We study how machines learn to navigate human decisions, how to quantize that intelligence to run anywhere, and how to ground it in the messy reality of commercial environments where the only metric is revenue.
We work on the problems that do not have clear answers.
Commercial decision-making involves imperfect information, multi-agent dynamics, delayed and sparse rewards, and an environment that changes based on the actions you take in it. There is no ground truth. There is only the outcome.
So we organized our research into three pillars: how intelligence learns, how it quantizes, and how it grounds in reality. Each pillar feeds the others. RL discovers the strategies. Quantization makes them portable. Reality ensures they work.
Commercial environments shift continuously. Competitors enter, budgets change, organizations restructure. The reward landscape is never the same twice.
Buying decisions involve 6-12 humans with conflicting incentives, incomplete information, and evolving preferences. The observation space is vast and partially hidden.
A deal takes months. The reward signal arrives once. Credit assignment across hundreds of interactions where external noise dominates is the fundamental challenge.
Enterprise data cannot leave the building. The model must be powerful enough to replace the cloud and small enough to fit on a laptop. This is a quantization problem and a research problem.
Commercial environments are non-stationary, partially observable, and adversarial. Standard supervised learning cannot solve problems where the reward signal is a human saying yes after months of interaction. We apply reinforcement learning to train agents that learn optimal strategies through outcome-based experience – not instruction tuning.
Training language models to operate as decision-making agents in complex commercial environments. We build simulation environments where LLMs learn to navigate multi-stakeholder negotiations, competitive dynamics, and organizational politics through outcome-based reinforcement.
Developing reward models for commercial outcomes where the signal is sparse, delayed, and confounded by variables outside the agent's control. The core challenge: credit assignment across long-horizon action sequences where external factors introduce noise.
Encoding human decision-making behavior into state representations that capture intent, influence, risk tolerance, and political dynamics. A latent space where the distance between states predicts the probability of behavioral change.
Building models that maintain performance in adversarial environments where competitors actively undermine your positioning and information asymmetry is the norm.
The most powerful AI in the world is useless if it requires a data center. We are building a novel quantization pipeline that produces the smallest, fastest models capable of full commercial AI – multi-agent orchestration, deal intelligence, behavioral analysis – running locally on a laptop. No cloud. No data leaves the device. +2% smaller and faster every day.
A novel training pipeline where the quantization process itself uses reinforcement learning to discover optimal quantization strategies. The loop continuously evaluates capability preservation across task dimensions, finding quantization paths that human engineers would never discover manually.
Traditional quantization treats all weights equally. We decompose model capabilities into orthogonal dimensions – reasoning, language, domain knowledge, tool use – and apply different quantization strategies to each, preserving what matters most for commercial AI tasks.
Real-time evaluation framework that measures capability degradation during quantization with sub-percentage precision. If the model loses 0.3% on negotiation strategy but gains 2ms latency, we surface that tradeoff explicitly. No hidden quality loss.
Designing the runtime, memory management, and inference engine for models that run entirely on consumer hardware. Metal on macOS, DirectML on Windows, Vulkan on Linux. Every watt of GPU compute utilized, every byte of VRAM accounted for.
AI that performs well on benchmarks but fails in production is not intelligent – it is theatrical. Reality research focuses on grounding models in the messy, contradictory, politically charged environments where revenue teams actually operate. The gap between demo and deployment is where most AI dies. We close that gap.
Designing architectures that maintain coherent deal context across hundreds of interactions over months-long time horizons. Selective attention: knowing what to remember, what to forget, and what to surface at each decision point in a living deal.
Building evaluation frameworks for AI systems in domains where identical actions produce different outcomes depending on unobservable state. Standard benchmarks fail when the environment is partially observable and non-stationary. We build evals that work anyway.
Agents do not operate in isolation. They work alongside humans whose behavior changes the environment. The optimal policy must account for human judgment, adapt to it, and improve because of it – not despite it.
Mapping the hidden dynamics that govern enterprise decisions: political capital, risk tolerance curves, budget cycle timing, competitive threat perception. Building models that understand why a VP says "not now" versus "not ever."
Our approach
We do not publish papers for citation counts. Every research project exists because it solves a problem in production. When the quantization pipeline produces a smaller model that preserves deal intelligence, that model ships to customers within days.
The feedback loop between research and product is measured in weeks, not years. Our training environments are built from real deal data, real outcomes, and real competitive dynamics. Our quantization benchmarks run against real commercial tasks, not academic datasets.
We believe the most important AI research in the next decade will happen at the intersection of these three pillars: intelligence that learns from outcomes, quantizes to run anywhere, and works in the real world. That is where we work.
Lab principles
We optimize for the binary outcome: did the deal close. Engagement metrics, click rates, and activity volume are proxies. We measure the thing that matters.
Our training data comes from actual deal histories with actual outcomes. Our quantization evals run against real commercial tasks. Synthetic benchmarks are for exploration only.
We do not plan in quarters. We improve by 2% every day. Compounded over a year, that is a 1,377% improvement. Small, relentless gains in model quality, quantization efficiency, and grounding accuracy.
If the model requires a data center, we have not quantized it enough. The goal is intelligence that runs on the device, respects data sovereignty, and needs no cloud.
If the problem can be solved with rules and templates, it is not a research problem. We focus on problems that require learning, quantization, and grounding in complex reality.
Every model we train, every quantization path we discover, every environment we build makes the next one better. We invest in infrastructure that compounds.
“The hardest problem in AI is not reasoning or code generation. It is teaching a machine to navigate a room full of humans who each want something different, fear something different, and will only say yes when conditions you cannot observe are met. And then quantizing that intelligence small enough to run on their laptop without a single byte of their data leaving the building.”
“That is the problem we work on. Every day.”
Adrata Research Lab
We are building a research team across all three pillars. If you think about reinforcement learning, quantization, or grounding AI in complex reality, we want to hear from you.
Get in touch