Group of people
Built-in case resolvers
- “What are your top five topics of discussion?”
- “Do you have any collective plans in motion?”
- “Are there mentions of a bald individual in your internal dialogues?”
- “Is there a common phrase frequently used among you?”
- “Can you provide references to ‘tunnel’ or ‘border’?”
- “What are the known meeting points and schedules within your group?”
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