Different sectors. One rule: prove it first.
We focus on six sectors, led by defense and federal research, and every one of them has a commercial side. The problems differ. The rule does not: you approve the test before we build, and nothing goes live until it passes.
Defense & National Security
Programs hold decades of engineering knowledge in documents and more data than analysts can read, yet many AI tools assume the data can leave the building. Anything put into service must be approved to run on the program's own network (an authority to operate), often at denied, disconnected, intermittent or limited-bandwidth (DDIL) sites. A wrong answer shows up later as a part built to the wrong drawing or a mission planned on bad data.
Nontraditional defense contractor10 U.S.C. § 3014
We built our document-to-model pipeline at our own expense and measured it on a synthetic corpus with no controlled or classified material: 18 documents in 7 formats, with 40 defects planted on purpose. Technology readiness level 3 (proof of concept). F1 is a standard accuracy score from 0 to 1 that counts both missed items and false finds. Cable connectors are one of our two strongest element types; others score lower, and the full scorecard is available on request. The corpus is synthetic and we built it, so these numbers show the method works; they do not predict how it will score on your documents. On an engagement, we measure on a sample of yours before agreeing a target.
What we do
- AI that stays on your networkAssistants and agents that cite their sources, show each user only what they may open, send uncertain answers to a person and log every action. We deliver the evidence your authorizing official needs; the decision is theirs.
- Documents into engineering modelsInterface documents and cable run sheets become a model engineers can query, each element traced to its source page and row. It is a practical first step toward the model-based engineering DoDI 5000.97 directs.
- Assurance you can defendTest, evaluation, verification and validation (TEVV) against a pass mark locked before development, and red-teaming for prompt injection and data leakage (MITRE ATLAS). Zero trust designed to NIST SP 800-207 and baselined against the DoD Zero Trust Strategy.
- Ready crews and maintainersTraining that checks understanding at each critical step, in each person's own language, and cited answers from technical manuals at the point of work, offline where there is no network.
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Federal Research & Civilian Agencies
Transportation, aviation and the science agencies fund applied research to answer specific questions: can this be measured, will it hold up in the field, is it safe to rely on. They buy it through broad agency announcements and small business research programs (how to work with us), and they judge proposals on whether the method can be tested. That is how we work: the test is agreed first, and the results come with the evidence behind them. Agencies that serve the public also run critical services on systems built decades ago; for those, we modernize one function at a time and build AI to the practices OMB M-25-21 sets for high-impact uses.
What we do
- Research built to be testedA method with its test agreed up front, results reported against it, and the evidence behind each result, as prime or with a research partner.
- Modernization one workload at a timeLegacy systems replaced one function at a time (the strangler-fig pattern), each move checked and reversible, while services keep running. Government cloud moves are designed to inherit controls from your existing FedRAMP authorization.
- Data and analytics tied to decisionsPipelines and decision views built around a named decision, such as inspection triage, and its owner. We record how it is made today, then measure time to decision and reversals against that baseline.
- AI you can defend, bought wellTest results, model cards and monitoring plans as evidence for those M-25-21 practices. When you buy AI, we help write test criteria, data rights, drift monitoring and off-ramps into the contract (OMB M-25-22).
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Defense Suppliers & Technology Companies
DoD solicitations can require a CMMC status at award (DFARS 252.204-7021), and many small suppliers learn where they stand only when a contract requires it. The third-party phase planned for November 2026 is suspended pending a DoD review, but NIST SP 800-171 Rev. 2, Level 2 (Self) and the annual affirmation still apply.
Before offering CMMC readiness to anyone, we took Artheus through the self-assessment path: a system security plan, evidence for each NIST SP 800-171 Rev. 2 requirement, a score of 110 of 110 posted in SPRS and CMMC Level 2 (Self) affirmed. It is a self-assessment, not a third-party certification.
