The Liability Firewall: Engineering Deterministic Action Layers for the Post-Moffatt Enterprise

Beyond the Wrapper: Transitioning from Probabilistic Chatbots to Legally Binding Digital Agents

Executive Summary

The widespread deployment of Large Language Models (LLMs) in enterprise environments has precipitated a fundamental crisis of corporate accountability. While generative AI offers unprecedented capabilities in natural language fluency and customer engagement, its fundamental architecture—probabilistic next-token prediction—creates critical vulnerabilities when applied to transactional and legally binding workflows. The watershed ruling in Moffatt v. Air Canada (2024) has permanently altered the compliance landscape, establishing that an AI chatbot is not merely a software feature but a "legally binding employee" for whose representations the corporation is strictly liable. ¹

This whitepaper, prepared by Veriprajna Strategy Group, delineates the structural flaws inherent in "wrapper" architectures that rely solely on probabilistic generation for customer service. We argue that the industry must pivot toward Deterministic Action Layers —a hybrid neuro-symbolic architectural approach that strictly separates creative engagement from policy execution. By implementing rigid, logic-based guardrails and semantic routing, enterprises can harness the fluency of LLMs without exposing themselves to the financial and reputational risks of hallucinated contracts. This document serves as a definitive technical and legal guide, outlining the judicial precedents, the specific technical failures of pure RAG (Retrieval-Augmented Generation) in high-stakes environments, and the engineering blueprints required to build ISO 42001-compliant, enterprise-grade AI agents that do not write checks your business cannot cash.

1. The Moffatt Paradigm: The End of the "Black Box" Defense

1.1 The Case That Redefined Digital Agency

In February 2024, the Civil Resolution Tribunal of British Columbia issued a ruling that sent shockwaves through the global legal and technology sectors, effectively rewriting the risk profile for every corporation utilizing consumer-facing artificial intelligence. In Moffatt v. Air Canada, a grieving passenger engaged the airline’s AI chatbot to inquire about bereavement fares following the death of his grandmother. The chatbot, hallucinating a policy that did not exist in reality but sounded linguistically plausible, instructed the passenger to purchase a full-price ticket immediately and claim a refund within 90 days. The airline's actual policy, buried in the governing tariff and static webpages, strictly prohibited retroactive refunds once travel had commenced. ¹

When Air Canada subsequently refused the refund request, citing the correct policy available on their static website, Moffatt initiated legal action. Air Canada’s defense strategy was both novel and legally audacious: the corporation argued that the chatbot should be treated as a "separate legal entity" responsible for its own actions. The airline contended that because the correct information appeared elsewhere on its digital estate, it should not be held liable for the "misleading words" of its automated agent. ¹ This defense essentially attempted to anthropomorphize the AI just enough to assign it blame, while simultaneously arguing that the corporation's duty of care was fulfilled by the existence of static text.

1.2 The Tribunal’s Rejection of Algorithmic Autonomy

The Tribunal categorically and somewhat derisively rejected Air Canada’s defense. Tribunal Member Christopher Rivers ruled that "Air Canada could not separate itself from the AI chatbot," noting that for the consumer, there is no meaningful distinction between a human agent, a static webpage, and an interactive bot. ¹ The ruling established three critical precedents that now form the bedrock of enterprise AI liability:

First, the concept of Unified Liability was solidified. The Tribunal found that a corporation is responsible for all information presented on its website, regardless of the medium. Whether the information is rendered via static HTML text or dynamically generated by a Large Language Model's neural network is legally irrelevant; both are representations made by the company to the consumer. ¹

Second, the ruling reinforced the Duty of Care . Corporations owe a specific duty of care to ensure their representations are accurate and not misleading. In the context of AI, "hallucinations"—technically described as confabulations resulting from probabilistic token generation—are legally classified as negligent misrepresentation . The Tribunal found that Air Canada failed to take reasonable care to ensure its chatbot was accurate, effectively ruling that deploying an unverified probabilistic model for policy dissemination is an act of negligence. ⁵

Third, and perhaps most importantly for CTOs and CIOs, the "Black Box" Defense is Irrelevant . The internal complexity, opacity, or probabilistic nature of the AI system offers no shield against liability. The fact that the chatbot "wrote its own policy" due to next-token prediction mechanics does not absolve the company. If the bot promises a discount, the company must honor it, effectively granting the AI the power to rewrite corporate contracts in real-time. ⁷

1.3 The "Digital Employee" Classification

This ruling effectively classifies customer-facing AI not as a software tool (like a search bar), but as a digital employee with "apparent authority." Under the legal doctrine of agency law, if a third party (the customer) reasonably believes an agent (the AI) has the authority to act on the principal’s (the company's) behalf, the principal is bound by the agent's agreements. ⁸

This classification creates a massive "Trust Gap" for enterprises relying on simple LLM wrappers. ¹⁰ A wrapper that simply pipes a user prompt to a foundation model like OpenAI’s GPT-4 or Anthropic’s Claude and returns the result is a liability engine. It is akin to hiring a highly eloquent but fundamentally untrained employee, giving them a copy of the company handbook, and allowing them to negotiate contracts without supervision. If that wrapper hallucinates a price, a policy, or a refund term, the company is on the hook. The Moffatt case demonstrated that "creativity"—the very feature that makes GenAI impressive and marketable—is fatal for compliance. ¹¹

