Launching KyroDB- Building Data infrastructure for Superintelligence
AI applications and services work on one most important things, i.e, data, and with the demand for AI applications growing exponentially, they truly need the infrastructure that could provide them a unified solution to their varied needs. The problem with current systems is the fragmented infrastructure; we currently glue together multiple databases, cache, an indexing engine, etc, to power one intelligence. This introduces brittleness, massive latency, and complexity.
KyroDB & Vritti
Today, we are launching v0.1 of two innovative systems that we built:
1. KyroDB- A High Performance vector database, purposefully built for RAG workloads and AI agents.
2. Vritti- A Persistent Episodic Memory for AI agents to learn from their past mistakes and never repeat them again, without the retraining and weight updates required.
→ With KyroDB, we implemented a new caching architecture to store and retrieve cache and thus developed a mechanism called ‘Learned Cache’, combined this with ‘Semantic Cache’ and developed a layer called Hybrid Semantic Cache (HSC)
On benchmarks, The system achieved 2-3x better cache hit rates(73.5%) than LRU(25-30%) for RAG workloads, resulting P50- 12µs and P99- 9ms.
→ In Vritti, referencing research papers like Reflexion, Voyager, and continuing our research after that, we developed a sophisticated memory system that combines Self-reflection, a gating mechanism, along with numerous cognitive psychology and neuroscience principles to model how human memory works, techniques like Power-Law Decay, Logarithmic Usage Scaling, etc .. to create a failure-based memory that gives AI knowledge of its own existence.
This is the first step toward Self-Awareness.
The reason it is focused on failure-based memory is that not all experiences are worth storing. Failures contain more learning signals than successes, similar to how we humans react to failures.
Think of, do you remember how many times you scored more than 90% grades in your whole academia?? little tough, right? But you would never forget the time when you almost failed in a subject.
Is this a permanent solution to the memory and hallucination problems of LLMs? Honestly, I don’t think so, continuing the research on this.
Research paper on both of these coming soon.
The Problem
The current databases treat every data as the same entity; it’s not. Think about human memory. When you recall a childhood memory, you're not retrieving data; you're accessing an experience, contextually linked to emotions, people, places, and consequences. When you remember a concept like "gravity," you're not looking up a definition; you're tapping into an abstraction built from countless observations, each weighted by confidence and relevance.
Yet every database today stores a customer record the same way it stores a log entry. A critical medical diagnosis sits in the same row-column structure as a trivial preference. Knowledge that changes daily is treated identically to timeless facts. A fleeting observation carries the same weight as a verified truth.
This is fundamentally incompatible with intelligence.
The real reason is, traditional databases weren't designed to store unstructured data; they were not made for this scale of data generated by AI. They were designed for transactions. They were optimized for CRUD operations, not cognition. They store bytes, not meaning. They treat data as static artifacts to be retrieved, not as living knowledge that evolves, connects, and infers.
On the other hand, the problem with current Large language Models and more generally AI is that they have become yes-men, they have all the knowledge of the internet, know every research paper, trained on all the public data available on the internet, yet why isn’t AI able to make any discovery?
Turns out, to truly acquire intelligence equal to the human level, you need three fundamental things:
1. Understanding of the Physical world
2. Persistent memory
3. Ability to reason and plan
The scaling paradigm has hit its limits: The little jump from GPT-4 to GPT-5 has shown us that doing 50x compute won’t give us 50x better results. It will come from fundamentally rethinking how AI systems interact with knowledge
Why This Matters
We stand at an inflection point in human history. The development of Artificial General Intelligence(AGI), and eventually Artificial Superintelligence(ASI), represents humanity's most consequential technological endeavor. A superintelligent system could solve problems that have plagued humanity for millennia: harnessing nuclear fusion for clean energy, understanding the fundamental nature of the universe, enabling multiplanetary civilization, eliminating poverty and water scarcity(e.g., figuring out a cheap and affordable way to separate the drinking water from the oceans), reversing climate change, and bridging societal inequalities.
