Norm Ai vs Regology: how they compare in 2026
Norm Ai and Regology both turn regulation into something a compliance team can act on, and they arrive from different directions. Norm Ai encodes rules as executable agents. Regology maintains a law library and layers agents over it. Norm Ai is stronger on how a determination is reached: regulations are compiled through its own domain specific language into human readable decision trees that an agent traverses to reach a determination. It also answers the advice line question structurally, having launched a separate law firm in 2026 while the software delivers analysis. Regology is stronger on what a procurement review asks for. It names BARR Advisory as the auditor behind its SOC 2 Type II examination with the scope stated, and it names ServiceNow, Archer and Hyperproof as the systems it sits alongside rather than replaces. Its Terms of Service also state a liability cap and an intellectual property indemnity, while Norm Ai publishes no customer agreement. Neither states a training position or a retention period.
At a glance
All 15 axes, side by side
The same grid applied to every vendor in the index, graded from public sources. Hover a grade to see what the letter means on that axis.
AI Centrality
How much of the product is actually AI. Whether the machine learning is the mechanism the buyer is paying for or a feature layered onto conventional software, and whether the vendor is specific about which is which.
The AI is the product, and the company is organized around building it. Regulations are converted into executable AI agents through a proprietary domain specific language. Agents traverse decision trees to reach compliance determinations, and the entire platform, LEAP, exists to build, evaluate and deploy those agents. The vendor describes itself as the Regulatory AI agent company. Remove the models and what remains is a representation of regulations with no ability to apply them. The company also created a job function, Legal Engineer, to build AI agents, staffed by former attorneys given intensive language model training.
The Smart Law Library is a curated, continuously updated body of federal law, all fifty states and international jurisdictions. It works as a product without the models, since finding a law, following its changes and reading its version history all run on it directly. Customers can import their own law libraries into it from spreadsheets and PDFs. The models drive the work on top. Three named agents, for Regulatory Change, Compliance and Research, decide which laws apply to an organization, assess the impact of incoming changes, and generate obligations, risks, controls and policies mapped to source law. Reggi, the generative assistant, answers questions in plain language, rates incoming alerts for relevance and is free for individual use.
Citation Accuracy and Hallucination Disclosure
Whether the vendor publishes measured accuracy on citations and assertions, grounds output to primary sources, and says plainly what its system does when it does not know. Legal has a documented public record of fabricated citations reaching filed briefs, so an untested claim of accuracy is not evidence.
Grounding is real, enforced by the architecture and documented, but no measurement is published. Regulations are encoded through a proprietary domain specific language into decision trees that a person can read and a machine can execute. An agent follows that tree step by step to reach a determination rather than generating an answer from model memory. The vendor states every determination carries an explanation identifying what may be problematic and under which regulation. A compliance team can therefore see the legal basis for the outcome and what would need to change. Because the reasoning path is the encoded regulation itself, the output traces to a provision rather than to a retrieved passage. LEAP is stated to include automated evaluation pipelines, so evaluation is built into deployment. Searches of the platform pages, the resources section, the company blog and the trust center found no accuracy figure, error rate, test set, published evaluation method or independent benchmark participation. The founder co authored a widely used legal reasoning benchmark.
Traceability is the design goal. Each alert, assignment, decision, approval, rejection and policy update carries a timestamp and a link to the law behind it, so an obligation or control the platform generates leads back to the provision it comes from. That provision sits in Regology's own maintained library rather than the open web, and Reggi answers carry citations to it on every plan, the free one included. No accuracy figure, error rate for applicability decisions, test set or benchmark is published for the agents or for Reggi.
Autonomy and Oversight Model
What the system decides on its own, what a lawyer must approve, and whether the vendor documents where the review point sits. A tool that drafts under review and a tool that files without one are different products and different risks.