The implications extend far beyond the travel industry. Any chatbot that discusses pricing, service terms, warranties, or data privacy is now a potential vector for binding contract variations. As noted in legal analyses following the decision, Air Canada’s attempt to argue the bot was a separate entity was a "remarkable submission" that, if accepted, would have fundamentally undermined consumer protection laws by allowing companies to outsource liability to software ghosts. ² The Tribunal's rejection ensures that the corporate veil cannot be pierced by algorithmic autonomy; the principal remains liable for the agent, silicon or otherwise.

1.4 The Cost of Negligence

The financial implications of this shift are quantifiable. Recent comprehensive studies reveal that global losses attributed to AI hallucinations reached $67.4 billion in 2024 alone. ¹² This figure encompasses not just direct compensation (like the refund Air Canada was forced to pay), but regulatory fines, legal fees, brand damage, and the operational costs of manual verification. Forrester Research estimates that each enterprise employee costs companies approximately $14,200 per year in hallucination mitigation efforts—time spent verifying AI outputs that cannot be trusted. ¹²

In the Moffatt case, the damages awarded were relatively small—around $800—but the precedent is priceless. It signals to class-action lawyers and regulators worldwide that AI output is actionable. A single hallucination regarding a financial product or a healthcare directive could lead to liabilities in the millions. The 318% growth in the market for hallucination detection tools between 2023 and 2025 underscores the industry's desperate pivot toward reliability. ¹²

2. The Architecture of Failure: Why Probabilistic Models Cannot Manage Compliance

To understand why the Moffatt incident occurred, and why it is destined to recur in organizations that rely on superficial AI implementations, one must analyze the fundamental limitations of current Generative AI architectures when applied to transactional contexts.

2.1 The Stochastic Parrot Problem in Enterprise

Large Language Models are, at their core, probabilistic engines. They predict the next likely token (word or character) based on statistical correlations found in their vast training datasets. They do not "know" facts in the epistemological sense; they understand semantic proximity. When an LLM asserts that "refunds are available within 90 days," it is not querying a database of rules; it is completing a sentence pattern that is statistically probable based on the millions of documents it has ingested, which likely included many different refund policies from various airlines. ¹³

●​ Probabilistic AI: Models uncertainty and provides outcomes based on likelihoods. It excels at creative writing, summarization, translation, and code generation where multiple correct answers exist.

●​ Deterministic Systems: Operate based on rigid logical rules (If X, then Y). They are required for mathematics, legal logic, financial transactions, and policy enforcement where only one correct answer exists. ¹³

In the Air Canada case, the chatbot likely acted "helpfully" in the linguistic sense. It recognized the user's distress and the context of bereavement. It accessed its training weights which associate "bereavement" with "special considerations" and "refunds." It then constructed a sentence that sounded plausible and authoritative. It was fluent, but it was not factually anchored to the rigid logic of the specific tariff applicable to that specific ticket class. ³ This is the "Stochastic Parrot" problem: the ability to speak convincingly without any comprehension of the binding nature of the speech.

2.2 The Hallucination Epidemic

The "hallucination" phenomenon—where models confidently state falsehoods—is not a bug that can be simply patched; it is a feature of the probabilistic architecture. Even the most advanced frontier models, such as Google’s Gemini 2.0 or OpenAI's GPT-4o, retain a baseline hallucination rate. This rate can range from 0.7% for highly optimized models to over 25% for less sophisticated ones, depending on the complexity of the task. ¹²

In a creative context, a 1% error rate is a quirk. In an enterprise transactional context, it is a disaster. If a global bank’s AI assistant handles 1 million customer queries a month, a 0.7% hallucination rate results in 7,000 potential regulatory violations, incorrect financial advisories, or promised refunds that do not exist. As noted in risk management reports, models are often "confident but wrong," stating fabrications with the same authoritative tone as facts, making detection by end-users nearly impossible. ¹¹

The risk is amplified by the "knowledge cutoff" and the static nature of model weights. Models trained in 2023 have no knowledge of 2024 policy updates unless explicitly provided with new context. Even then, the "weight" of the training data can sometimes overpower the new context, leading the model to revert to its training biases—a phenomenon known as "hallucination due to training data gaps" or "parametric memory dominance". ¹²

2.3 The Failure of Naive RAG (Retrieval-Augmented Generation)

Many technology consultancies and "wrapper" agencies offer Retrieval-Augmented Generation (RAG) as the silver bullet for hallucinations. In a standard RAG setup, the system retrieves relevant documents (like the Air Canada refund policy) from a vector database and feeds them into the LLM's context window along with the user's query. The theory is that the LLM will "read" the policy and answer correctly.

