AllenMox helps companies deploy private, efficient AI systems that continuously improve through reinforcement learning — without the cost and complexity of large foundation models.
Most companies want to deploy AI — but run into the same walls every time.
Sending sensitive business data to third-party cloud AI providers creates compliance exposure, IP risk, and regulatory uncertainty — especially in finance, healthcare, and legal sectors. Companies want to run fine-tuned models on their own infrastructure, private cloud, and edge services — but most do not have enough computing power to host large language models at scale, leaving them dependent on cloud providers to host the models for them.
Large foundation models are powerful but often expensive to run and slower than smaller specialized models. API costs typically scale with usage, which can become difficult to predict as applications grow. While these models provide strong general capabilities, adapting them deeply to specific business domains and workflows usually requires additional engineering, such as retrieval systems, prompt design, or fine-tuning. They also do not natively maintain persistent memory about a company's data or evolving priorities, which means organizations must build additional infrastructure to keep the models aligned with their business needs over time.
MoX follows a closed-loop cycle — models get smarter with every real-world interaction.
Set your benchmarks and business goals. We align the model to what success looks like for your use case.
Start with an optimized small or medium model ready for your workflow — no training from scratch required.
As users interact, MoX gathers structured feedback signals from real-world usage automatically.
Reinforcement learning continuously fine-tunes the model against your objectives — no manual labeling needed.
Evaluate each improved version in a safe sandbox. Deploy only when it exceeds your benchmark.
MoX is a complete platform — from open-source model deployment to continuous RL-driven improvement, with full human oversight and privacy control.
Unlike AWS or Google Cloud which bill by API calls or compute hours, AllenMox charges only when your model measurably improves business outcomes and exceeds your agreed benchmark.
Billed per API call or compute hour — whether results improve or not.
Only charged when the model exceeds your benchmark. Evaluate for free.
Set the business objective and success criteria for your use case.
Test and benchmark the model against your real workflows at no cost. Only pay when it delivers.
When the model meets the benchmark, download and deploy directly in your own environment.
MoX keeps refining the model. Each new version goes through evaluation before you decide to upgrade.
Start evaluating MoX against your own business objectives — free. No compute bills. No contracts. Pay only when it works.
MoX is not just a model — it is a complete reinforcement learning platform built for companies that need private, efficient, and continuously improving AI.
Structured memory that learns only what matters.
MoX keeps records of logs and user engagements and organizes the information in the format of vectorized data and a social graph. This structured memory allows the system to understand not just what happened in an interaction, but who was involved, how entities relate, and what signals carry the most weight for improvement.
We only keep records for the data points that are either used for evaluation or fine-tuning — keeping the system lean, privacy-safe, and purpose-driven. No bloated data lakes. No unnecessary retention.
Privacy is built into the architecture. MoX never retains raw data beyond what is necessary for model improvement — and only you control what qualifies.
Human judgment where automation falls short.
The MoX Testing Interface gives your team full visibility and control over how models are performing — in evaluation and in production. Designed for both data and business teams to work together in assessing and guiding model quality.
Some failure modes only humans can catch. MoX is designed so that human judgment is a first-class signal, not an afterthought.
An AI that manages its own improvement strategy.
One of the most distinctive components of MoX is the LLM-as-Judge — an autonomous AI layer that acts as a live product manager for your model's improvement cycle. Rather than relying on human product managers to define what to optimize and when, the Judge does this automatically based on real-world outcomes.
The Judge does not just evaluate — it strategizes. Each iteration is an informed decision, not a scheduled job.
A versioned history of your model's evolution.
Every fine-tuned version of your model is stored, versioned, and available for evaluation and deployment at any time. The repository gives you complete control over which version runs in production — you can roll back, compare side-by-side, or promote a new version after it passes your benchmark.
Each version is independently testable in the MoX evaluation environment before deployment. You decide when — and whether — to upgrade.
You never lose a model. Every iteration is preserved, reproducible, and ready to redeploy if needed.
Start fast. Scale without compromise.
MoX is built on a curated selection of open-source small- to medium-sized pre-trained models, optimized for speed, efficiency, and deployability in private infrastructure. These models are the starting point for every fine-tuning cycle — you never have to train from scratch.
