Turing
An intelligent solution for Data structuring
Turing AI is an innovative artificial intelligence that simplifies data structuring through an advanced ontological strategy. It transforms raw data into structured information, considering user-defined context. With this approach, Turing optimizes data management, enabling precise organization tailored to specific business needs while simplifying data utilization for effective interaction with large language models (LLMs).
Context retrieval
Create a complete context of your environment by analyzing multiple data formats stored in a unified knowledge base.
Automation of structuring
Transform raw data into structured information tailored to your LLM business needs with Turing, by retrieving the context of your environment.
Context understanding
Identify relationships and context within data for interacting with the LLM business with a use case specialized.
Data
optimization
Utilize Turing’s ontological strategy to precisely organize your data, facilitating its use for in-depth analysis.
Context retrieval in streaming or real-time
Turing focuses on sorting, categorizing, and segmenting your company’s existing data to ensure it is well-organized and easily accessible. We manage your current data by applying an ontology strategy that classifies and contextualizes information for better use. Additionally, we set up the necessary framework to handle and integrate new data as it comes in.
PHASE 1:
FIRST RECORDING
Turing phase 1 captures all existing data in an initial recording to ensure a comprehensive dataset is available from the very beginning.
TURING A 1.1: Turing simplify taxonomy
- The user will define the themes (3 lines).
- The user will also define the relationships between domains (entities) and between the domain and the theme (max 3 words).
TURING A 1.2: Add missing context
- Check if the data is already saved
- Generate questions based on the data (previously for Turing B).
- Create Cypher queries based on the questions
- Execute the Cypher MATCH query
- Construct the contextualized sentence
The user provides additional context. Based on contextualized paragraphs and existing data in A1.1, Turing will fill in the missing entities and relationships to achieve a more complete context.
TURING A 2: Automatization workflow
PHASE 2: Recording workflow in real-time
The TURING PHASE 2 enables seamless, real-time data recording and management, capturing information across platforms as it happens. From instant capture to comprehensive organization, the Turing Phase 2 transforms real-time data flow into a strategic asset for your business. Through its four interconnected modules it structures, enriches, and stores data continuously, ensuring immediate relevance and accessibility:
- TURING A
- TURING B
- TURING C
- TURING D
TURING A Recording workflow in real-time
Just like phase 1, but in real time, with updates automatically processed after each addition in tools like Skype, Slack, or Notion. Turing A1.2 seamlessly transitions into Turing A1.1.
Ontology strategy for existing data
Our ontology strategy is applied to your existing data, organizing it according to its context for more precise use.Full data context
We provide the complete context for all your data, ensuring that every piece of information is accurately categorized and connected to its relevant business applications.Future-proof setup
Along with managing current data, we establish the necessary framework for handling and integrating new data as it comes in. 
TURING B Real-time information retrieval
Turing b focuses on real-time data retrieval, where new entity detection and classification are performed based on their existence in the knowledge graph.
- Add missing entities
- Update in real-time the knowledge graph with missing context added
- Construct the contextualized sentence
TURING CStoring information in chromaDB
Turing c involves a backend script that stores information in ChromaDB. It translates paragraphs using the model’s embedding.
TURING D
Call recording and classification data
Turing d handles tool call recording with classification by project or topic. Using a tool extension and speech-to-text (STT) technology, calls are segmented and classified. The tool extension can be Skype, Google Meet. The output is stored in JSON format, generating Cypher requests to save the classified information in the knowledge graph.
Key benefits of Turing
Turing offers advantages that revolutionize data management.
Flexibility and customization
Adapt data structuring to the specific needs of each business, with customization based on user-defined relationships.
Data security
Ensure secure storage of structured information, with data organized and safeguarded.
Data quality improvement
Reduce errors and inconsistencies through optimized management of context and relationships between data.
- Data consistency
- Intelligent data storage
ARTIFICIAL INTELLIGENCE IN DATA STRUCTURING
Structure your raw data in real-time
Save the context related to a business topic and create an ontology strategy to enable interaction with a large language model (LLM), ensuring it understands the business context for more accurate and relevant responses.
Efficiently retrieve and store real-time conversations and data.
Transforming a graph database into a vector database enables similarity searches, allowing the LLM to go beyond its original datasets and understand similar contexts, while efficiently handling complex queries.
Ontology strategy
The ontology strategy enables the development of a structured organization of your data while taking their context into account. This approach ensures that raw data is not only consistently classified but also connected to its environment and the relationships surrounding it. Moreover, it specifically structures the data for efficient retrieval by a large language model (LLM), enhancing the overall effectiveness of information access.
By adopting an ontology strategy, your organization enhances data interoperability and improves information retrieval through Turing AI. You can:
- Coordinate complex data
- Automate and optimize information flows
With our expertise in ontology design, we help businesses streamline their processes and maximize the potential of their data using Turing AI.
Conversion of graph database into vector database
The conversion of graph to vector for the database is a key process for transforming complex graph databases into a simplified vector format. This transformation enables more efficient data analysis and leverages similarity search to tailor LLM responses even if the user prompt is not in the dataset. It is also essential for complex and multi-dimensional searches. Cypher query generation is used to create contextualized phrases, enhancing the accuracy and relevance of the responses.
USE CASE
Turing
Turing stands out as an invaluable asset for project management and business intelligence, simplifying data structuring and providing strategic insights that promote better coordination and more informed decisions.
Turing AI, perfectly designed to your datas
Open-weight model with flexible support
Turing, through its open-weight model, unites developers and data scientists to continually enhance its features, all while being completely free to use. We only charge for implementation services if clients need assistance.
Your data remains under your control
No software lock-in:
- Access to the source code included.
- Access to our GitHub repository.
- Freedom to choose hosting on our infrastructure or yours.
Affordable training with GPU
We offer competitive GPU pricing during model training, making us more cost-effective than most cloud platforms.
Fully customizable model
Use Turing Studio to automate actions, create custom reports, or configure webhooks.
Eager to learn more?
We would be delighted to discuss your needs to boost your business with AI.