Part of the DoW SBIR/STTR FY27 Release 2 topic list. Browse all 83 topics by industry or service.
Topic DAF27BZ02-DV023 from the DoW SBIR 2027 BAA, Release 2. Pre-release now; proposals due January 6, 2027, 12:00 PM ET.
Quick answer
The Department of the Air Force wants an executable modeling and simulation environment that unifies FinOps and MBSE to predict cost, resource use, and performance for the Rapid Sustainment Office (RSO) CBM+ hybrid-cloud system. It is a Direct-to-Phase II (D2P2) SBIR topic, so no Phase I awards are anticipated. Proposals are due January 6, 2027, 12:00 PM ET.
Solicitation at a glance
- Topic number: DAF27BZ02-DV023
- Official title: A unified model for specification and simulation to design, acquire, and sustain a hybrid- or multi-cloud large scale data analytics system that balances cost, performance, modularity, and security.
- Agency: Department of the Air Force
- Program: SBIR (DoW SBIR 2027 BAA, Release 2)
- Award type: Direct to Phase II (no Phase I awards)
- Award amount: Up to $2,000,000. Source: Department of the Air Force Release 2 instructions in DSIP.
- CMMC level: Level 2 (Self)
- ITAR: No
- Technology areas: Air Platform, Information Systems
- Pre-release (direct TPOC contact allowed): November 4 to December 2, 2026, 8:00 AM ET
- Opens in DSIP: December 2, 2026, 8:00 AM ET
- Proposals due: January 6, 2027, 12:00 PM ET
- Official source: DSIP Topics app (search DAF27BZ02-DV023)
BW&CO's take
What the Department of the Air Force wants
Defense sustainment data systems such as the RSO CBM+ enterprise run on shared cloud and hybrid infrastructure where costs swing with workloads, data egress, and compute allocation. Static MBSE models, separate MATLAB scripts, and spreadsheets cannot simulate this behavior inside the system specification. The Air Force wants a SysML (or equivalent open standard) model coupled with simulation software that ingests telemetry, recalibrates, forecasts CapEx and OpEx, and runs multi-objective optimization across cost, performance, latency, and security, including disconnected on-premise operation. Phase II delivers the integrated environment, an AI/ML or heuristic demonstration, a technical data package, and training for government and A&AS sustainment staff, moving from TRL 4 to TRL 6.
Who is a good fit
- Prior deployments of predictive cost analytics, FinOps platforms, or advanced MBSE tools in complex enterprise IT or defense-adjacent environments.
- Working code that couples SysML or comparable system models with computational or discrete-event simulation engines to model stochastic behavior.
- Pipelines that ingest logs, infrastructure metrics, or telemetry streams to calibrate system models continuously.
- Machine learning regression, multi-objective optimization, or Pareto-frontier analysis applied to predicting resource use or spotting anomalous cost drivers.
Watch-outs
- Direct-to-Phase II: you must document prior Phase I-type work showing TRL 4 maturity. The topic calls basic MBSE syntax studies and standard cloud billing dashboards redundant.
- The model must cover secure, disconnected on-premise operation alongside cloud assets, not only cloud spend.
- Minimum deliverables include a full technical data package, interface control descriptions, and an operator and administrator training package so government and A&AS staff can sustain the tool independently. Budget for this work.
- The topic lists CMMC Level 2 (Self). It is not marked ITAR.
Think your company fits DAF27BZ02-DV023? Talk with BW&CO before the January 6 deadline.
Plan Your DAF27BZ02-DV023 Submission
Official topic text (verbatim from DSIP)
The text in this section is copied word for word from the official DSIP topic as published on November 4, 2026. DSIP is the official source and topics can be amended. Always confirm the current version at dodsbirsttr.mil before you submit. Our commentary above is BW&CO's interpretation, not Government guidance.
Title
A unified model for specification and simulation to design, acquire, and sustain a hybrid- or multi-cloud large scale data analytics system that balances cost, performance, modularity, and security.
Objective
Develop and validate an innovative unified financial operations (FinOps) and Model-Based Systems Engineering (MBSE) solution to solve lifecycle cost, resource predictability, and performance modeling challenges within modern defense Condition-Based Maintenance Plus (CBM+) hybrid-cloud systems.