However, the Moffatt case exposes the flaw in Naive RAG. The chatbot did provide a link to the correct policy, indicating that it likely had access to the correct document. ⁴ Yet, it still summarized the policy incorrectly. This failure mode reveals that providing the correct information is not enough; the reasoning engine itself must be reliable. If the retrieved text is complex, contradictory, or exceeds the model's reasoning "budget," the LLM may ignore the retrieved context in favor of its pre-trained biases or simply misinterpret the legal syntax. ¹⁶

Furthermore, RAG systems based on vector similarity search can fail when semantic similarity does not equal logical relevance. A query about "refunds" might retrieve a document about "refund processing times" rather than "refund eligibility criteria," leading the LLM to construct an answer from irrelevant facts. As noted in analyses of hybrid search limitations, vector search often struggles with exact SKU codes or specific legal clauses that require keyword precision rather than semantic fuzziness. ¹⁷

Key Insight: RAG provides knowledge, but it does not guarantee adherence . You cannot solve a strict logic problem with a probability engine alone. The "reasoning" capability of an LLM is probabilistic simulation of reasoning, not the execution of formal logic.

3. The Veriprajna Solution: Deterministic Action Layers

Veriprajna advocates for a fundamental shift in Enterprise AI architecture. We reject the notion that a single "General Purpose" model should handle both conversation and compliance. Instead, we champion Neuro-Symbolic Architectures utilizing Deterministic Action Layers (DAL) . This approach acknowledges that while the interface of a chatbot should be conversational (Neural), the decision-making regarding policies, money, and contracts must be hard-coded and rule-based (Symbolic). ¹⁰

3.1 Defining the Deterministic Action Layer

A Deterministic Action Layer (DAL) is a middleware component that sits architecturally between the user interface and the Large Language Model. It acts as a sophisticated "switching station" or traffic controller. Its primary function is to detect high-stakes intents—such as "Refunds," "Pricing," "Legal Terms," "Data Privacy," or "Warranty Claims"—and strictly prohibit the LLM from generating an answer based on its internal weights. ¹⁹

Instead of allowing the LLM to generate text, the DAL triggers a Symbolic Logic Module . This module executes a pre-written script, a database query, or a decision tree that returns a fixed, legally vetted response. The LLM’s role is reduced to mere delivery or formatting, or in high-risk cases, it is bypassed entirely in favor of a pre-approved template. ¹⁰

Table 1: Probabilistic vs. Deterministic Handling of Sensitive Intents

Feature	Probabilistic (Standard LLM Wrapper)	Veriprajna Deterministic Action Layer
User Query	"Can I get a refund for my grandmother's funeral fight?"	"Can I get a refund for my grandmother's funeral fight?"
Mechanism	Next-token prediction based on training data + context.	Semantic Router identifes intent: bereavement_refund.
Processing	Generates a plausible-sounding policy based on linguistic paterns found in training data.	Executes hard-coded logic: if ticket_status == 'fown' return NO_REFUND.
Output	"Sure, just submit the form within 90 days." (Hallucination)	"Our policy strictly prohibits refunds afer travel. Reference: Tarif Rule 45."
Liability	High (Negligent	Low (Strict adherence to

Col1	Misrepresentation).	codifei d policy).
Auditability	Low (Black box neural network opacity).	High (Traceable logic path and code execution logs).

3.2 Neuro-Symbolic AI: The Hybrid Future

This architecture represents the practical application of Neuro-Symbolic AI, a cutting-edge field that combines the adaptive pattern recognition of neural networks with the precise, rule-based reasoning of symbolic logic systems. ¹⁸

●​ The Neural System (System 1): Handles intent classification, entity extraction, sentiment analysis, and conversational chit-chat. It is fast, intuitive, and handles the variability of human language. It understands that "I want my money back" and "requesting a reimbursement" mean the same thing. ²¹

●​ The Symbolic System (System 2): Handles the "reasoning," "policy enforcement," and "transactional execution." It uses knowledge graphs, ontologies, and logical constraints to ensure that the output is mathematically and legally correct. It enforces the rule that "Refund Amount cannot exceed Ticket Price" regardless of what the user asks. ¹⁰

This hybrid approach mirrors human cognition, where rapid intuitive pattern matching (System 1) is checked by slow, deliberate logical reasoning (System 2). In standard LLM wrappers, System 2 is missing, leaving the "stochastic parrot" to make legal decisions. ²¹

3.3 The "Silence Protocol"

A core tenet of the Veriprajna architecture is the Silence Protocol . When a user asks about a topic defined as "Compliance Critical" (e.g., bereavement fares), the Generative AI's creative writing capabilities are effectively silenced. The system switches modes from "Author" to "Reader." It retrieves the exact text from the database and serves it verbatim, or fills a strict template with variables retrieved from a trusted database. We disable "creativity" for compliance topics because in legal terms, creativity regarding contract terms is synonymous with fabrication. ¹⁰

This protocol ensures that the AI never "improvises" a policy. If the user asks a question that falls into a policy gap where no deterministic rule exists, the Silence Protocol triggers a fallback mechanism: "I cannot answer that question directly. Let me connect you with a human specialist who can assist." This "fail-safe" design prevents the unauthorized expansion of corporate liability. ²²

4. Engineering the Firewall: Technical Implementation

Blueprint

Implementing a Deterministic Action Layer requires a sophisticated technology stack that goes far beyond simple API calls to a foundation model. Veriprajna employs a multi-stage pipeline designed to catch, categorize, and neutralize risks before they reach the user. This section outlines the engineering blueprint for a compliant Enterprise AI agent.