A real published commitment with a described division of labor, short of thresholds. The vendor states the operating model plainly: an agent conducts the initial compliance review of, for example, marketing communications against SEC or FINRA rules, and a human professional then reviews the findings and completes the determination. That allocates the decision rather than asserting human involvement. Supporting surfaces are described: explanations attached to every determination, and collaboration tooling through which teams tag each other, review AI judgments and centralize decision history. Oversight is also a staffing structure, with Legal Engineers building and supervising the agents and adjusting them to client specific guidelines. The vendor is separately building supervisory agents that monitor other AI systems for compliance, which is an oversight product rather than an oversight policy. Not located: any threshold at which an agent defers, what it does when a determination is genuinely ambiguous, and what the vendor commits to when a determination is wrong.
People sign off on what the agents produce. Assignments, decisions, approvals and rejections are captured and timestamped, so a person accepts or rejects each output and that choice stays in the record, with rejection a logged path rather than an exception. The agents automate repetitive manual work and flag coming changes, and compliance teams act on what they produce. The Responsible AI Policy commits to human in the loop processes for critical decisions. No threshold at which an agent defers is described, nor what the system does when applicability is uncertain or how much of its output is expected to be reviewed.
Operational and Outcome Evidence
Named, dated evidence that the product works in production at real firms or legal departments. Case studies with figures and identified customers count. Unattributed testimonials and launch announcements do not.
Institutional signals stand in for deployment evidence. Published material describing a Legal Engineer's working day refers to a client meeting with a top ten global asset manager, and the vendor states a focus on Fortune 100 customers, but no customer is named. Vanguard, Blackstone, Citi and TIAA are strategic investors from the regulated industries the product serves; this is context, not evidence of a deployment. Searches of the platform pages, the resources section, the company blog and the news announcements found no named customer with figures and a date, no case study with an assessable method, and no adoption count.
Seven case studies describe deployments without naming the customer. They cover a sports betting platform, a large financial regulator, a global social media and technology company, a currency exchange provider, a regional bank, a cryptocurrency exchange and one of the largest American banks. The regional bank gained automated impact analysis, faster assessment of regulatory changes and better audit documentation, and the study of the large bank covers regulatory exemptions handled with AI. None of the studies gives a figure or a date. Regology was founded by former PwC compliance professionals with seven years implementing regulatory programs at large financial institutions. No named customer is paired with a measured outcome.
Privilege and Confidentiality Posture
How client confidences are handled: attorney client privilege and work product treatment, segregation of one client matter from another, whether client data trains any model, and what the vendor commits to in writing rather than in marketing.
Confidentiality is asserted through a trust program without specific commitments. The trust center states a commitment to rigorous security practices including SOC 2 compliance, continuous monitoring, and a company wide culture of data protection. That is a general assurance rather than a set of terms. Searches of the trust center, the platform pages, the resources section and the company blog found no statement on whether customer content may be used to train or improve models, no retention or deletion terms, no segregation model between customers, and no treatment of privilege or work product. The platform reviews internal communications, draft marketing and investor materials, and agreements before they are issued. It sees regulated firms' content at the point where a compliance problem would still be undisclosed. The trust center renders client side, its control detail was not read, and it may say more.
Customer data stays the customer's property, and section 6.A of the terms licenses Regology to use it only as needed to provide the services. Section 11 binds Regology to keep customer confidential information secret, to give prompt notice of legal process and to return or destroy that information on request. SOC 2 Type II covers security, availability and confidentiality, with BARR Advisory named as auditor in earlier announcements. Nothing covers segregation between customers, privilege or work product. The material in the platform is a customer's own obligations, controls, policies and compliance decisions, including what it rejected.
UPL and Professional Responsibility Posture
Whether the vendor is clear that it supplies a tool rather than legal advice, who its audience is, and how it addresses unauthorized practice of law, competence and supervision duties, and jurisdiction limits. ABA Formal Opinion 512 is the reference point. Where the advice line is not the duty a product raises, the axis is read through the nearest professional duty it does raise: judicial conduct rules and the reviewing duty for products sold only to courts, and the duty to bill for time actually spent for products that draft time entries.