Description
Modern software-centric data systems across the Department of War operate in highly dynamic, shared cloud and hybrid infrastructure environments. Within complex weapon system sustainment platforms, such as the RSO CBM+ enterprise, accurately predicting and managing lifecycle resource consumption, computing performance, and ongoing operational costs presents a substantial operational challenge. Cloud and hybrid hosting costs fluctuate due to stochastic workload behaviors, data egress, and dynamic compute allocation. Across commercial and defense sectors, approximately 49% of organizations lack dedicated FinOps capabilities, resulting in frequent budget variances exceeding 18% and inefficient allocation of critical resources.
Traditional MBSE methodologies and legacy toolchains (e.g., static Cameo models, detached MATLAB scripts, disconnected spreadsheets) lack the capability to execute high-fidelity, time-dependent simulations directly within the primary system specification model. This fragmentation creates significant lag between architectural planning, empirical software telemetry, and financial governance, severely limiting rapid system analysis and trade-space optimization.
To address this capability gap, this project seeks innovative solutions that bridge DevOps, FinOps, and MBSE into a unified, executable environment. The proposed solution must demonstrate how system design specifications and executable simulation software can coexist within a cohesive architecture, such as an extensible, layered Service-Oriented Architecture (SOA) utilizing Systems Modeling Language (SysML) or equivalent open modeling frameworks. Offerors should propose methods to capture continuous empirical data from operational and developmental environments, allowing the model to dynamically update, calibrate, and predict both Capital Expenditures and Operational Expenditures. The solution must support hybrid operational constraints, verifying that on-premise components sustain secure, disconnected operations while remaining optimized alongside cloud assets.
The entry Technology Readiness Level (TRL) for this D2P2 effort is expected to be TRL 4, progressing to TRL 6 by the conclusion of Phase II, culminating in a fully integrated, operationally viable prototype model and software suite demonstrated under representative operational conditions.
Phase I
As this is a Direct-to-Phase-II (D2P2) topic, no Phase I awards are anticipated. To qualify for this D2P2 topic, the Department of the Air Force expects applicants to demonstrate feasibility through prior "Phase I-type" research and commercialization efforts that do not constitute work undertaken as part of an active SBIR/STTR funding agreement for the identical scope.
The scientific and technical merits of foundational systems modeling, commercial FinOps telemetry, and cloud monitoring tools is established in the commercial marketplace. Therefore, traditional Phase I feasibility studies exploring basic MBSE modeling syntax or standard cloud billing dashboards are considered redundant. Applicants must provide verifiable documentation substantiating that the underlying technology is mature (TRL 4) and that the offeror possesses deep technical capability in unified modeling, simulation execution, and resource forecasting.
Sufficient Feasibility Documentation should include, but is not limited to:
- Commercial or Operational Deployment Records: Verifiable case studies, white papers, or performance data demonstrating prior implementation of predictive cost analytics, FinOps platforms, or advanced MBSE tools within complex enterprise IT or defense-adjacent environments.
- Executable Simulation Metrics: Technical reports, architecture diagrams, or code demonstrations validating the ability to couple visual system models (e.g., SysML or comparable schemas) with computational or discrete-event simulation engines to model stochastic system behavior.
- Telemetry & Feedback Integration Data: Documented capability to ingest continuous empirical log data, infrastructure metrics, or telemetry streams to dynamically calibrate system models or baseline behavioral algorithms.
- AI/ML or Advanced Algorithmic Validation: Preliminary data or benchmark analyses demonstrating algorithmic feasibility (e.g., machine learning regression, multi-objective optimization, Pareto-frontier analysis) to accurately predict resource utilization or detect anomalous cost drivers.
Submission of this documentation confirms that the technology has progressed beyond conceptual formulation and laboratory proof-of-concept into relevant environment validation, establishing the baseline needed to proceed directly to advanced Phase II operational maturation and defense integration.
Phase II
The Phase II effort requires performers to conduct targeted research, development, and operational validation that elevates the proposed technology from TRL 4 to TRL 6. Performers should propose innovative methods to develop, tailor, and demonstrate an executable modeling and simulation platform aligned to the operational challenges of the RSO CBM+ architecture.
To establish feasibility, evaluate effectiveness, and execute the required R&D, Phase II capability demonstrations should address the following operational and technical parameters:
-Unified Executable Architecture: Develop and demonstrate an integrated modeling framework wherein system specifications and simulation software interact seamlessly. Offerors should demonstrate methods to execute multi-objective optimization simulations (exploring thousands of iterations) to map Pareto-optimal balances across lifecycle costs, compute performance, latency, and data security constraints.