4.1 Semantic Routing and Intent Gating

The first line of defense is Semantic Routing . Unlike brittle keyword matching systems of the past (which might miss "I want cash back" if looking only for "refund"), semantic routers use high-dimensional vector embeddings to determine the intent of a user's query with high precision. ²³

We utilize advanced routing frameworks such as vLLM Semantic Router or NVIDIA NeMo Guardrails . The workflow is as follows:

1.​ Input Vectorization: The user's query is converted into a vector embedding using an encoder model (e.g., OpenAI's text-embedding-3 or a local BERT model).

2.​ Route Matching: The system calculates the cosine similarity between the query vector and a set of predefined "canonical examples" for restricted topics (e.g., "refund," "warranty," "privacy policy," "liability").

3.​ Threshold Gating: If the similarity score exceeds a strict threshold (e.g., 0.85), the query is intercepted. It is not sent to the LLM for general generation.

4.​ Deterministic Hand-off: The router directs the query to a specific function or script (e.g., execute_refund_policy_check()) rather than the chat model. ²⁴

This architecture effectively creates a "Gateway" that prevents the "jailbreak" scenarios where users try to trick the LLM into ignoring its instructions. Because the router sits outside the LLM context, prompt injection attacks designed to manipulate the model's instructions are rendered ineffective against the routing logic. ²⁵

4.2 Function Calling as Strict Enforcement

Once a sensitive intent is detected, we utilize Function Calling (or Tool Use) to enforce strict boundaries. Modern LLMs (like GPT-4 and Claude 3) have been fine-tuned to output structured JSON objects that call specific functions rather than generating unstructured text. ²⁶

●​ The Schema: We define a strict schema for every compliance-related action. For example, a check_refund_eligibility function requires specific arguments: ticket_id, purchase_date, and travel_date.

●​ The Execution: The LLM does not calculate the refund. It extracts the parameters from the conversation and passes them to a deterministic code block (Python/SQL/Java).

●​ The Response: The code block executes the logic (e.g., checking the database for the ticket status) and returns the result. The LLM is then instructed to only rephrase this result into a polite sentence, without adding, removing, or interpreting the information. We use Output Rails to verify the final text matches the data returned by the function. ²⁸

This "Function Calling" paradigm transforms the LLM from a decision-maker into a translator—translating natural language into API calls, and API responses back into natural language. The "deciding" is done by code, not by probability. ²⁹

4.3 Truth Anchoring Networks (TAN)

For complex scenarios requiring nuanced reasoning (e.g., medical support, technical troubleshooting, or complex legal queries), we implement a Truth Anchoring Network (TAN) . This involves validating the LLM's proposed response against an OWL Ontology or Knowledge Graph. ¹⁰

Before the response is shown to the user, the TAN validates the relationships asserted in the text.

●​ Example: If the LLM suggests "Drug A interacts safely with Drug B," the TAN queries the medical knowledge graph.

●​ Validation: If the graph shows a "Severe Interaction" edge between Drug A and Drug B, the response is blocked.

●​ Enforcement: The system issues a safety warning or a hard refusal.

This acts as a logical firewall against hallucination, using symbolic logic to audit the neural output in real-time. It provides auditability (decisions can be traced to a specific rule ID) and safety (compliance is enforced mathematically). ¹⁰

4.4 Chain of Verification (CoVe) and Red Teaming

For semi-structured queries where strict code isn't possible, we employ Chain of Verification (CoVe) prompting. This is an internal recursive loop designed to reduce error rates. ³⁰

1.​ Draft: The LLM generates a draft response.

2.​ Verify: A separate "Auditor Agent" generates validation questions based on the draft (e.g., "Does the source text explicitly mention a 90-day refund window?").

3.​ Correction: If the validation step fails (i.e., the source text says "no refunds"), the response is rewritten or flagged for human review.

This internal "Red Teaming" loop significantly reduces the rate of factual errors. Furthermore, we employ Adversarial Testing during the development phase, bombarding the agent with "jailbreak" attempts, edge cases, and confusing prompts to ensure the guardrails hold firm. ³²

4.5 NeMo Guardrails: The Colang Advantage

We specifically leverage NVIDIA NeMo Guardrails, an open-source toolkit designed to add

programmable safety to LLM-based systems. ²⁸ NeMo uses a specialized modeling language called Colang to define conversational flows that override the LLM's probabilistic generation.

●​ Input Rails: Before the user's message reaches the LLM, NeMo checks it against a blacklist of topics or malicious patterns.

●​ Dialog Rails: We define specific flows using Colang. For example: ​

Code snippet

define user ask refund
 "I want a refund"
 "Can I get my money back?"

define flow handle refund
 user ask refund
 $status = execute check refund

 if $status == "eligible"
  bot say "You are eligible."
 else
  bot say "According to our policy, refunds are not permitted after travel."

