AI FOR RESEARCH AND ENGINEERING
From Generative Text to Generative Science
Most AI models today are linguistic — they are trained to write convincing text, not to solve physical problems. For deep tech R&D, a hallucinating chatbot is a liability. The AI for Research & Engineering Division builds the "Brain" of NanoTRIZ.
We exist to solve the "Cognitive Limit Contradiction": human researchers cannot read 5,000 papers a day or visualize 10-dimensional parameter spaces, yet modern innovation requires exactly that. We build Physics-Informed AI and RAG (Retrieval-Augmented Generation) architectures that act as tireless, logically rigorous co-inventors.
OUR MISSION: THE "CO-PILOT" FOR DISCOVERY To transition science from a manual, intuitive craft to a digitally augmented discipline. We build sovereign AI systems that ingest proprietary R&D data, map knowledge gaps, and propose verifiable engineering solutions without hallucination.
CORE CAPABILITIES
We build the Operating System of Discovery:
1. The "Invention Engine" (TRIZ-AI Integration) Algorithmic Creativity.We fine-tune Large Language Models (LLMs) specifically on the logic of TRIZ (Theory of Inventive Problem Solving) and global patent databases. Unlike standard AI, our models are trained to look for contradictions, not just statistical likelihoods.
The Advantage: It breaks "Psychological Inertia," suggesting non-obvious solution pathways that human experts often overlook due to bias.
Applications: Automated patent circumvention, root cause analysis, and novel system design.
2. Scientific Knowledge Graphing (Advanced RAG) Connecting the Dots.We build Retrieval-Augmented Generation pipelines that digest millions of PDFs, patents, and internal lab reports. We map these into a structured Knowledge Graph, linking concepts (e.g., "Material A" -> "Property B" -> "Application C").
The Advantage: Allows researchers to "query the collective intelligence" of their entire field instantly. "Show me every material used for X that failed due to Y."
Applications: Accelerated literature review, competitive intelligence, and hypothesis generation.
3. Physics-Informed Neural Networks (PINNs) AI That Obeys the Laws of Physics.Standard AI generates data that looks real. We train models that incorporate differential equations (Navier-Stokes, Schrödinger) into their loss functions.
The Advantage: Fast simulation. Our models can predict fluid flow or stress distribution 1000x faster than traditional CFD/FEA solvers, enabling real-time design iteration.
Applications: Real-time digital twins, material property prediction, and aerodynamic optimization.
4. Autonomous Research Agents The Lab that Never Sleeps.We design agentic workflows (using frameworks like LangChain/AutoGPT) where AI agents can plan an experiment, write the code to run it, analyze the result, and iterate—with human supervision only at critical checkpoints.
The Advantage: Turns a researcher into a "Research Manager," scaling their output by order of magnitude.
OUR PROCESS: THE "COGNITIVE ARCHITECTURE"
We do not just prompt AI; we engineer it.
Data Ingestion & Cleaning: We convert unstructured scientific data (PDFs, tables) into vector embeddings.
Logic Mapping: We overlay a "Reasoning Layer" (TRIZ/Physics rules) to prevent the AI from proposing impossible solutions.
Generative Solving: The AI proposes candidate solutions or designs.
Verification Loop: Candidates are checked against physical simulators or constraints before being presented to the human engineer.
HOW TO ENGAGE
R&D Organizations: Commission a "Sovereign Brain"—a private, secure RAG system trained exclusively on your internal R&D history and trade secrets (no data leakage to public models).
Software/Tech Firms: Partner with us to build Domain-Specific Copilots for specialized engineering fields (e.g., a "Copilot for Polymer Chemistry").
Universities: Engage us to deploy our "AI Research Assistant" platform to accelerate PhD student output.