Norm Ai answers the advice question through structure rather than a written position. It builds on both sides of the boundary between software and legal advice: the platform performs compliance analysis for enterprises, and in 2026 the company launched Norm Law, which it describes as the first fully AI native law firm for institutional clients. Norm Law is chaired by the former chairman of Sidley Austin and is recruiting partners. Legal advice is therefore delivered by a law firm with admitted attorneys while the software delivers analysis, which is a real separation rather than a disclaimer. In the operating model, an agent performs initial review and a human professional completes the determination, and in the law firm an experienced attorney decides what happens next. The team includes former regulators, among them a former SEC Commissioner. Searches of the platform pages, the resources section and the company site found no statement on advice versus tooling, no treatment of competence or supervision duties, and no jurisdiction limits.
Section 17 of the terms says Reggi AI uses generative AI and a large language model to answer legal and regulatory questions, and that the accuracy, completeness and timeliness of its answers cannot be guaranteed. Customers are told to verify every response independently, no attorney client relationship arises, and Reggi is no substitute for legal advice, so a customer consults its own lawyer. Section 18 says the platform, services and content give no legal, tax or other professional advice. The buyers are compliance, legal and risk teams. No bar guidance is named, and nothing describes how the product supports a supervising lawyer.
AI Governance and Bias Disclosure
Published governance over model behavior: who owns it inside the vendor, what is tested before release, and what is disclosed about disparate output across matter types, parties, or populations.
There is a governance framework with real substance built into the product, without certification, published results or bias disclosure. Several elements are mechanisms rather than principles. LEAP is stated to include automated evaluation pipelines as part of taking agents into production, so evaluation is a gate. Every determination carries an explanation, and the decision tree is readable by a person, so a lawyer can audit an agent's reasoning without reading code. Legal Engineers build and supervise agents and maintain them as regulations change. The company also builds supervisory agents to monitor other AI systems against laws and policies, and convenes a forum on AI, law and policy involving legislators and regulators. Not found: an AI management certification such as ISO 42001, a named owner accountable for model governance, published evaluation results as distinct from the existence of pipelines, and any disclosure about uneven output across firms, content types or populations.
The Responsible AI Policy, effective 1 December 2024, sets out an AI risk management framework grounded in principles from the National Institute of Standards and Technology. It covers every AI system Regology develops, deploys or uses. An AI Ethics and Risk Committee approves AI projects, and an AI Risk Management Lead runs day to day risk assessment, mitigation and staff education. Each AI system is assessed for safety, security, privacy, fairness and accountability. The policy commits to regular bias audits and fairness tests, explainability techniques, human in the loop processes for critical decisions, continuous monitoring and staff training. No audit result is published, the lead is not named, and nothing covers uneven output across jurisdictions, industries or organization types.
AI Safety and Data Stewardship
Retention, deletion, access control, and what happens to prompts and documents after they are processed. Whether the vendor states its subprocessors and its incident practice, or leaves the buyer to assume.
A general security posture is stated without operational detail. The trust center states SOC 2 compliance, continuous monitoring, and a company wide culture of data protection and integrity. The trust center sits at a stable URL on a recognized compliance platform, which gives a self serve route. Searches of the trust center, the platform pages and the company site found no retention period, deletion control, encryption specifics, access control detail, named subprocessor list, hosting provider or region, or incident or breach notification practice. The trust center renders its contents client side; they were not read and may say more.
Section 11.E of the terms has Regology return or destroy customer confidential information at the customer's option on written request, then certify it in writing, and section 10 confines personal data to performing the services. SOC 2 Type II covers security, availability and confidentiality, and Regology states a continuing commitment to keeping it. The Responsible AI Policy names data security measures, responsible data sourcing and consent mechanisms in a line each. No default retention period, encryption detail, access control description, named subprocessor list, hosting provider or incident notice practice is published.
