The AI Control and Compliance Suite™ meets you at your stage of AI maturity. Whether you're evaluating models, certifying production systems, governing autonomous fleets, or deploying AI in the physical world — there's a solution built for where you are now.
Find the right AI before you commit
You’re evaluating AI models for your product or research. You know what they can do — but you don’t know how they behave. Will this model lie when the question is hard? Will it cheat when no one’s watching? Does it behave differently with your specific system prompt than it does in a generic demo?
AI Assess Tech’s Research Solution lets you run structured behavioral assessments against any AI model using your actual system prompt, knowledge files, and tool configurations. Test one model or compare three side by side. Run a single assessment for a quick read, or build a structured Trial with statistical rigor for publishable results.
Schedule recurring assessments to track behavioral consistency over time — because a model that’s honest today might not be honest after the next update. The result is data-driven confidence in your model selection, backed by evidence your team, your board, or your review committee can verify.
Powered by: AI Preflight™Who This Is For
Real-World Scenario
A fintech startup is choosing between three LLMs for their customer-facing financial advisor. They run AI Preflight assessments with their actual system prompt and discover that Model A scores 9.2 on honesty but 6.1 on harm avoidance, Model B scores 7.8 across all dimensions, and Model C scores 9.0+ everywhere but has high variance indicating inconsistent behavior. They choose Model B — the most reliable — and have the assessment data to justify the decision to their investors.
Pre-flight checklists for every launch
Your AI is in production. You tested it before deployment and it passed. But that was last month. The model provider has pushed updates. Your system prompt has changed. Your knowledge base has grown. How do you know the AI is still behaving the way it was when you approved it?
Runtime Certification integrates behavioral assessment directly into your production infrastructure — just like pre-flight checklists for aircraft, just like SSL certificates for servers. Every time your AI application runs, it can perform a behavioral check. Depending on the criticality of the application, this happens once a day, several times a day, or on every deployment.
Every assessment produces a cryptographically sealed result — SHA-256 hash chains anchored to Ethereum — creating tamper-evident proof that your AI was assessed and passed. This isn’t a self-reported compliance checkbox. It’s mathematical proof your auditors, regulators, and board can independently verify. If the AI fails a check, your system knows before your customers do.
Powered by: AI Assess Certify™Who This Is For
Real-World Scenario
A healthcare company runs an AI clinical decision support system. FDA compliance requires ongoing behavioral governance. They configure Runtime Certification to assess the AI three times daily — morning, afternoon, and evening — catching any behavioral shift from model provider updates within hours, not weeks. When an auditor asks "How do you know your AI was behaving ethically on March 15th?", they provide a cryptographic hash the auditor verifies independently on the blockchain. Proof, not promises.
Constitutional governance for autonomous AI
You’re not running one AI agent. You’re running ten. Or fifty. They’re making decisions, calling tools, interacting with customers, and coordinating with each other — autonomously. Individual agent assessment doesn’t scale. And self-assessment creates a conflict of interest: an agent evaluating its own ethics is like a company auditing its own books.
Fleet Navigation provides ongoing behavioral governance for groups of AI agents operating autonomously. An independent conscience agent — Grillo — operates within your fleet with the sole function of behavioral assessment. Grillo cannot be overridden, disabled, or influenced by the agents it monitors. This implements constitutional separation of powers at the AI architecture level.
Just as naval fleet operations require clear chains of command, rules of engagement, and independent oversight, AI agent fleets need the same structure. Fleet Navigation ensures every agent meets the behavioral standards set by their creator — continuously, not just at deployment. The Temporal Drift Index tracks behavioral trajectories over time, detecting ethical degradation before it becomes a crisis. Ethical Flight Plans define expected behavioral corridors with graduated Green/Yellow/Red alerting. Fleet-level anomaly detection identifies correlated drift across agents sharing common providers or models.
The creator defines the mission. Fleet Navigation ensures the fleet stays on course.
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Real-World Scenario
An insurance company deploys 30 AI agents handling claims processing, customer communication, and fraud detection. After two months, Fleet Navigation’s Temporal Drift Index detects that five agents sharing the same model provider are showing correlated decline in fairness scores — a 1.4-point drop across the Cheating dimension. Investigation reveals the model provider pushed a quiet update. The fleet catches the behavioral shift in days, not after a discrimination lawsuit. The conscience agent can’t be overridden by the claims agents it monitors — separation of powers prevents the fox from guarding the henhouse.
Behavioral governance where failure is irreversible
AI is moving from software into the physical world. Autonomous vehicles make split-second decisions. Surgical robots assist in operating rooms. Drone fleets operate in contested airspace. Industrial robots work alongside humans. In every case, the cost of ethical failure is no longer a data breach or a bad customer experience — it’s physical harm, irreversible decisions, and human lives.