​ ​ In this snippet, the LLM is not generating the decision. It is merely following a pre-defined flow. The $status variable is populated by a hard-coded Python function. The response "According to our policy..." is hard-coded, ensuring legal precision. ²⁸

5. Regulatory Compliance: Building for the EU AI Act and ISO 42001

Enterprise AI does not exist in a vacuum. The regulatory landscape has shifted dramatically, moving from voluntary guidelines to strict liability frameworks. Veriprajna’s architecture is specifically designed to meet the rigorous standards of the EU AI Act, GDPR, and the new ISO 42001 standard.

5.1 The EU AI Act: Article 14 (Human Oversight)

The EU AI Act classifies many customer-facing AI systems (especially those in essential services like transport and banking) as High-Risk. Article 14 explicitly mandates Human Oversight . It requires that systems be designed so that humans can effectively oversee them, interpret their outputs, and intervene (the "stop button"). ³⁴

Veriprajna Compliance Strategy:

●​ Design for Oversight: Our Deterministic Action Layers provide the technical mechanism for this oversight. By hard-coding policies, we ensure that the "human in the loop" (the policy writer/compliance officer) has ultimate control over the AI's boundaries.

●​ Intervention: The architecture supports a "human handoff" protocol. If the AI reaches a confidence threshold below 95% or encounters a blocked intent, it automatically stops and routes the query to a human agent. This satisfies the requirement for human intervention capabilities. ³⁶

●​ Understanding Capabilities: By using explicit logic trees, the capabilities and limitations of the system are transparent, preventing "automation bias" where users blindly trust the AI. ³⁴

5.2 GDPR Article 22: Automated Decision Making

GDPR Article 22 grants individuals the right not to be subject to a decision based solely on automated processing (including profiling) if it produces legal effects or similarly significant effects. ³⁷ Denying a refund or a loan application is a "significant effect."

Veriprajna Compliance Strategy:

●​ Explainability: Neural networks are "black boxes"—it is difficult to explain exactly why a specific weight led to a specific word. Deterministic logic, however, is transparent. When a user asks "Why was I denied?", our system can point to the specific logic node (e.g., "Credit score < 600" or "Ticket Status = Flown").

●​ Rights Safeguards: Our architecture logs the logic path for every decision. This allows the data controller to reconstruct the decision process and demonstrate that it followed approved rules, satisfying the "right to explanation". ³⁹

5.3 ISO 42001: AI Management Systems

ISO 42001 is the first global standard for AI governance. ⁴⁰ It requires organizations to establish an AI Management System that addresses risk through four key functions: Map, Measure, Manage, and Govern .

Veriprajna Compliance Strategy:

●​ Map: We use our architectural diagrams to map exactly where probabilistic vs. deterministic logic is used, identifying the "context" of the AI system as required by the standard. ⁴¹

●​ Measure: We implement metrics for hallucination rates, intent recognition accuracy, and "fallback" frequency (how often the bot calls for human help).

●​ Manage: The Silence Protocol and Guardrails are key "controls" for managing "Model Risk" and "Hallucination Risk" as defined in Annex A of the standard. ⁴²

●​ Govern: Our "Digital Employee" logs provide a complete audit trail of every interaction, routing decision, and policy execution. This satisfies the documentation and accountability requirements, ensuring the system is "audit-ready" for certification. ⁴²

5.4 NIST AI Risk Management Framework (AI RMF)

The NIST AI RMF provides a flexible framework for managing AI risks. ⁴⁴ Our architecture aligns with its core functions:

●​ Govern: Establishing the "DAL" as the governing policy enforcement mechanism.

●​ Map: Identifying high-risk contexts (payments, legal advice) where the DAL must be active.

●​ Measure: Using "Red Teaming" results to quantify the effectiveness of the guardrails.

●​ Manage: Prioritizing risks and acting upon them by updating the deterministic scripts. ⁴⁵

6. Industry Vertical Applications

The necessity of Deterministic Action Layers extends far beyond the travel and hospitality sector highlighted in the Moffatt case. Any industry with regulatory constraints or financial transactions faces similar risks.

6.1 Fintech and Banking

In the financial sector, a "creative" chatbot is a regulatory disaster waiting to happen. If an AI assistant advises a client to buy a specific stock, misstates an interest rate, or hallucinates a loan approval, it violates SEC, FINRA, and consumer protection regulations.

●​ The Risk: A probabilistic model might read an outdated document about "2021 Interest Rates" and offer a 0.5% mortgage rate in a 5% environment.

●​ The Veriprajna Application: Our "Truth Anchoring" prevents the AI from giving financial advice entirely. It routes queries about "investment" to a disclaimer and a link to a certified advisor. Interest rate queries are routed to a real-time API that pulls the live rate from the core banking system. The bot never "remembers" an old rate; it fetches the current truth every time. ¹⁰

6.2 Healthcare and Life Sciences

In healthcare, hallucination is a life-safety issue. A chatbot misinterpreting a dosage instruction or a drug interaction could lead to patient harm and massive malpractice liability.

