Technology
A physics-first intelligence stack for high-stakes control.
Under the hood, MMI is a layered system: sensing pipelines for messy real-world signals, control-theoretic world models that learn stable regimes, and evaluation rails that keep deployments observable, reproducible, and policy-ready.
Architecture
How the stack is wired
MMI runs as a layered system. Raw signals move through sensing pipelines, control-theoretic world models, and evaluation rails, then surface as decision interfaces your teams can act on.
Sensing and data ingress
We wire up streams from instruments, logs, and partner systems into versioned datasets with schemas, data quality checks, and reproducible ingestion scripts.
Signal processing and features
Domain specific transforms denoise and compress the signals into features, combining physical models, simulations, and learned representations where it helps.
World models
Control oriented world models learn the dynamics of your system, track uncertainty, and expose stable latent states for downstream planners and analysts.
Control and planning heads
On top of the world models, we attach policies and planners that generate scenarios, recommendations, and alerts under explicit constraints and reward structures.
Evaluation and safety rails
Every deployment runs through eval suites, monitors, and guardrails that track drift, robustness, and misuse risk across both data and model behavior.
Interfaces and policy surfaces
Outputs ship through APIs, mission dashboards, and briefings that preserve provenance from raw data and models to human facing decisions and policies.
Field feedback loop
Real world outcomes, experiments, and operator feedback route back into the stack as new data and labels, closing the loop for retraining and model governance.
Outcomes
What the platform delivers
MMI’s stack produces concrete artifacts at every layer: versioned datasets, stable world models, evaluated policies, and decision surfaces with full provenance. Each stakeholder sees outputs they can verify, audit, and deploy.
For research teams & PIs
- Versioned datasets with schemas, lineage, and reproducibility metadata.
- World models with documented assumptions, calibration traces, and uncertainty estimates.
- Experiment registries, open notebooks, and artifacts ready for publication or grant review.
For technical program owners
- Mission dashboards tying sensing, models, and planners into a single operational flow.
- Audit ready links from raw data to recommendations with explicit model behavior summaries.
- Go / no go criteria, SLA aligned checks, and readiness gates mapped to federal standards.
For security & governance leads
- Controls for sensitive corpora, including RBAC, sealed storage, and redaction pipelines.
- Logs, model cards, eval reports, and event trails suitable for oversight and external inspection.
- Separation of open, restricted, and export controlled outputs with automated enforcement.
For the broader ecosystem
- Reference pipelines, model templates, and evaluation suites released where it is safe to do so.
- Reusable patterns for high signal sensing, control oriented modeling, and safe deployment.
- Trusted signals for future collaborators, reviewers, and funders evaluating program quality.