But the truth is that we cannot build AGI on today's data infrastructure. Current AI systems are fundamentally constrained by their memory architecture. They train on frozen datasets and operate without true continuous learning. They don't remember. They don't adapt. They don't truly understand, they are just master at predicting next tokens and pattern matching. The path to AGI requires a fundamental rethinking of how intelligent systems interact with knowledge. Not as passive storage, but as living, evolving cognitive memory that learns, reasons, and grows.
This is KyroDB's mission: To build the cognitive memory layer that enables the transition from narrow AI to artificial general intelligence, and eventually to superintelligence.
What Is Intelligence, Really?
Intelligence isn't just pattern matching. It isn't only prediction. True intelligence requires:
Grounded Understanding: Knowledge connected to causal models of reality
Continuous Learning: Integration of new information without forgetting
Temporal Reasoning: Understanding how knowledge evolves over time
Meta-Cognition: Reasoning about one's own knowledge and limitations
Active Inference: Knowing what you don't know and seeking to fill gaps
Current AI systems have none of these. They are sophisticated compression algorithms applied to human-generated text. They approximate reasoning by pattern-matching on examples of reasoning. This is why scaling alone won't achieve AGI; a bigger pattern matcher is still a pattern matcher.
The Memory Problem
The fundamental bottleneck isn't computing. It isn't data quantity. It's memory architecture. (We have a good amount of computing in the world right now)
Biological brains solve the memory problem elegantly by:
Hippocampus: Rapid encoding of new experiences
Neocortex: Slow consolidation into long-term knowledge
Predictive Coding: Constant prediction and error correction
Attention: Dynamic retrieval based on current context
Forgetting: Active pruning of irrelevant information
No artificial system replicates this. Current approaches use:
Context windows: A hack that pretends 200K tokens are memory.
RAG: A band-aid that retrieves without understanding
Fine-tuning: Destructive updates that cause catastrophic forgetting
Vector databases: Static embedding stores with no learning
So basically, we are living on a hack.
Our Vision
KyroDB aims to be the first true cognitive memory and infrastructure system for AI, not a database that stores data, but infrastructure that:
Learns from access patterns to predict what knowledge will be needed( we already achieved this in the current version)
Evolves its understanding as new information arrives
Reasons about knowledge at multiple levels of abstraction
Maintains temporal coherence tracking how truth changes over time
Self-optimizes continuously without manual tuning
This is not an incremental improvement. This is a fundamental reimagining of what data infrastructure means in the age of intelligence.
We have taken a multiphase approach to solve this problem: some of those phases are:
RAG Excellence + Temporal Knowledge(here right now)
Multi-Modal Unification + Continuous Learning (coming early Next year)
Hierarchical Abstraction + Distributed Cognition
Meta-Cognition + Reasoning
Autonomous Evolution + Superintelligence Support
……list goes on
We have taken 5 years of timeframe to complete this vision.
The strange thing about this technology(AI) is, even the scientists are uncertain of what the AGI and Superintelligence may look like, so half of the things run on speculation and calculative guesswork
Impact
Solving Humanity's Grand Challenges
With proper cognitive memory infrastructure, an AGI could solve:
Nuclear Fusion Energy
Integrate decades of plasma physics research across institutions
Reason about novel reactor configurations
Simulate and iterate faster than physical experiments
Identify non-obvious solutions in vast design spaces
Climate Change
Model complex climate systems with full temporal understanding
Reason about intervention cascades and second-order effects
Integrate research across chemistry, biology, economics, and policy
Track evolving understanding as new data arrives continuously
Medical Research
Integrate all published medical literature with real-time updates
Reason about drug interactions across diverse populations
Track confidence in treatments as evidence accumulates
Personalize medicine based on the complete patient's temporal history
Space Exploration
Reason across physics, engineering, and biology for life support
Maintain coherent knowledge across multi-year mission timescales
Self-improve systems during journeys with no human intervention
Here is the link to the company website: Click here
Star the GitHub repo and use the database(KyroDB): Click here
Star the GitHub Repo and use the Memory system(Vritti): Click here
Doesn’t matter what the superintelligence and AGI are going to shape out, but humanity's future is bright. Peace.
Kishan