DeFi All Odds combines advanced statistics, AI, and simulation to help teams evaluate workforce readiness, operational capacity, and logistics decisions. Our technology foundation was developed through NSF SBIR Phase I and II research.
Identify work to stop, automate, or augment, and where human judgment stays essential.
02 / FROM METHOD TO MISSION
Mission applications
These concepts illustrate mission applicability, not agency customers, contract awards, or deployed systems.
01 · ARMY LOGISTICS
A clearer chain of custody.
Working with device and connectivity partners, a hub-and-tag sensor architecture could capture asset location, condition, tamper signals, and handoffs. Our contribution would focus on data models, analytics, and software integration; the concept requires new hardware integration and field validation for densely packed cargo and intermittent connectivity.
Encrypted custody records would be prepared for a government relay endpoint and a blockchain-ready accountability layer. This is an upstream data concept, not a replacement for the government’s blockchain.
Validation focus: field connectivity, device-to-asset identity, data integrity, and integration with government interfaces.
Government relayPrepared for the existing system of record
Conceptual data flow · subject to integration testing
02 · DEFENSE LOGISTICS AGENCY
Mission Twin. Test the move before making it.
A workforce digital twin could combine a workforce hierarchy database, a recommendation engine, and a commander what-if dashboard. Leaders could compare reassignment, backfill, and surge options alongside potential effects on source missions.
Task-level analysis would classify work as stop, automate, augment, or human-led, making the role of AI explicit while preserving human judgment.
Validation focus: new domain adaptation and validation are needed for DLA use, including testing productivity and readiness outcomes against relevant evidence.
Commander what-if dashboardCompare tradeoffs before deciding
Conceptual workflow · human-led decisions
03 · SELECTIVE SERVICE SYSTEM
Find the constraint. Then test the response.
Combine aggregate readiness discrete-event simulation with Monte Carlo analysis to explore workload, staffing, capacity, queues, and disruption across the full pipeline, including interactions between stages.
Rank candidate interventions from a government-approved library, then rerun the simulation to verify modeled impact against the baseline. Report uncertainty, downstream tradeoffs, and whether an improvement is repeatable.
Authority boundary: government decision authority remains unchanged. The system would not select registrants or make individual medical or legal decisions. Analysis is limited to aggregate simulation runs.
A recommendation is a hypothesis to test
01
Simulate the full pipelineBaseline · uncertainty · bottlenecks
Rerun + compareTest modeled impact before human review
Simulation evidence is not proof of real-world impact
03 / ABOUT DEFI ALL ODDS
Research origins. Mission-focused questions.
Our statistical and software foundation grew from workforce research funded through the National Science Foundation’s Small Business Innovation Research program, NSF SBIR Phase I and II.
The patent-pending recommendation engine brings a reusable method for turning data into ranked recommendations. Applying that foundation to government missions requires new domain-specific development, operational data, constraints, and validation.
NSF funding supports the research foundation. It is not an endorsement by NSF or any government agency of these mission applications.
04 / CAPABILITIES
Dynamic Recommendation Generation
From the evidence you have to the options worth testing.
01
Ingest what you already hold
Start with available operational records, structured datasets, and approved parameters. Define what is usable, what is missing, and where new evidence is needed.
02
Model without distribution assumptions
Use non-parametric bootstrap methods and Monte Carlo analysis without prescribing a fixed distribution for observed data. Sampling quality and model assumptions still matter.
03
Return ranked, defensible options
Compare candidate actions with their drivers, uncertainty, and tradeoffs. Confidence is conditional on the model and data, not a guarantee of operational performance.
Synthetic data and simulation outputs are explicitly labeled. They support exploration; they do not substitute for observed operational evidence.
THE MODEL WORKFLOW
Built to question, compare, and re-test.
01
Configure
Agree on the objective, data, constraints, and government-approved decision criteria.
02
Simulate
Explore baseline, surge, and disruption scenarios across uncertain inputs.
03
Quantify
Identify drivers, interactions, bottlenecks, and uncertainty in the outcomes.
04
Recommend & re-test
Rank candidate actions, rerun the model, and compare results before a human decision.
05 / WHY US
The reasoning matters as much as the ranking.
Decision support should make the evidence easier to examine and the reasoning easier to explain.
Transparent drivers
See the conditions and interactions behind an option, not just a score.
Reproducible analysis
Retain data and model versions, parameters, scenario identifiers, and random seeds.
Domain adaptation
Translate the research foundation into mission-specific models, then validate against relevant evidence.
Human authority
Government teams define the constraints, review the evidence, and retain decision authority.
06 / COMPANY & TEAMING
Let's continue the conversation.
We invite government program teams, prime contractors, and technology partners to discuss mission needs and upcoming opportunities. We are interested in joining complementary teams where our modeling, simulation, and software capabilities support a shared mission.
