Applied AI · Cybersecurity · Governance · Assurance
Orvexen engineers trustworthy AI for high-risk, regulated and mission-critical environments — where capability and assurance are built together, not added after deployment.
Our compliance platform encodes work we already perform for defense industrial base clients.
Detection built from documented impersonation losses — not a solution in search of a problem.
Partners and prospective clients across government contracting and financial services are already engaged.
Products
Each product turns a manual, high-friction workflow into an engineered, evidence-backed system — delivered as an assessment, pilot, or full deployment.
PRODUCT 01 / COMPLIANCE
AI-enabled CMMC compliance platform
Tens of thousands of defense industrial base organizations must prove CMMC readiness. Documentation alone is not enough — they need a practical platform that reduces the time, cost, and complexity of getting and staying assessment-ready.
PRODUCT 02 / DIGITAL TRUST
AI fraud detection for banks & financial institutions
A single deepfake voice call impersonating a CFO can cost a bank millions — and fraud no longer arrives through one channel. SignalVer helps banks and financial institutions verify what’s real across voice, video, text, social media, and identity signals — before funds move.
PRODUCT 03 / SECURE PROPOSALS
Secure AI for RFP analysis and proposal development
Contractors are pasting RFPs, proposals, and sensitive client data into public AI tools — and agencies have noticed. RFPfort is a private, controlled AI environment where proposal intelligence never leaves your boundary. And because winning starts before the RFP drops, RFPfort also finds the sole-source and set-aside openings — 8(a), Tribal 8(a), SDVOSB, HUBZone, WOSB — where agencies award direct. It’s all about being there at the right time.
Core Capabilities
Every product and engagement draws on the same engineering foundation.
01
Governed copilots, agents, decision systems, and human-in-the-loop workflows designed for real operating environments.
02
Security, resilience, identity, monitoring, and response built into the architecture — not around it.
03
Policy, risk, and regulatory expectations translated into practical controls, ownership, and evidence.
04
Performance, explainability, security, and adoption validated — with teams equipped to operate responsibly.
Problems We Solve
The question is not "which tool should we buy?" It is "what outcome must become reliable — and what is preventing it?"
How We Work
A disciplined path keeps innovation connected to mission value, risk, evidence, and operational ownership.
STEP 01
Define the mission outcome, constraints, stakeholders, and the evidence that will be needed.
STEP 02
Design the system, controls, workflow, and operating responsibilities together.
STEP 03
Test performance, security, explainability, traceability, and human oversight.
STEP 04
Deploy, document, train, monitor, and improve through measurable feedback.
Trust Principles
Technology choices begin with the decision, workflow, and operating context.
Claims must be supported by observable tests, records, and traceability.
Identity, data protection, monitoring, and resilience shape the architecture.
Clear ownership and meaningful oversight remain visible at every critical decision.
Policies become practical gates, roles, metrics, and escalation — not shelfware.
Proof Points
Selected engineering outcomes from our team's applied AI work — the same discipline built into every Orvexen platform. Named client case studies available on request.
01
Led TEVV for a generative-AI retrieval initiative, benchmarking semantic search against full-text search to validate real accuracy gains before production.
02
Architected an LLM-powered knowledge graph application with automated entity and relationship extraction and full data provenance across structured and unstructured sources.
03
Built a conversational interface letting analysts query the knowledge graph directly in plain language — no query syntax required.
04
Engineered a custom semantic-similarity model on MPNet transformers that cut manual analysis time by roughly 95% — an estimated 10,000 analyst hours saved.
Sector-Specific AI
Select a sector to see the business problems we solve, the AI systems we build, and the applications clients can request.
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Engagement Pathways
ADVISE
Clarify the problem, operating model, governance, roadmap, and investment priorities.
ENGINEER
Design and build governed AI, cybersecurity, evidence, and decision-support solutions.
ASSURE
Assess performance, security, explainability, controls, evidence, and readiness.
ENABLE
Equip leaders and practitioners with role-based methods, playbooks, and governed use.
Leadership
Orvexen was founded to translate advanced AI research into practical systems for business automation, cybersecurity, digital trust, and intelligent decision-making.
GWU alumni · Industry executives · Researchers · Educators · Thought leaders
CEO and President
AI strategy, cybersecurity, governance, executive advisory, business automation, and applied AI productization.
Chief Scientist
Machine learning, AI research, advanced modeling, scientific validation, and intelligent system architecture.
Chief AI Advisor
AI advisory, technical strategy, cybersecurity analytics, intelligent systems, and applied research translation.
Insights
Applied AI, cybersecurity, and compliance — written plainly.
CMMCHERO · READINESS QUICK-CHECK
Ten questions drawn from the gaps that fail real CMMC Level 2 assessments. Answer honestly — your score and top exposure areas appear instantly. Nothing is stored or transmitted.
YOUR READINESS SIGNAL
INDICATIVE ONLY — NOT AN ASSESSMENT. SCORING RUNS ENTIRELY IN YOUR BROWSER. FULL READINESS REVIEWS MAP EVIDENCE TO EVERY NIST SP 800-171A OBJECTIVE.
Frequently Asked
It depends on your starting evidence maturity, but most organizations move from initial gap review to assessment-ready evidence in 8–16 weeks. Environments with scattered or missing evidence — especially around encryption validation, access reviews, and audit logging — run longer.
Final-form, dated artifacts an assessor can independently verify — screenshots of live configurations, exported logs, signed policies, review records — not narrative claims. A control described as "in place" without a corresponding artifact will not pass.
RFPfort is built around the same data-handling discipline as CMMCHero: CUI stays inside your controlled boundary, access is scoped and logged, and outputs are reviewed before anything leaves the environment — no proposal content is used to train external models.
Alongside. SignalVer adds multi-modal verification — voice, video, text, and identity signals — as an additional check ahead of high-risk actions like wire transfers or executive requests. It's designed to plug into an existing fraud and KYC stack, not replace it.
No. The Quick-Check is a 2-minute, browser-only self-screen against ten common failure points — it gives you a directional signal, not a certifiable result. A full readiness review maps evidence to every NIST SP 800-171A assessment objective.
We prepare you. Our engagements build and organize your evidence package so you walk into a C3PAO assessment ready — we don't perform the certifying assessment itself, since that requires an accredited third party.
Client Next Step
Engineer trust into your AI systems. We will build the path from complexity to clarity — from promising capability to trustworthy execution.
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