Your agentic code is in production.
Your audit trail is not.
CognitionHive helps regulated enterprises in banking, insurance, healthcare, and life sciences build the Composite Reliability Index (CRI) that puts AI-generated code under the same control regime as human-authored code.
- 8 dimensions assessed
- CRI Maturity Scorecard delivered
- Gap analysis mapped to your regulatory framework
- Executive briefing for CISO, CRO, regulatory liaison
- 60% assessment-to-implementation conversion rate
"Regulators are asking. Your controls were built for human code."
Agentic coding operates at sub-minute timescales, outside pull request workflows, and leaves no cryptographic trail documenting the reasoning behind changes.
Regulatory Exposure
OCC 2011-12, FDA 21 CFR Part 11, and SOC 2 CC7 controls were designed for human code. Examiners are now questioning agentic governance.
No Decision Trail
When agents modify payment calculations or auth paths, there is no captured context or reasoning documentation.
No Cryptographic Integrity
Human commits are signed. Agentic commits typically lack verification mechanisms entirely.
Policy Enforcement Gap
Sensitivity rules enforced at merge for humans. Agents bypass these gates entirely and leave no record.
Four ways to engage
All services are built on CRI methodology and the Prufs framework. No platform deployment required to begin.
We map to the frameworks your examiners use
Built for regulated environments
Banking
OCC, Federal Reserve, and FDIC model risk management frameworks.
Insurance
State regulatory filings and actuarial model governance.
Healthcare
HIPAA Security Rule and clinical decision support oversight.
Life Sciences
FDA 21 CFR Part 11, GMLP guidance, and electronic records compliance.
Energy & Utilities
NERC CIP and operational technology AI governance.
The researcher behind the methodology
Wade Lovell is principal of CognitionHive. His AI work began in a 1981 doctoral seminar in Econometrics, making him a founding-era practitioner in a field most people entered three decades later. Across that span he has served as CTO, Technical Architect, and AI Architect in banking, insurance, and enterprise software. His doctoral dissertation at Walsh College focuses on LLM agent reliability in regulated settings. He is a published author on Ethical AI in Education, the Workforce, and the C-Suite.
Products built on the same CRI foundation
Prufs.ai
Cryptographic audit trails for every AI-generated code commit. SHA-256 signed. Examiner ready.
Explore Prufs.ai →AgentPruf
Governance-as-a-Service for enterprise agent deployments. Five-agent pipeline with Human at the Helm escalation.
Explore AgentPruf →BrandSprint
A complete brand launch in 48 hours. Seven agents delivering naming, identity, messaging, and launch assets.
Explore BrandSprint →EntityForge
Agentic company formation from entity selection through operating agreement. Six agents, 14-step checklist.
Explore EntityForge →Nexum Fiducia
Institutional-grade AI portfolio management for private banks, family offices, RIAs, and trust departments.
Explore Nexum Fiducia →Nexum Prism
Institutional portfolio intelligence made accessible to self-directed investors. SHAP explainability on every recommendation.
Explore Nexum Prism →Selvara
One Navigator. Every destination. Research-grade AI education across eleven tiers, from Pre-K tracing to executive AI literacy.
Explore Selvara →Request a CRI Readiness Assessment
No commitment required. Response within one business day.
Every AI commit,
cryptographically signed.
Prufs.ai wraps every AI-generated code change in a SHA-256 signed audit trail that captures agent identity, reasoning context, and policy decisions, making agentic code as auditable as human code.
Governance at the commit level
Agent Identity Signing
Every agent action is signed with a unique key, establishing a verified chain of custody from instruction to deployed code.
Reasoning Capture
The context behind each change (the prompt, the policy constraints, the decision path) is hashed and stored alongside the commit.
Policy Enforcement Record
Each Prufs record documents which OPA policies were evaluated and what outcome they produced before the change was accepted.
Examiner-Ready Artifacts
Audit packages formatted to OCC, FDA, SOC 2, and HIPAA evidence expectations are generated automatically.
SDK-First Deployment
The Prufs SDK wraps your existing agent toolchain. No infrastructure replacement required. 212 tests confirm production stability.
Cloud Backend
Records stored in Prufs-managed cloud infrastructure with customer-controlled retention, export, and access policies.
Governance-as-a-Service for
enterprise agent deployments.
AgentPruf operationalizes the CRI framework as a managed service: five governance agents that wrap your agentic workflows with identity verification, policy enforcement, Human at the Helm escalation, and SHA-256 audit trails.