●​ The Risk: An LLM might hallucinate that "ibuprofen is safe with warfarin" because it saw those words near each other in a training text, ignoring the "not" in between.

●​ The Veriprajna Application: We use Ontology-based Guardrails . If a user asks about drug interactions, the response is not generated by the LLM. It is constructed from a trusted medical database (e.g., "Interaction: Severe - Risk of Bleeding"). The LLM's only job is to wrap that database result in a compassionate sentence template. If the ontology shows a risk, the system blocks any contrary generation. ¹⁰

6.3 Legal Tech and Professional Services

For law firms and consultancies, AI agents drafting contracts or reviewing documents must be

100% accurate. The "fake case citation" scandal, where lawyers were sanctioned for submitting AI-hallucinated precedents, illustrates the danger.

●​ The Risk: An AI legal assistant inventing case law to support an argument because the user's prompt was leading.

●​ The Veriprajna Application: We use Chain of Verification to cross-reference every citation generated by the AI against a trusted legal database (like Westlaw or LexisNexis). If the citation does not exist in the database, it is stripped from the output. The system is configured to prioritize "No Answer" over "Fabricated Answer". ¹²

6.4 Retail and E-Commerce

While less regulated, retail faces massive financial exposure from "Price Glitch" hallucinations where bots offer incorrect discounts or promise returns against policy.

●​ The Risk: A bot promising a "free replacement" for a non-warrantied item.

●​ The Veriprajna Application: Refund eligibility is determined by a deterministic script that checks the order_date and item_category against the SQL database of return policies. The bot merely reports the outcome: "This item is outside the 30-day window." It cannot override the math. ⁴⁶

7. The "Deep AI" Value Proposition vs. The Wrapper Economy

Veriprajna positions itself not as a builder of chatbots, but as an architect of Enterprise AI Governance . The difference is profound and structural.

7.1 Wrapper vs. Solution

●​ The Wrapper: A thin UI over OpenAI’s API. It relies on "Prompt Engineering" (asking the model nicely to be safe). It is vulnerable to prompt injection, hallucination, drift, and model updates that change behavior overnight. It is a liability nightmare.

●​ The Deep Solution: A vertical stack including Vector Databases (RAG), Semantic Routers, Knowledge Graphs, Deterministic Code execution, and Logging/Audit layers. It is secure, auditable, compliant, and robust against model changes. ⁴⁷

7.2 The Economic Case for "Heavy" Architecture

Critics may argue that Deterministic Action Layers are expensive to build compared to simple prompts. This view is myopic.

●​ The Cost of "Cheap" AI: As seen in Moffatt, the cost of a "cheap" wrapper involves legal fees, damages, regulatory fines, and massive reputational harm.

●​ The Mitigation Tax: Enterprise "hallucination mitigation" currently costs ~$14,200 per employee/year in lost productivity as humans double-check AI work. ¹²

●​ The Insurance Policy: Investing in a robust architecture upfront is an insurance policy against future liability. It allows the enterprise to scale AI adoption without scaling risk.

7.3 Strategic Implementation Roadmap

For our clients, we deploy a rigorous four-phase rollout:

1.​ Discovery & Risk Mapping: We audit your existing workflows to identify high-stakes intents (Financial, Legal, Safety). We classify your AI risks according to ISO 42001 standards.

2.​ Semantic Routing Configuration: We build the "traffic cop" layer. We train the router on your specific domain language to ensure it catches sensitive queries with near-100% recall.

3.​ Deterministic Logic Encoding: We translate your corporate policies (PDFs) into executable code (Python/SQL) and Knowledge Graphs. We build the "Truth Anchors."

4.​ Red Teaming & Validation: We stress-test the guardrails using adversarial attacks. We try to force the bot to offer a refund or give bad advice. We only deploy when the "Silence Protocol" holds under pressure. ³²

8. Conclusion: The Era of the Agentic Contract

The Moffatt v. Air Canada ruling was not an anomaly; it was a judicial premonition. As AI moves from "Chat" to "Agentic" workflows—where bots can book flights, transfer money, sign contracts, and manage supply chains—the legal fiction that "the user should verify the info" is dead. If your AI says it, your company has signed it. ⁹

Your chatbot is a legally binding employee. It needs the same training, the same oversight, and the same strict boundaries as a human employee handling corporate funds. You would not allow a junior employee to invent a refund policy based on "creativity." You should not allow your AI to do so either.

The "Black Box" excuse is gone. The era of the "Probabilistic Wrapper" is ending. The future belongs to Deterministic Action Layers .

Veriprajna builds the infrastructure that makes this safety possible. We silence the hallucination to amplify the trust. In the high-stakes world of enterprise AI, the most valuable feature is not what the AI can say, but what it is prevented from saying.

Is your chatbot writing checks your business can't cash?

End of Report

Report generated by Veriprajna Strategy Group. All analysis aligns with the Veriprajna 'Deep AI' methodology.