Because these are small and medium-sized models, they can run on your own infrastructure — private cloud, on-premise, or edge — without the compute requirements of large foundation models. This is the architectural decision that makes privacy-first deployment practical, not just theoretical.
Open-source means no vendor lock-in. You own the model, the weights, and the deployment. AllenMox improves it — you control it.
Evaluate MoX against your own business objectives — free. Pay only when it exceeds your benchmark.
MoX enables IoT companies to unlock the full potential of their sensor data by deploying local AI models that continuously improve through reinforcement learning — keeping sensitive data private.
The IoT industry generates large amounts of rich and sensitive data from sensors deployed across devices, machines, and local ecosystems. However, much of this data remains underutilized because extracting actionable insights often requires complex infrastructure, cloud processing, and continuous manual model tuning.
With the MoX infrastructure, companies can deploy efficient models directly on devices or in local environments such as edge services. These models learn from real-world actions and outcomes, improving decision-making over time while keeping sensitive data private.
Prevent failures before they happen.
Industrial equipment and critical infrastructure require precise maintenance schedules to avoid costly failures and downtime. MoX uses reinforcement learning to continuously fine-tune local models that analyze sensor data and operational patterns.
Over time, the models learn to predict when maintenance is needed and which components require attention, allowing companies to:
Because MoX models run locally on edge infrastructure, they can act on sensor signals in real time — without waiting for cloud round-trips that delay critical decisions.
Coordinate smart devices. Reduce waste.
Modern buildings contain many smart devices such as thermostats, breakers, HVAC systems, and lighting controllers. While each device collects useful data, coordinating real-time decisions across all of them can be complex.
MoX enables IoT companies to deploy local models that make real-time energy optimization decisions across multiple devices simultaneously. These models can:
Instead of relying on constant cloud communication, MoX allows these models to operate locally with reinforcement learning feedback loops that improve decisions over time.
The MoX platform uses an LLM-based PM and Judge system to evaluate outcomes, analyze performance, and guide future model improvements — ensuring local models stay aligned with business objectives.
Deploy private, self-improving AI models on your edge infrastructure. Start evaluating free.
MoX is built for environments where AI must continuously adapt. Explore the industries and use cases where self-improving, privacy-first AI delivers the most value.
AI agents that continuously adapt strategies based on battlefield intelligence, adversarial actions, and rapidly evolving environments.
Local AI models that unlock sensor data for predictive maintenance and real-time energy optimization — privately, at the edge.
Robots, drones, and automation platforms that learn from real-world interactions and continuously improve performance.
Adaptive AI that detects emerging fraud patterns, adjusts risk scoring models, and evolves compliance workflows in real time.
Adaptive AI for medical devices and health systems that continuously improve using real-world clinical data while maintaining privacy.
Private AI agents that assist with case research, risk assessment, and legal drafting — entirely within the firm's secure infrastructure.
AI that adapts as fast as the environment changes.
MoX enables organizations operating in highly dynamic environments to deploy AI agents that continuously adapt their strategies based on new data from the surrounding environment. This may include battlefield intelligence, adversarial actions, competitive activity, or rapidly evolving environmental conditions.
MoX uses real-time operational data for two key purposes:
An LLM-based Judge and Product Manager (PM) system evaluates the performance of deployed AI agents based on real-world outcomes and incoming signals.
Based on these evaluations, MoX automatically determines the strategy for the next model iteration. Reinforcement learning pipelines incorporate the latest operational data to retrain and improve the models.
This enables AI systems to continuously improve their decision-making and adapt to changing environments without requiring constant human intervention.
Sensor data that finally works for you.
The IoT industry generates large amounts of rich and sensitive data from sensors deployed across devices, machines, and local ecosystems. However, much of this data remains underutilized because extracting actionable insights often requires complex infrastructure, cloud processing, and continuous manual model tuning.
MoX enables IoT companies to unlock the full potential of their sensor data by deploying local AI models that continuously improve through reinforcement learning.
MoX analyzes sensor data and operational patterns to predict equipment failures before they occur. This allows companies to:
Modern buildings contain many smart devices such as thermostats, HVAC systems, breakers, and lighting controllers. MoX allows IoT companies to deploy local models that coordinate decisions across these devices in real time to:
Because models run locally, sensitive data remains private and systems can make decisions without relying on constant cloud communication.
