Current Projects & Initiatives

Adhishtanam Neural Engine (ANE)

ANE is the core platform powering our product initiatives. It is a multimodal neural foundation model that maps sensory inputs — text, audio, video, and imagery — directly to predicted brain activations. Organizations license ANE to integrate neural prediction into their own workflows, from creative testing to safety monitoring.

Wreltik

A live product built on ANE that delivers neural predictions for advertising and short-form content. Wreltik quantifies attention, emotional engagement, and memory encoding before an ad or reel goes live — replacing guesswork with brain-level data. Visit wreltik.com to learn more.

Pharma & Chemical Combination Intelligence

We are building AI models for pharmaceutical formulation and chemical combination discovery. The goal is to move beyond trial-and-error chemistry toward simulation-driven prediction — identifying viable formulations and eliminating non-viable ones before a single compound is mixed in the lab.

Comprehensive Chemical Database

The foundation of this system is a structured knowledge base covering all chemicals used in pharmaceutical formulations — active pharmaceutical ingredients (APIs), excipients, solvents, stabilizers, and co-factors. Each chemical entry includes molecular structure, physicochemical properties, reactivity profiles, safety classifications, and known interaction data. This database also encodes basic chemistry fundamentals — bonding behavior, solubility principles, pH dependence, and thermodynamic stability — giving the models the grounding they need to reason about what combinations are chemically plausible.

Combination Discovery & Simulation

Our models explore the combinatorial space of chemical formulations, searching for new combinations that meet target profiles for stability, bioavailability, and therapeutic effect. Every combination is evaluated with precise weight ratios for each constituent chemical, since composition proportions are as critical as the ingredients themselves. A formulation that works at a 70:30 ratio may be ineffective or dangerous at 50:50.

Simulations run across multiple dimensions simultaneously — predicting how a given combination with specific weights will behave under physiological conditions, how the chemicals interact with each other over time, and whether any degradation pathways or antagonistic effects emerge. By simulating before synthesizing, we eliminate the vast majority of non-viable candidates computationally, leaving only the most promising formulations for laboratory validation.

Weight-Aware Simulation Engine

Our simulations are composition-aware — each chemical is evaluated at its specific weight or concentration within the combination, not just as a binary present-or-absent ingredient. By computationally eliminating combinations that fail core criteria, we dramatically narrow the experimental search space. What remains is a ranked set of high-confidence candidates ready for wet-lab validation.

Other Product Tracks

• Safety Intelligence Systems — Real-time cognitive load monitoring for operators in nuclear, aviation, and rail environments, predicting fatigue before it leads to critical errors.
• Adaptive Entertainment Interfaces — Game engine integrations that adjust difficulty, pacing, and atmosphere based on predicted player emotional and cognitive states.
• Behavioral Decision Models — Tools that quantify the Reward-Pain ratio of consumer experiences, moving organizations beyond surface-level metrics to neurologically grounded insight.
• Neuro-Architecture Platforms — Design evaluation systems that predict how built environments impact stress, focus, and cognitive well-being.

To discuss implementation pathways, enterprise pilots, or research collaboration, reach out through our contact page.