Every agent action governed end-to-end
Identity Agent
Establishes and verifies agent identity using signing keys before any action executes in your environment.
Policy Agent
Evaluates each proposed action against your OPA policy bundle before approval is granted.
Audit Agent
Records every decision, input, and output with a Prufs.ai-backed cryptographic chain of custody.
Escalation Agent
Routes Human at the Helm decisions for high-stakes or policy-boundary actions before they execute.
Compliance Agent
Packages evidence artifacts in examiner-ready format aligned to your specific regulatory framework.
Three tiers. No per-seat pricing.
A complete brand launch
in 48 hours.
BrandSprint deploys a seven-agent pipeline to deliver naming, visual identity, messaging, competitive positioning, and launch-ready assets in two days, work that previously took agencies six weeks and cost six figures.
A full brand team working in parallel
Discovery Agent
Interviews the founder, extracts positioning inputs, and maps the competitive landscape before any creative work begins.
Naming Agent
Generates and scores brand name candidates against trademark risk, memorability, and domain availability.
Identity Agent
Produces color palette, typography system, and logo direction derived from the positioning brief.
Messaging Agent
Writes tagline, value proposition, homepage hero copy, and elevator pitch in three audience-specific variants.
Competitive Agent
Maps the category landscape and identifies the differentiation angles your brand can credibly own.
Asset Agent
Packages everything into a launch-ready brand kit: style guide, copy deck, and social media templates.
Audit Agent
Prufs.ai verifies every agent output with a signed audit trail: CRI governance on every deliverable, not just code.
Three tiers. Delivered in 48 hours.
From idea to operating company
in hours, not weeks.
EntityForge runs a six-agent company formation pipeline that takes a founder from blank page to filed entity, registered agent, operating agreement, and compliance calendar: the full 14-step checklist in a single session.
14-step formation checklist. Six agents. One session.
Entity Selection
C-Corp, LLC, or S-Corp analysis based on your business model, investor requirements, and tax position.
State Filing
Articles of incorporation or organization prepared and submitted to your chosen jurisdiction.
Operating Documents
Operating agreement or bylaws drafted to your entity type, with standard governance provisions included.
Registered Agent
Registered agent designation handled for the formation state, with annual compliance reminders built in.
Compliance Calendar
Annual report dates, tax filing deadlines, and state renewal obligations mapped into a calendar you own.
CRI Audit Trail
Every formation document and decision step is recorded with a Prufs.ai-backed audit chain.
Entity formation at a fraction of attorney rates.
Institutional-grade AI portfolio management
for banks and family offices.
Nexum Fiducia integrates three AI paradigms (machine learning portfolio optimization, generative AI client communication, and agentic RAG research) into a single fiduciary-grade workflow for private banks, family offices, RIAs, and ERISA-governed accounts.
Three AI paradigms. One fiduciary workflow.
ML Portfolio Optimizer
Constrained optimization across asset classes with configurable risk tolerance, investment policy statement rules, and mandate compliance baked in.
GenAI Client Communication
Investment policy statements, regulatory disclosures, and client-facing reports generated in plain language with full methodology transparency.
Agentic RAG Research
Continuously updated research synthesis across market data, analyst reports, and regulatory filings, surfaced at the moment of portfolio decision.
SHAP Explainability
Every portfolio recommendation includes a SHAP-based explanation chain that satisfies fiduciary documentation requirements.
Compliance Documentation
Automated generation of IPS drafts, Form ADV disclosures, and ERISA documentation aligned to the institutional mandate.
CRI Governance
Every agent recommendation is scored across the four CRI dimensions and logged with a Prufs.ai-backed cryptographic audit trail.
Built for the fiduciary standard
Private Banks
Portfolio intelligence for high-net-worth and ultra-high-net-worth client mandates.
Family Offices
Single and multi-family office investment operations with institutional-grade governance.
Registered Investment Advisers
Scale advisory capacity without proportional headcount growth.
Trust Departments
ERISA and fiduciary-governed trust operations with compliance documentation requirements.
Institutional licensing. Pilot engagements available.
Institutional portfolio intelligence
for self-directed investors.
Nexum Prism brings the same Nexum engine powering institutional wealth managers to individual investors: SHAP-powered explainability that makes quantitative portfolio decisions visible, not opaque.
You see the reasoning, not just the recommendation.
SHAP Explainability
Every recommendation arrives with a plain-language explanation of which factors drove the decision and how much weight each carried.
ML Optimization
The same constrained optimization engine from Nexum Fiducia, configured for retail account types and individual risk profiles.