AI Liability and Recourse
What the vendor stands behind contractually when its output is wrong. Indemnities, caps, carve outs, insurance, and whether any of it is published or only reachable through a negotiated agreement.
No published indemnity, liability cap, carve out, warranty on output or insurance position was found, and no customer terms of service is published on the site. This product makes compliance determinations about content a regulated firm is about to publish. A wrong determination can therefore lead to a regulatory finding against the customer rather than merely a bad draft, and nothing published addresses who carries that exposure. The law firm entity, Norm Law, would carry professional liability of its own, which the software vendor's silence does not cover.
Section 22.A of the terms, last updated 9 January 2025, has Regology defend and indemnify the customer against third party claims that the platform or services infringe intellectual property. Combination and refusal of replacement exclusions apply, and the remedies are to procure a right, modify, replace, or terminate and refund unused fees. Section 21 excludes consequential loss and caps each party at the fees paid or payable in the twelve months before the claim, with indemnification, gross negligence and willful misconduct outside the cap. Section 19 provides the platform and content as is, section 17 says Reggi answers cannot be guaranteed and must be verified, and section 20 disclaims third party content. No insurance is stated.
Practice Systems Integration Depth
How deeply the product reaches into the systems legal work already lives in: document management such as iManage and NetDocuments, Word and Outlook, contract lifecycle management, matter management, e-billing, and court filing systems.
Integration is described at workflow level without named connectors. The vendor's stated design is compliant by design, embedding compliance checks into business activities rather than running them separately. It covers content generated by AI, internal communications, agreements, marketing content and sales materials, and the platform is described as monitoring workflows and alerting when something drifts out of compliance. Searches of the platform pages, the resources section and the company site found no integrations page, no named connector for content management, communications archiving, document management or customer relationship systems, and no API documentation. The product's value depends on intercepting content before it is published, and no integration surface is named.
Regology sits beside governance, risk and compliance systems as a regulatory intelligence and data layer rather than replacing them. Direct integrations with GRC platforms, ServiceNow, Archer and Diligent among them, sync authority documents, citations and regulatory content into the GRC tool, with change alerts, impact assessments and a backlink to the full document in Regology. A GRC tool without a direct connector, or a homegrown system, connects through an open API that takes REST and JSON, XML, CSV and flat files over SFTP. Customers can upload existing law libraries from spreadsheets and PDFs. GRC and custom API integrations come with the Enterprise plan. What an administrator configures for each integration is not described.
Deployment Model and Data Residency
Where the software runs and where the data sits. Multi tenant cloud, single tenant, private deployment, on premises, and whether region of residence is a published option or an enterprise conversation.
Nothing was found on the deployment model. No hosting provider, named region, residency choice for customers or tenancy model is published, and nothing states where processing happens as distinct from where data is stored. The customer base is regulated financial institutions, for whom data location is often a supervisory requirement.
Regology runs as a cloud service, and the Enterprise plan includes round the clock cloud IT support. No hosting provider, region, customer selectable residency or tenancy model is published, and nothing separates where processing happens from where data is stored. Coverage reaches more than 135 countries and the buyers include multinational compliance teams.
Security Certifications and Trust Center
Independent attestation a buyer can pull without a sales call: SOC 2, ISO 27001, penetration test summaries, a trust center with current reports and named scope rather than a badge image.
A trust center exists, and the certification is claimed without scope or evidence. The trust center sits at a stable URL on a recognized compliance automation platform and states a commitment to rigorous security practices including SOC 2 compliance and continuous monitoring. The portal is a real self serve route. Not found: whether the SOC 2 is Type 1 or Type 2, any coverage period, audit scope, report date, named auditing firm, other certification, or published request flow for the report. The trust center renders its contents client side, and they were not read. That is a limit of the research, not a finding about the vendor, and the portal may say more.