·Empirical FinOps Telemetry Loop: Implement mechanisms to ingest live or simulated empirical telemetry from hybrid-cloud environments, demonstrating that model parameters dynamically update to forecast CapEx and OpEx under fluctuating operational tempos and disconnected on-premise operational states.
·Behavioral Modeling: Explore, evaluate, and integrate techniques to capture complex, non-linear system behaviors and enhance predictive accuracy for workload and cost spikes.
·Defense System Integration & Transition Readiness: Demonstrate the capability within an operationally representative RSO CBM+ hybrid-cloud scenario, verifying model fidelity, ease of schema integration, and cyber resilience.
·Knowledge Transfer & Sustainment Enablement: Produce comprehensive technical documentation, data packages, training curricula, and provide collaborative engineering support to enable government engineers and Advisory & Assistance Services (A&AS) personnel to operate, extend, and sustain the tool chain independently.
Phase II Minimum Acceptable Deliverables:
- A comprehensive, unified modeling environment (SysML or equivalent open standard) and integrated simulation software capable of predicting resource consumption, lifecycle costs, and operational performance across both cloud and disconnected on-premise architectures.
- A functional demonstration showcasing the utilization of AI/ML or advanced heuristic techniques to capture, predict, and optimize complex system behaviors within the simulation environment.
- A complete technical data package (TDP), system documentation, interface control descriptions, and an operator/administrator training package with an executable training plan for organic government and A&AS sustainment.
Phase III Dual Use Applications
Potential Military Applications: Beyond the primary target of optimizing the Rapid Sustainment Office's (RSO) Condition Based Maintenance Plus (CBM+) hybrid-cloud system, this technology has incredibly broad military applications. The unified modeling solution can be directly applied to optimize cost, performance, and security for other large-scale data analytics systems, IT architectures, and complex software-centric systems such as aircraft avionics and sensor suites. Furthermore, the executable Model-Based Systems Engineering (MBSE) artifacts produced by this model provide immense value for securing mandatory Department of Defense (DoD) compliance approvals, including Authority To Operate (ATO) packages, safety, airworthiness, and nuclear certifications, depending on the system it is applied to.
Potential Commercial Applications: The commercial sector faces the exact same cloud resource management and budget challenges as the DoD. Currently, 49% of organizations lack a dedicated Financial Operations (FinOps) team for cloud management. Consequently, many of these organizations waste roughly 28% of their cloud budget and are frequently 18% over their annual cloud spend. The commercial application of this unified modeling approach allows enterprise organizations to integrate predictive FinOps and DevOps capabilities directly into their software development and Extract, Transform, Load (ETL) data pipelines. By adopting this technology, commercial entities operating large multi-cloud or hybrid-cloud analytical systems can accurately predict stochastic software behaviors and save tens of millions of dollars annually.
References
Booth, T., Ghosh, S., Yet Another MBSE Methodology (YAMM): A DevOps and Empirically-Based Unified-Modeling, Design, Analysis, and Optimization Methodology for Software-Centric Data Systems. PhD Dissertation
Colorado State University, 2025 Booth, T., Ghosh, S., A Modular Simulation-based MBSE Approach Applied to a Cloud-Based System. 34th INCOSE International Symposium, 2024
Keywords
Unified Modeling Approach; Data System Optimization; FinOps; CBM+; SysML; MBSE; Cost Predictions; Modeling and Simulation; Hybrid-Cloud
Frequently asked questions
What is DAF27BZ02-DV023?
A Department of the Air Force topic in the DoW SBIR 2027 BAA seeking a unified FinOps and MBSE modeling and simulation solution that predicts lifecycle cost, resource consumption, and performance for the RSO CBM+ hybrid-cloud data analytics system.
When is DAF27BZ02-DV023 due?
Proposals are due January 6, 2027, 12:00 PM ET. DSIP pre-release began November 4, 2026, and direct contact with the topic authors (TPOCs) is allowed only until the topic opens on December 2, 2026, 8:00 AM ET.
Is DAF27BZ02-DV023 Phase I or Direct-to-Phase II?
Direct-to-Phase II. No Phase I awards are anticipated. Applicants must show feasibility through prior Phase I-type research and commercialization work that was not done under an active SBIR/STTR agreement for the identical scope.
What tradeoffs does the model need to optimize?
Phase II calls for multi-objective optimization simulations, exploring thousands of iterations, that map Pareto-optimal balances across lifecycle costs, compute performance, latency, and data security constraints, with CapEx and OpEx forecasts that update from empirical telemetry.