Agentic Research
Continuously updated market intelligence synthesized from public filings, analyst reports, and macro data, delivered at portfolio decision time.
Constraint-Aware Optimization
Your stated constraints (tax situation, sector exclusions, liquidity needs) are enforced at the optimization layer, not just displayed.
No Black Box
Every recommendation includes the full reasoning chain so you can evaluate, question, and override with confidence.
CRI-Scored Outputs
Each recommendation is scored across Correctness, Faithfulness, Stability, and Constraint Compliance before delivery.
Institutional intelligence at individual pricing.
Nexum Prism is in preview. Join the waitlist at nexumprism.ai. General availability follows Nexum Fiducia institutional validation.
Research-grade AI education,
from Pre-K to the C-suite.
Selvara is CognitionHive's consumer and professional education platform. One Navigator, an AI learning companion that ages with the learner, across eleven tiers from first letters to executive AI literacy. Built on the Composite Reliability Index so every answer is measured, not guessed.
Six commitments that separate Selvara from every other learning app.
Age-Adaptive Navigator
One persistent AI companion whose voice, vocabulary, and relationship grow with the learner from Pre-K through adulthood.
Anti-Gaming Architecture
Voice verification, gaze detection, timing checks, in-app reward economy, and session caps prevent breakout and reward-shortcutting.
Portable Memory
Every learner owns an open-schema memory file they can carry across devices, grades, and a lifetime. No data lock-in.
CRI-Scored Outputs
Correctness, Faithfulness, Stability, Constraint Compliance. The Navigator's answers are measured against Wade Lovell's doctoral research framework.
Human at the Helm
Parents, teachers, and administrators see every escalation, every edge case, every moment the Navigator defers to a human.
Research-Backed
Published author on Ethical AI in Education. Thirty-plus years of enterprise AI experience. Doctoral candidate in ML/AI.
Eleven tiers. One Navigator. Every destination.
Selvara Start
Pre-K. First letters, tactile tracing, voice-first interaction.
Selvara Kids
K-2. Reading fluency, early math, guided discovery.
Selvara Explorer
Grades 3-4. Writing, fractions, structured curiosity.
Selvara Builder
Grades 5-6. Pre-algebra, research skills, project work.
Selvara Middle
Grades 7-8. Algebra, composition, study habits.
Selvara High
Grades 9-12. SAT/ACT, AP prep, college-ready writing.
Selvara College
Undergraduate study partner across every major.
Selvara Graduate
Graduate research, literature synthesis, thesis support.
Selvara Professional
Career learning, certifications, continuing education.
Selvara Leader
Manager-level AI literacy and applied decision support.
Selvara Executive
C-suite AI literacy, board governance, strategic judgment.
Selvara Executive for boards, districts, and the C-suite.
Beyond consumer subscriptions, Selvara licenses structured AI literacy programs to school districts, university systems, and enterprise leadership teams. The Executive tier draws directly from Wade Lovell's published Ethical AI book series and CognitionHive consulting engagements.
A CognitionHive Product. Selvara is built on the Composite Reliability Index, the measurement framework from Wade Lovell's doctoral research on agent reliability. Selvara pending USPTO registration, Classes 9 and 41.
Four decades of AI,
from econometrics to agents.
Wade Lovell is principal of CognitionHive. His AI journey began in a 1981 doctoral seminar in Econometrics at the College of William & Mary, three decades before most practitioners entered the field. Today he builds the measurement frameworks that make agentic AI auditable in regulated industries.
Across a career spanning CTO, Technical Architect, and AI Architect roles in banking, insurance, and enterprise software, Wade has led multi-million dollar technology transformations for Fortune 500 clients. His consulting practice at Salesforce placed him at the intersection of enterprise architecture, data strategy, and the rapid emergence of agentic AI.
His doctoral dissertation at Walsh College develops the Composite Reliability Index (CRI), a measurement framework for hallucination detection, uncertainty quantification, and operational trustworthiness of LLM agents in regulated settings. Every product in the CognitionHive portfolio operationalizes this research.
Wade is a published author on Ethical AI in Education, the Workforce, and the C-Suite. His two published books address AI literacy in K-6 classrooms and the research case for early AI education. A third book, AI or Extinction: The C-Suite Reckoning, examines executive-level AI adoption in regulated industries.
Academic foundation
PhD Candidate, Walsh College
Doctoral research on LLM agent reliability in regulated environments. Developing the Composite Reliability Index (CRI) framework.
MS in Artificial Intelligence, Udacity/Woolf University
Graduate program in machine learning, deep learning, natural language processing, and computer vision.