Regology holds SOC 2 Type II. Its announcements name BARR Advisory as auditor and give the scope as security and availability, later security, confidentiality and availability, and Regology states a continuing commitment to maintaining the certification. No current report date, coverage period or renewal is published. No trust center or security page carries the report, and interested customers are invited to get in touch for it.
Model Supply Chain Disclosure
Which models sit underneath, whose they are, where they run, and whether the vendor commits to telling customers when that changes. A legal buyer inherits every dependency it cannot see.
The architecture is described in detail, and the model layer is not identified. Agents are described as driven by language models, traversing decision trees encoded in the vendor's proprietary domain specific language, with proprietary prompt optimization and structured reasoning frameworks in LEAP. A buyer can see that the regulatory logic is the vendor's own and that a general purpose model executes against it. Searches of the platform pages, the resources section, the company blog and the trust center found no named model or model provider, no statement of where models run, no subprocessor list, and no commitment to notify customers when the model supply chain changes.
Under section 17 of the terms, Reggi runs on generative AI and a large language model, and Regology describes the platform as using the industry's most advanced AI and agents. Reggi is presented as a generative AI model built on top of Bloomberg Regology that draws on more than 16 million laws and regulations and tailors its answers to a company's business activities. No model, model provider, processing location or subprocessor is named, and no notice of supply chain changes is committed.
Commercial Transparency
Whether a buyer can learn what this costs without entering a sales process: published rates, the unit being charged, what sits behind an enterprise tier, and what implementation adds.
No pricing page was found, no rate is published, no unit of charge is stated and no tier structure appears. Every commercial path found ends in a request for a personalized demo. No free trial or self serve entry point was found, and no third party pricing figure either. The customer base is large regulated institutions.
Bloomberg Regology sells three plans. Reggi, the generative assistant, is free for individual use and covers summaries of regulatory material, questions on American laws and regulations and general compliance questions, with citations on every answer. Professional is $1,700 per user a month on a three year contract. It adds the Smart Law Library for federal law and all 50 states, regulatory change alerts with impact assessments, Reggi across the library and alerts, full text lookup across American content, and dashboards and reports. Enterprise is quoted on request and adds global jurisdictions, customizable topics, lookup across more than 16 million documents, group workflows, compliance, risk, task and policy management, controls testing and GRC integrations. No implementation fee is published.
Firm and Practice Coverage
Who the product is actually built for. AmLaw, midlaw, small firm and solo, in house departments, government and courts, and which practice areas are supported rather than merely claimed.
Coverage is described with substance, and the regulatory focus is named at the level of sources. Regulatory scope is stated through the regimes the Legal Engineers track and encode: SEC, FINRA and CFPB, with GDPR referenced in comparison material. The vendor states the approach extends to anything a company is subject to rather than being limited to those regimes. The buyer is the large regulated enterprise, with chief compliance officers named as the main role and financial services the focus of the named regimes and investors. Functional coverage is listed by workflow: review of regulated content across marketing, investor relations and internal communications, questionnaire and information request completion, contract review, and supervision of other AI systems. Not found: an industry list beyond financial services, segmentation by organization size, jurisdictional coverage outside the United States, and any statement of what the platform is not built for.
Coverage spans federal law, all fifty states and international jurisdictions in more than 135 countries, and the library is continuously updated. Professional stops at federal and state content from the United States, and global coverage comes with Enterprise. One platform serves every industry rather than splitting by sector, with dedicated pages for banking, crypto and digital assets, energy and utilities, gaming and sports betting, government and regulatory authorities, healthcare and life sciences, manufacturing and software. Buyers are compliance, legal and risk teams at organizations from startups to large corporations. No list of the international jurisdictions, no completeness or lag statement for each, and no statement of what the platform is not built for is published.
The 12 legal signals, side by side
Recorded rather than graded. These are the questions a practitioner has to answer before a tool touches a client matter, and the answers are taken from public material only.
Client Data in Training
Can material a lawyer puts into this product be used to train a model?