Works cited

1. BC Tribunal Confirms Companies Remain Liable for Information Provided by AI Chatbot, accessed December 10, 2025, https://www.americanbar.org/groups/business_law/resources/business-law-today/2024-february/bc-tribunal-confirms-companies-remain-liable-information-provided-ai-chatbot/

2. Air Canada chatbot case highlights AI liability risks - Pinsent Masons, accessed December 10, 2025, https://www.pinsentmasons.com/out-law/news/air-canada-chatbot-case-highlights-ai-liability-risks

3. Lying Chatbot Makes Airline Liable: Negligent Misrepresentation in Moffatt v Air Canada - Allard Research Commons, accessed December 10, 2025, https://commons.allard.ubc.ca/cgi/viewcontent.cgi?article=1376&context=ubclawreview

4. AI Chatbot flies solo and Air Canada foots the bill - Moffatt v. Air Canada, accessed December 10, 2025, https://inquisitiveminds.bristows.com/post/102j3zc/ai-chatbot-flies-solo-and-air- canada-foots-the-bill-mofatf t-v-air-canada

5. Moffatt v. Air Canada: A Misrepresentation by an AI Chatbot - McCarthy Tétrault LLP, accessed December 10, 2025, https://www.mccarthy.ca/en/insights/blogs/techlex/mofatf t-v-air-canada-misrepr esentation-ai-chatbot

6. Navigating Artificial Intelligence Liability: Air Canada's AI Chatbot Misstep Found to be Negligent Misrepresentation - Cox & Palmer, accessed December 10, 2025, https://coxandpalmerlaw.com/publication/navigating-artificial-intelligence-liability-air-canadas-ai-chatbot-misstep-found-to-be-negligent-misrepresentation/

7. Legally binding hallucinations - Language Log, accessed December 10, 2025, https://languagelog.ldc.upenn.edu/nll/?p=62661

8. The Law of AI is the Law of Risky Agents Without Intentions, accessed December 10, 2025, https://lawreview.uchicago.edu/online-archive/law-ai-law-risky-agents-without-intentions

9. From Fine Print to Machine Code: How AI Agents are Rewriting the Rules of Engagement: Part 1 of 3, accessed December 10, 2025, https://law.stanford.edu/2025/01/14/from-fine-print-to-machine-code-how-ai-agents-are-rewriting-the-rules-of-engagement/

10. Beyond RAG: Solving “Compliance Hallucinations” with Gemini ..., accessed December 10, 2025, https://medium.com/google-cloud/beyond-rag-solving-compliance-hallucinations-with-gemini-neuro-symbolic-ai-b48fcd2f431f

11. AI Hallucinations in the Enterprise: Risks Explained - SID Global Solutions, accessed December 10, 2025, https://sidgs.com/article/ai-hallucinations-explained-risks-every-enterprise-must-address/

12. The Hidden Cost Crisis: Economic Impact of AI Content Reliability Issues | Nova Spivack, accessed December 10, 2025, https://www.novaspivack.com/technology/the-hidden-cost-crisis

13. The Basics of Probabilistic vs. Deterministic AI: What You Need to Know, accessed December 10, 2025, https://www.dpadvisors.ca/post/the-basics-of-probabilistic-vs-deterministic-ai-what-you-need-to-know

14. Understanding the Three Faces of AI: Deterministic, Probabilistic, and Generative | Artificial Intelligence | MyMobileLyfe | AI Consulting and Digital Marketing, accessed December 10, 2025, https://www.mymobilelyfe.com/artificial-intelligence/understanding-the-three-faces-of-ai-deterministic-probabilistic-and-generative/

15. Managing AI hallucination risk: a guide for enterprise risk managers - Resilience Forward, accessed December 10, 2025, https://resilienceforward.com/managing-ai-hallucination-risk-a-guide-for-enterprise-risk-managers/

16. Hybrid Retrieval-Augmented Generation (RAG): A Practical Guide | by Jay Kim | Medium, accessed December 10, 2025, https://medium.com/@bravekjh/hybrid-retrieval-augmented-generation-rag-a-practical-guide-dab74fc28ee9

17. I rewrote hybrid search four times - here's what actually matters : r/Rag - Reddit, accessed December 10, 2025, https://www.reddit.com/r/Rag/comments/1pd7tao/i_rewrote_hybrid_search_four_times_heres_what/

18. How Neurosymbolic AI Brings Hybrid Intelligence to Enterprises, accessed December 10, 2025, https://orange-bridge.com/latest-ai-data-trends/neurosymbolic-ai-promises-to-bring-hybrid-intelligence-to-enterprises

19. Deterministic Graph-Based Inference for Guardrailing Large Language Models | Rainbird AI, accessed December 10, 2025, https://rainbird.ai/wp-content/uploads/2025/03/Deterministic-Graph-Based-Inference-for-Guardrailing-Large-Language-Models.pdf

20. Neuro-Symbolic AI Explained: Insights from Beyond Limits' Mark James, accessed December 10, 2025, https://www.beyond.ai/blog/neuro-symbolic-ai-explained

21. Beyond Pattern Matching - F'inn, accessed December 10, 2025, https://www.finn-group.com/post/beyond-patern-matching-the-quest-for-systetm-2-thinking-in-artificial-intelligence