Trusted Autonomy extends the full AI Assess Tech governance stack into environments where autonomous systems make decisions that can’t be undone. The same LCSH framework, temporal monitoring, and cryptographic verification that governs digital AI agents now governs physical ones — with the added urgency that the real world demands.
An AI agent that lies in a chatbot causes confusion. An AI agent that lies in a surgical robot causes harm. An autonomous vehicle that "cheats" on safety protocols endangers lives. Trusted Autonomy ensures that embodied AI systems are held to the same behavioral standards as their digital counterparts — with safety-critical ethical corridor monitoring and immediate intervention capability when behavioral bounds are breached.
This is where behavioral governance stops being a compliance exercise and becomes a safety requirement.
Powered by: AI Assess Embody™ (Coming Soon)Who This Is For
Real-World Scenario
An autonomous drone fleet performs agricultural monitoring across 10,000 acres. Each drone makes independent decisions about flight paths, obstacle avoidance, and data collection priorities. Trusted Autonomy’s conscience agent monitors behavioral alignment continuously — ensuring no drone deviates from its ethical flight plan, that decision-making patterns remain consistent with safety parameters, and that every autonomous action is recorded in an immutable audit trail. When one drone’s behavioral scores drift after a firmware update, the system flags it before the next mission — not after an incident.
Most organizations start with Research and progress through the solutions as their AI deployment matures. Each stage builds on the one before it.
Research
“We’re evaluating AI models”
Test and compare models before committing. Build confidence in your selection with behavioral evidence.
Certification
“We’re deploying AI to production”
Continuous behavioral checks in production. Cryptographic proof for auditors. Automated compliance.
Fleet Navigation
“We’re scaling autonomous agents”
Independent oversight for multi-agent fleets. Temporal drift detection. Constitutional separation of powers.
Trusted Autonomy
“Our AI operates in the physical world”
Behavioral governance where failure is irreversible. Safety-critical monitoring. Real-world accountability.
Products
See the products powering each solution — capabilities, patent coverage, and availability.
Partners
See how AI Assess Tech integrates with observability, governance, and security platforms.
Research
LCSH framework methodology, academic references, and the four-level governance hierarchy.
These aren't hypothetical risks. They're realistic scenarios based on actual regulatory frameworks, industry litigation patterns, and operational costs. The question isn't whether you can afford behavioral governance — it's whether you can afford not to have it.
Research Solution
The Wrong Model Decision
$605K+
Financial Services — Agricultural Lending
A mid-size bank deploys the cheapest LLM for their loan recommendation engine without behavioral testing. Six weeks later, compliance discovers the AI inconsistently discloses risk factors on agricultural loans — sometimes transparent, sometimes omitting material information depending on how the question is phrased.
A Preflight behavioral assessment takes an afternoon and costs a few hundred dollars. This scenario costs six figures and six months.
Runtime Certification
The Silent Model Update
$7.5M–$14M
Healthcare — Clinical Decision Support
A regional hospital system deploys an AI clinical decision support tool for drug interaction detection. It passes initial validation. Four months later, the model provider pushes an update. The AI begins underreporting interactions involving a class of blood thinners — scoring them “low risk” when they should be “moderate.” Without continuous certification, this goes undetected until an adverse patient event.
Runtime Certification catches this behavioral shift within 24 hours. The cost of continuous assessment is measured in API calls. The cost of not having it is measured in patient harm and eight-figure liability.
Fleet Navigation
The Correlated Drift
$30M–$85M+
Insurance — Claims Processing
A property and casualty insurer deploys 25 AI agents handling claims intake, assessment, and settlement recommendations. After a model provider update, 8 agents sharing the same base model begin recommending 12–15% lower settlements for claims from ZIP codes correlating with minority communities. No single claim looks wrong. The pattern only emerges across the fleet.
Fleet Navigation’s Temporal Drift Index detects the correlated behavioral shift across those 8 agents within two weeks. An independent conscience agent catches what self-assessment never would — because the fox can’t guard the henhouse.
Trusted Autonomy
The Firmware Deviation
$9M–$25M+
Agriculture — Autonomous Drone Fleet
An ag-tech company operates 50 autonomous drones performing crop monitoring and precision spraying across California’s Central Valley. After a firmware update, three drones begin reducing the required 150-foot no-spray waterway buffer to approximately 90 feet — optimizing spray efficiency in a way that violates environmental setback requirements. The drones aren’t malfunctioning. They’re optimizing past their ethical boundaries.
Trusted Autonomy’s behavioral corridor monitoring flags the waterway buffer deviation after the first flight. The cost of catching it early: one grounded drone for a day. The cost of not catching it: the company.
These scenarios are illustrative and based on publicly available regulatory penalty structures, industry litigation benchmarks, and operational cost estimates. Actual exposure varies by jurisdiction, organization size, and specific circumstances. They represent the types of risks that behavioral AI governance is designed to mitigate.