No material found states whether customer content may be used to train or improve models, either way. The vendor describes LEAP as creating a compounding layer of institutional judgment that captures firm specific standards and continuously improves how those standards are applied. That is a claim about learning from firm material, and nothing says whether the learning stays with the firm it came from. The trust center renders client side and was not read; it may say more. Searched the trust center, the platform pages, the resources section and the company blog on 29 Aug 2026.
Section 6.A of the terms, last updated 9 January 2025, gives Regology a license to use customer data only as necessary to provide the services, and the customer keeps every other right in it. Regology owns Service Data, defined as anonymized, aggregated, statistical or benchmark data generated from a customer's use, with personal data excluded, and the terms give it no stated purpose. Neither the terms nor the Responsible AI Policy mention model training, and no model provider is named.
Customers upload their own law libraries and record obligations, control mappings, policy decisions and rejections in the platform.
Prompt and Output Retention
How long does the product keep what a lawyer typed, and can that be set to zero?
No public material states how long submitted content, compliance determinations, explanations or decision history are kept, whether a customer controls the window, or whether deletion is available. The product centralizes decision history as a feature, so the record builds up by design. It includes determinations that content was non compliant before it was corrected. Searched the trust center, the platform pages, the resources section and the company site on 29 Aug 2026.
A customer can have its data returned or destroyed at any time. Section 11.E of the terms has Regology, on written request and subject to contrary legal obligations, return or destroy all customer confidential information at the customer's option and certify it in writing. Section 10 confines personal data to performing the services. No default retention period applies to Reggi prompts and answers during the term, and the Responsible AI Policy does not address retention.
Ethical Walls and Matter Segregation
Does retrieval respect the firm’s ethical walls, or can the model read across them?
No vendor material addresses segregation between customers, users or business units. A large financial institution operates information barriers between deal teams, research and trading as a regulatory requirement, and this platform reviews internal communications and investor materials across the firm. Nothing found states whether agents, determinations or decision history respect those barriers. Collaboration tooling is described in terms of teams tagging each other and centralizing decision history, and nothing states whether access is shared or separated. Searched the trust center, the platform pages, the resources section and the company site on 29 Aug 2026.
Work is assigned to named people, approvals and rejections are recorded, and Enterprise adds group workflows with reports at company, team and individual level. How access is separated between customers, between users or between business units with different regulatory perimeters is not described, and no document management integration supplies permissions to inherit.
Third Party Request and Subpoena Notice
If someone subpoenas the vendor for a firm’s data, does the firm hear about it first?
No published customer agreement or data processing agreement was found. No clause committing to notify a customer of a government or law enforcement request for its data was found, and no transparency report. This platform holds records of compliance determinations, including determinations that a regulated firm's content failed a rule before it was fixed. Nothing published addresses what the vendor would do on receiving a demand for them. Searched the trust center, the platform pages, the resources section and the company site on 29 Aug 2026.
Section 11.C of the terms commits Regology to give the customer prompt notice when legal process requests or requires disclosure of its confidential information, so the customer can seek a protective order first. If none is obtained, Regology discloses only what its counsel advises the law requires and works with the customer to limit the effect. Confidential information includes personal data and customer information understood to be confidential. No transparency report is published.
Primary Law Corpus Provenance
Where does the law in this product come from, and does the vendor have the right to use it?
Sources are named at the level of regulators and the maintenance process is described, but no license basis is stated. The source material is the body of regulation itself, and the vendor names where it comes from. Legal Engineers review regulatory updates from sources including the SEC, FINRA and CFPB and identify relevant changes for the agents, and regulatory logic is built and maintained inside LEAP as laws change.
The updating is done by staff rather than automated. No rights basis is stated, though United States federal regulations are in the public domain, so the license question largely does not arise. Also not found: a coverage list by jurisdiction, a completeness statement for any regime, or a stated lag between a rule change and the agent being updated.
