Robots Forget. We Give Them Memory.
Today’s robots restart from zero in every task — no memory, no context, no adaptability. Our system makes every experience cumulative and usable.
Robots evolve from generic machines into adaptive, ready-to-work companions
Learning That Stays. Decisions You Can Trust.
Our memory OS enables robots to learn and adapt on the job — without retraining. Context builds over time, decisions are explainable, and every step is transparent. No black boxes, only traceable intelligence.
Where AI Memory Makes the Difference
From warehouses to healthcare, robots need more than algorithms — they need professions and memory. Partenit: DeepContext AI turns fragmented actions into long-term knowledge, enabling robots to work with context, precision, and adaptability.
NeoIntelligent Robotics: Memory as Evolution
Robots transcend programming. Our ontological memory transforms machines from rigid executors into adaptive, learning entities that accumulate experience like living organisms. Each interaction becomes a neural pathway, creating machines that understand context, not just commands.
Professional Knowledge Amplification
Imagine expertise that never forgets. Doctors, lawyers, engineers gain an intelligent archive that doesn’t just store information, but actively interprets, connects, and surfaces insights across massive knowledge landscapes in milliseconds.
Corporate Intelligence Networks
Knowledge transforms from static data pools into dynamic, interconnected ecosystems. Our multi-layered ontological memory turns complex information into living, breathable intelligence – where insights emerge organically, not through mechanical querying.
Autonomous Learning Ecosystems
We don’t just help machines remember – we teach them to think. Partenit memory enables systems to recognize patterns, predict challenges, and autonomously adapt their behavior, creating a new paradigm of machine consciousness.
Ontology vs. Large Language Model: How to Get More While Spending Less
Left: Response from powerful (and costly) GPT model. Right: Response from a simple ontology query. Same accuracy, dramatically lower costs.
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Cost-Efficient Performance Even simple queries to a well-structured ontology can produce results comparable to those of powerful (and expensive) large language models—saving substantial computing resources and token costs.
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Precision and Reliability Ontology-based retrieval ensures highly accurate and dependable results, essential in sensitive domains like healthcare, finance, or legal services, where precision is critical.
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Transparency and Explainability Answers retrieved directly from an ontology are fully transparent and auditable, unlike probabilistic outputs from large language models. This ensures trust, regulatory compliance, and explainability.
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Ease of Integration Ontological memory is easier and faster to integrate into existing systems compared to complex interactions with large AI models, making it ideal for organizations aiming for rapid deployment without extensive resources.
How It Works — And Why Robots Need More Than AI Alone
Robots Without Memory: Stuck in Trainee Mode
- Execute tasks blindly, without context
- Restart from zero each time
- Never grow into real professions
How Partenit Gives Robots Professions
Step 1: Experience Mapping
Robots turn every task and interaction into structured memory — a profession they can build on.
Step 2: Contextual Recall
Instead of repeating mistakes, robots use past experience to adapt and perform smarter on the job.
Step 3: Autonomous Growth
Robots evolve skills continuously, learning like human colleagues — not like resettable machines.
Industries & Use Cases
Education
Personalized AI tutoring that remembers student progress
Healthcare
Patient history tracking for accurate diagnostics
Customer Support
Chatbots that retain detailed user interactions
Finance
Contextual client profiling for precise recommendations
Legal Services
Structured retrieval of relevant case histories
HR & Recruitment
Intelligent matching of candidate skills to job roles
Marketing
Enhanced personalization based on past customer behavior
Research & Development
Organizing vast knowledge bases for efficient discovery
E-commerce
Tailored recommendations based on detailed user journeys