MBA, Columbia Business School
Strategy, finance, and organizational leadership.
BBA, College of William & Mary
Foundation in economics and quantitative methods. First AI exposure in a 1981 doctoral seminar in Econometrics.
Professional certifications and designations
Enterprise leadership across four decades
What colleagues and clients say.
LinkedIn recommendations from leaders across Salesforce, Accenture, and the broader enterprise technology community.
Books, research, and
thought leadership.
Published works on ethical AI adoption across education, the workforce, and executive leadership.
Books & Research
Ethical AI in K-6 Education: Everyday Classroom Practice for Agency, Confidence, and Equity
A practical framework for educators integrating AI into K-6 classrooms. Addresses how schools can implement AI systems that promote student agency, build confidence in AI literacy, and maintain equity across diverse learning environments.
The Ethical AI Argument: Why Educators Must Rethink AI in K-6
The research-driven companion to the classroom practice book. Presents the evidence base for why early AI education matters and why the current approach of restricting student access to AI tools creates more risk than it mitigates.
AI or Extinction: The C-Suite Reckoning
A direct examination of executive-level AI adoption: the organizational, strategic, and governance decisions that determine whether AI investments create value or compound risk. Written for boards, CISOs, CROs, and technology leaders in regulated industries.
Doctoral Dissertation: Reliability Frameworks for LLM-Based Agents in Enterprise Environments
Walsh College. Research focuses on developing the Composite Reliability Index (CRI), a measurement framework for hallucination detection, uncertainty quantification, and operational trustworthiness of LLM agents in regulated settings. This research directly informs every product in the CognitionHive portfolio.
Perspectives on AI Governance & Strategy
Why Your Agentic Code Needs an Audit Trail Before Your Examiner Asks for One
Agentic coding tools are writing, testing, and deploying code at sub-minute timescales. The productivity gains are real. The governance gap is equally real. When a human developer writes code, the pull request workflow captures intent, review, and approval. When an agent writes code, that trail disappears. The commit lands, the tests pass, and the reasoning that produced the change is gone. For teams in regulated industries, this is a compliance exposure that grows with every sprint. OCC 2011-12, FDA 21 CFR Part 11, and SOC 2 CC7 controls were designed for a world where humans wrote code and other humans reviewed it. Examiners are beginning to ask whether that model still holds when agents produce a material percentage of production changes. The answer, in most organizations, is that it does not. The Composite Reliability Index addresses this gap by establishing four measurable dimensions (Correctness, Faithfulness, Stability, and Constraint Compliance) that apply equally to human and agentic code. Each dimension produces a score that can be tracked over time, compared across agents, and presented to examiners alongside the traditional controls they already understand.
The Fiduciary Standard and AI: What Nexum Fiducia Taught Us About Explainability
When a portfolio manager recommends a rebalancing trade, the client has every right to ask why. When an AI system makes the same recommendation, the same right applies, but the answer is harder to produce. Black-box optimization is powerful. It is also insufficient for fiduciary-governed accounts where the investment policy statement creates legal obligations around transparency, suitability, and documentation. Building Nexum Fiducia forced us to solve this problem at the architecture level. Every recommendation passes through a SHAP explainability layer that decomposes the decision into factors the portfolio manager can review, the compliance team can audit, and the client can understand. The CRI framework scores each recommendation across four dimensions before it reaches anyone. This is the difference between an AI that produces good answers and an AI that produces auditable answers. In wealth management, auditable is the only kind that matters.
Teaching AI to Children: Why Restriction Creates More Risk Than Education
The dominant approach to AI in K-6 education is restriction. Block the tools. Filter the access. Pretend the technology does not exist in the homes, phones, and conversations that children navigate every day. This approach fails for the same reason that abstinence-only approaches fail in every domain: it confuses restriction with preparation. My research, published across two books on Ethical AI in Education, makes the case for structured AI literacy that begins in the earliest grades. Children who learn how AI systems work, where they fail, and what questions to ask develop a critical framework that serves them for decades. Children who encounter AI for the first time as teenagers or adults, with no foundational understanding of how these systems produce outputs, are more vulnerable to manipulation, misinformation, and over-reliance. Selvara, the educational platform in the CognitionHive portfolio, operationalizes this research across eleven tiers. The Navigator adapts to the learner's age, language level, and cognitive development. Anti-gaming architecture prevents the reward-hacking behavior that undermines most educational AI tools. Every answer is CRI-scored for accuracy and reliability before it reaches the student.