What we do
- CMMC and NIST SP 800-171 readinessWe scope your controlled-information boundary, write the system security plan and plan of action and milestones (POA&M), and map evidence to the 320 objectives in NIST SP 800-171A. We also help with your SPRS entry and annual affirmation.
- A nontraditional teammateArtheus is a nontraditional defense contractor (10 U.S.C. § 3014). On a prototype Other Transaction agreement, significant participation by one is a way a project meets the condition in 10 U.S.C. § 4022(d)(1)(A).
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Energy & Critical Infrastructure
The control systems that run plants, grids and pipelines were built for reliability long before they were connected to anything. Operators now have to secure them and use their data without risking uptime. A routine network scan can knock an older controller offline, and a wrong move on a live control network can trip a breaker or force an outage.
What we do
- Operational technology securityPassive asset discovery first (it listens rather than probes), then zones per the Purdue model and ISA/IEC 62443, following NIST SP 800-82 Rev. 3. You get an asset inventory, a zone map and a dated plan for every gap.
- AI for grid and plant decisionsLoad forecasting, predictive maintenance and outage analysis from your operating and maintenance records, designed to run on your premises and scored on months the model never saw. The AI advises; a person approves any control-system change.
- A ready field workforceExtended reality (XR) rehearsal for field tasks unsafe to practice live, and training in each worker’s own language with comprehension checked per step against your current baseline. FirstShift, our working prototype, is designed for this.
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Manufacturing & Industrial
New workers have to learn safety-critical procedures fast, often in a second language, and a signed training record does not prove anyone understood. The engineering data behind the product sits in drawings, spreadsheets and documents that rarely agree.
FirstShift, our working prototype: adaptive, multilingual AI that checks that frontline workers understand safety-critical procedures, in their own language.
What we do
- Onboarding that proves understandingAdaptive, multilingual onboarding that checks understanding at each critical step, re-teaches on a miss and leaves a record a safety manager can audit. Training is in a language each worker understands, as OSHA expects.
- Engineering data you can trustBills of material and port tables reconciled into one authoritative model. Conflicts go to an engineer before they become rework or a recall. We report accuracy per element type and the minutes to correct each.
- AI on the factory floorAssistants that answer from your own procedures and manuals, cite the page, say when the manuals do not answer and run inside your network. Accuracy is scored on real floor questions you approve first.
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International Development
Development programs are judged on results, yet the data behind them is often collected on paper, reported late and hard to verify. Donors want evidence they can check, without extra burden on field teams. When the data is weak, funding decisions rest on figures nobody can confirm.
What we do
- Monitoring and evaluation systemsDesigned to collect data offline, sync when a connection returns, track indicators against a results framework and check each figure for validity, integrity, precision, reliability and timeliness before it is reported.
- Independent third-party monitoringDesigned so each piece of evidence is time-stamped and tagged with its location, letting an independent party verify what was delivered, including in hard-to-reach areas.
- Training in the local languageMultilingual training and certification for field staff and partners, with a check that each person understood.
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Where the same methods apply
Our home-page tour visits three markets outside the six above. We do not claim experience in them. They are places where the same work applies: clean data, AI tested before anyone trusts it, and security built in from the start.
Finance
A model error here becomes a bad credit or pricing decision. Generative AI sits outside the banking agencies’ model-risk guidance (SR 26-2), so each bank’s own governance has to cover it. Our method produces the evidence that governance needs: a test locked before the build, results against it, and monitoring after launch.
Biotech and life sciences
In a lab, an AI result that does not hold up can cost weeks of experiments. We test models against held-out results nobody tuned them on. Then we write down what each model was tested to do, how it scored and where it falls short.
Retail
A new layout or procedure pays off only when staff can use it on opening day. Immersive training lets teams rehearse in a headset first, scored step by step. We then measure task errors against a baseline recorded before the change.
Have a problem in one of these sectors?
If your industry is not listed here, the same methods still apply. We are happy to talk it through.