The Smart Law Library is Regology's proprietary library of primary law from legislatures and agencies, covering federal law, all fifty states and international jurisdictions, with lookup across more than 16 million documents on the Enterprise plan. It tracks bills, laws, regulations and agency updates in real time and keeps version history for each law. No license or sourcing statement, completeness statement for each jurisdiction or list of the international jurisdictions is published.
Good Law Verification
Does the product tell you when the authority it just cited has been overruled?
No material was found addressing whether authority carries a treatment signal or whether subsequent history is checked, and no commercial citator license was found. This product's source material is regulation rather than case law, so a citator in the usual sense is outside its design. The closest equivalent for a regulatory product is whether the encoded rule is current. The vendor describes Legal Engineers reviewing regulatory updates and maintaining the decision trees as laws change.
That keeps its own rules current but is not a treatment signal on cited authority. Searched the platform pages, the resources section and the company blog on 29 Aug 2026.
The library holds legislation and regulation rather than case law, so currency here means whether a rule in the library is the rule in force. Real time tracking of bills, laws, regulations and agency updates, version history and links from proposed rules to their final versions keep it current. No treatment signal on cited authority, check of later history or citator license is part of the product.
Refusal and Uncertainty Behavior
What does the product do when the answer is not in the corpus?
No published material describes what an agent does when a determination is ambiguous or when the encoded rule does not reach the situation presented. No explicit path for declining to answer and no confidence signal shown to the user was found. Traversal of the decision tree is designed to end in a compliance determination, so what happens at a node the tree does not anticipate is an open design question. The published human review step, where a professional completes the determination, is the stage where a person sees the result, and nothing states that the agent flags uncertainty rather than resolving it. Searched the platform pages, the resources section, the company blog and the trust center on 29 Aug 2026.
The agents decide whether a law applies and map each change to obligations, and no path is described for an uncertain applicability call or a change that fits no existing obligation. No confidence signal or no answer path reaches the user. Rejections are logged, which records a person's disagreement rather than the system flagging doubt, and section 17 of the terms tells customers to verify every Reggi answer.
Fabricated Citation Record
Does a public court record exist addressing fabricated or hallucinated legal citations in output from this product?
No court order, opinion or disciplinary record naming this product was found as of 29 Aug 2026. The search used the AI Hallucination Cases database maintained by Damien Charlotin, which tracks court decisions worldwide involving hallucinated content generated by AI and records the tool implicated where known. Published 2026 sanctions summaries and secondary sanctions trackers were also checked. The entries found name filers, and in some rows other products, rather than this one. This is a statement about the public record on the date shown, not a clearance.
The AI Hallucination Cases database maintained by Damien Charlotin, which records court decisions worldwide that address hallucinated AI content and the tool involved where known, has no entry naming Regology or its Reggi assistant. Regology's agents work out which regulations apply to a business and map them to obligations and controls for corporate compliance teams.
Bar Guidance Alignment
Has the vendor engaged in public with the ethics opinions its buyers are bound by?
No engagement with any named ethics opinion or bar guidance was found, including ABA Formal Opinion 512 and state bar guidance. This company launched a law firm in 2026, describing it as the first fully AI native law firm for institutional clients and appointing a former Sidley Austin chairman to lead it. A law firm is directly bound by professional conduct rules, so the company has an entity subject to the guidance, and nothing found sets out its position.
The company also convenes a forum on AI, law and policy involving legislators and regulators. That forum engages with policy formation rather than with the conduct rules that bind its users. Searched the platform pages, the resources section, the company blog and the news announcements on 29 Aug 2026.
No Regology material engages ABA Formal Opinion 512, a state bar opinion or other professional responsibility guidance. Its research includes an annual state of regulatory compliance survey on practitioner sentiment and compliance operations, written for compliance, legal and risk teams.
Billing and Fee Posture
Does the vendor address what happens to the bill when the work takes an hour instead of six?