Machines that get smarter on the job.
Autonomous systems such as robots, drones, and industrial automation platforms operate in complex and unpredictable environments where decision-making must continuously adapt to new conditions.
MoX enables robotics platforms to deploy local AI models that learn directly from real-world interactions and improve through reinforcement learning. Robotic systems powered by MoX can:
The MoX Judge and PM architecture evaluates the outcomes of robotic actions and triggers improved model iterations — allowing robots to continuously improve their behavior while maintaining safety and operational constraints.
Stay ahead of threats that haven't appeared yet.
Financial systems operate in environments where new risks, fraud patterns, and market behaviors constantly emerge. Traditional rule-based systems often struggle to keep up with these evolving threats.
MoX enables financial institutions to deploy AI systems that continuously adapt to new and previously unknown risk patterns. Using reinforcement learning and real-time feedback loops, MoX can:
Instead of relying on static rule systems, MoX allows organizations to automatically evolve their risk models and workflows as new threats emerge — enabling faster response to financial crime while reducing the operational burden on risk and compliance teams.
AI that improves with every patient interaction.
Healthcare systems and medical devices generate large volumes of real-time patient and operational data. MoX enables healthcare organizations and medical device manufacturers to deploy adaptive AI systems that continuously improve using real-world medical data while maintaining privacy and security requirements.
AI models embedded in devices such as diagnostic systems, imaging equipment, or monitoring devices can improve their performance as they observe more real-world cases.
MoX can analyze signals from wearable devices and hospital monitoring systems to detect early warning signs of health deterioration. This enables:
MoX can help healthcare systems optimize operational workflows by learning from patient flow, treatment outcomes, and operational signals. Over time, these systems improve decision-making while maintaining transparency and oversight through MoX's evaluation and reporting capabilities.
All models run within your infrastructure. Patient data never leaves your environment — meeting the strictest privacy and compliance requirements.
Private AI that learns your firm's legal expertise.
Legal firms manage large volumes of highly sensitive and confidential data, including contracts, case files, legal research, and internal communications. Because of privacy concerns and regulatory requirements, many firms cannot rely on public cloud AI services to process this information.
MoX enables legal firms to deploy private, specialized AI agents directly within their secure infrastructure, allowing them to leverage AI while maintaining full control over sensitive data. These agents continuously improve through reinforcement learning and feedback from real legal workflows, enabling them to better understand each firm's practice areas, strategies, and case history.
MoX agents can assist legal teams in organizing and analyzing case materials. These agents can:
Over time, the models learn from the firm's internal knowledge base and legal outcomes, becoming more specialized for the firm's practice areas.
Legal cases often involve complex risk evaluations and strategic decision-making. MoX enables AI agents to help lawyers assess potential risks by analyzing:
The system can highlight potential legal risks and suggest strategic considerations while leaving final decisions to legal professionals.
MoX agents can assist attorneys while drafting legal letters, contracts, and legal arguments. These agents can:
As legal teams use MoX agents in their daily workflows, the system continuously improves through reinforcement learning. The LLM Judge and PM architecture evaluates outcomes, analyzes how agents assist with cases and drafting, and guides improvements to future model versions.
Because MoX runs locally, the system works directly with confidential case files without exposing sensitive information to external AI services — giving legal firms AI capability without privacy compromise.
Start evaluating MoX against your business objectives — free. Pay only when it delivers results.
AllenMox is built by a team with deep roots in enterprise AI, distributed systems, and reinforcement learning — with experience from Amazon and Johns Hopkins University.
AllenMox Inc., founded in 2020, is led by its Founder and CEO, a former Principal Product Manager at Amazon, with over 12 years of experience building SaaS and B2B products across multiple industries, including IoT and business intelligence.
Throughout his career, he has led the development of large-scale technology platforms and AI-driven products, focusing on bringing advanced technologies into real-world business applications.
His experience spans product strategy, AI systems, and enterprise platforms operating at global scale.
The founding team behind AllenMox and the MoX platform includes principal engineers and scientists from Amazon as well as researchers from Johns Hopkins University.
The team brings together deep expertise in:
To learn more about the founding team or to connect with the MoX leadership team, please submit a request through our contact form.
AllenMox brings together enterprise AI expertise and cutting-edge research to build the self-improving AI platform of the future.