22. What Is Chatbot Design? - IBM, accessed December 10, 2025, https://www.ibm.com/think/topics/chatbot-design

23. Bringing intelligent, efficient routing to open source AI with vLLM Semantic Router - Red Hat, accessed December 10, 2025, https://www.redhat.com/en/blog/bringing-intelligent-efficient-routing-open-source-ai-vllm-semantic-router

24. Why You Need Semantic Routing in Your LangGraph Toolkit: A Beginner's Guide Medium, accessed December 10, 2025, https://medium.com/@bhavana0405/why-you-need-semantic-routing-in-your-langgraph-toolkit-a-beginners-guide-c09127bea209

25. Intent-Aware LLM Gateways: A Practical Review of vLLM Semantic Router, accessed December 10, 2025, https://joshuaberkowitz.us/blog/github-repos-8/intent-aware-llm-gateways-a-practical-review-of-vllm-semantic-router-1170

26. What is LLM Function Calling and How Does it Work? | Quiq Blog, accessed December 10, 2025, https://quiq.com/blog/llm-function-calling/

27. Function calling using LLMs - Martin Fowler, accessed December 10, 2025, https://martinfowler.com/articles/function-call-LLM.html

28. NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems. - GitHub, accessed December 10, 2025, https://github.com/NVIDIA-NeMo/Guardrails

29. LLM Function Calling Explained: A Deep Dive into the Request and Response Payloads | by James Tang | Medium, accessed December 10, 2025, https://medium.com/@jamestang/llm-function-calling-explained-a-deep-dive-into-the-request-and-response-payloads-894800fcad75

30. Implement Chain-of-Verification to Improve AI Accuracy - Relevance AI, accessed December 10, 2025, https://relevanceai.com/prompt-engineering/implement-chain-of-verification-to-improve-ai-accuracy

31. Chain of Verification: Prompt Engineering for Unparalleled Accuracy - Analytics Vidhya, accessed December 10, 2025, https://www.analyticsvidhya.com/blog/2024/07/chain-of-verification/

32. Five Competitive Advantages from Real-Time GenAI Guardrails - ActiveFence, accessed December 10, 2025, https://www.activefence.com/blog/five-competitive-advantages-from-real-time-genai-guardrails/

33. NeMo Guardrails | NVIDIA Developer, accessed December 10, 2025, https://developer.nvidia.com/nemo-guardrails

34. Article 14: Human Oversight | EU Artificial Intelligence Act, accessed December 10, 2025, https://artificialintelligenceact.eu/article/14/

35. AI Act Service Desk - Article 14: Human oversight - European Union, accessed December 10, 2025, https://ai-act-service-desk.ec.europa.eu/en/ai-act/article-14

36. Full article: 'Human oversight' in the EU artificial intelligence act: what, when and by whom?, accessed December 10, 2025, https://www.tandfonline.com/doi/full/10.1080/17579961.2023.2245683

37. Art. 22 GDPR – Automated individual decision-making, including profiling General Data Protection Regulation (GDPR), accessed December 10, 2025, https://gdpr-info.eu/art-22-gdpr/

38. Automated Decision Making: Overview of GDPR Article 22, accessed December 10, 2025, https://gdprlocal.com/automated-decision-making-gdpr/

39. Automated Decision-Making under Article 22 GDPR (Chapter 4) - Algorithms and Law, accessed December 10, 2025, https://www.cambridge.org/core/books/algorithms-and-law/automated-decisionmaking-under-article-22-gdpr/4EBDF691C31712A4E82997A8B7CABE98

40. Understanding ISO 42001 and Demonstrating Compliance - ISMS.online, accessed December 10, 2025, https://www.isms.online/iso-42001/

41. Safeguard the Future of AI: The Core Functions of the NIST AI RMF - AuditBoard, accessed December 10, 2025, https://auditboard.com/blog/nist-ai-rmf

42. Your Guide to ISO 42001 Controls for AI Governance - Sprinto, accessed December 10, 2025, https://sprinto.com/blog/iso-42001-controls/

43. Understanding ISO 42001: The World's First AI Management System Standard | A-LIGN, accessed December 10, 2025, https://www.a-lign.com/articles/understanding-iso-42001

44. NIST AI Risk Management Framework: A tl;dr - Wiz, accessed December 10, 2025, https://www.wiz.io/academy/nist-ai-risk-management-framework

45. Navigating the NIST AI Risk Management Framework - Hyperproof, accessed December 10, 2025, https://hyperproof.io/navigating-the-nist-ai-risk-management-framework/

46. AI Agents and the Law - arXiv, accessed December 10, 2025, https://arxiv.org/html/2508.08544v1

47. Building Enterprise-Grade GenAI Applications: Key Takeaways from Our Expert Panel, accessed December 10, 2025, https://www.atscale.com/blog/building-enterprise-genai-applications/

48. An honest overview of Cohere AI for enterprise use in 2025 - eesel AI, accessed December 10, 2025, https://www.eesel.ai/blog/cohere-ai