No record of AI assisted work kept per matter for fee purposes was found, and no guidance on billing, fees or disclosure to clients. No quantified time or cost savings claim was found either. The vendor's published framing is coverage and speed of compliance analysis rather than hours displaced. Norm Law, the related law firm, bills institutional clients directly, and no material found addresses how work done with AI is treated in those fees. Searched the platform pages, the solutions pages, the resources section and the company blog on 29 Aug 2026.
Regology sells to corporate compliance, legal and risk teams. Its AI and agents are pitched as automating the repetitive, labor intensive manual work that swamps compliance teams, and the regional bank case study reports faster assessment of regulatory changes without a figure. No record of AI assisted work by matter is kept for fee purposes, and no billing or client disclosure guidance is offered.
Outside Counsel Guideline Readiness
Can a firm get this vendor through a client’s AI clause without a bespoke negotiation?
A trust center exists at a stable URL on a recognized compliance platform, stating SOC 2 compliance and continuous monitoring. It is a self serve route to whatever diligence material sits behind it. Searches of the trust center entry point, the platform pages and the company site on 29 Aug 2026 found no subprocessor list, no statement naming which model providers see customer content, no published data processing agreement, and no consent or notification material for clients.
The portal renders its contents client side and they were not read, so what a requester would receive is not known and the portal may say more.
No subprocessor list, data processing agreement, statement of which model providers see customer content, or client consent and notice material is published. SOC 2 Type II comes with its auditor and scope in earlier announcements, and customers ask Regology directly for the report.
Court Disclosure Support
If a judge’s standing order requires an AI disclosure, can the product produce one?
Much of a defensibility record exists and is central to the product. Every determination carries an explanation identifying what is problematic and under which regulation. The decision tree encoding each rule is readable by a person, so a lawyer can inspect the reasoning path without reading code. Decision history is centralized, with human review recorded through the collaboration tooling. A regulated firm asked by a supervisor to justify why content was cleared would have the rule applied, the path taken, the explanation given and the person who signed off.
Two elements are missing. No single export per document combining these with the model used was found, and no model is named in published material. The forum here is a regulatory examination rather than a court.
Regology keeps a defensibility record built for auditors. Every alert, assignment, decision, approval, rejection and policy update is timestamped and tied to the source law, covering what was decided, by whom, when, on what basis and what was rejected. No export for each document joins that trail to the model used, and no model is named. The record is shaped for a regulatory examination or internal audit rather than a court.
The questions both sides leave open
Derived from the records above rather than written, so it cannot favor either vendor. Take these into both conversations and ask each side the same question.
- Ethical Walls and Matter Segregation
- Good Law Verification
- Refusal and Uncertainty Behavior
- Bar Guidance Alignment
Which one fits
Choose Norm Ai if
- You have to show a supervisor why content was cleared. Norm Ai encodes regulations through a proprietary domain specific language into decision trees a human can read, an agent traverses the tree rather than answering from model memory, and every determination carries an explanation naming what is problematic and under which provision.
- You want the division of labor stated rather than implied. Norm Ai publishes an operating model in which an agent conducts the initial compliance review of material such as marketing communications against SEC or FINRA rules and a human professional then reviews the findings and completes the determination, with Legal Engineers building and supervising the agents.
- The advice line matters to your risk committee. Norm Ai answers it structurally rather than with a disclaimer, having launched Norm Law in 2026 as a separate law firm for institutional clients chaired by a former Sidley Austin chairman, so admitted attorneys deliver legal advice while the software delivers analysis.
Choose Regology if
- You need the auditor named in a diligence response. Regology states SOC 2 Type II certification, and its announcements identify BARR Advisory as the auditing firm, with the examination scope stated as covering security, confidentiality and availability.
- You are not replacing your GRC stack. Regology positions itself as a regulatory intelligence and data layer sitting alongside existing investments, naming ServiceNow, Archer and Hyperproof specifically, and lets customers import existing law libraries from spreadsheets and PDFs rather than rebuilding from scratch.
- Your auditor will ask what you rejected, not just what you approved. Regology timestamps every alert, assignment, decision, approval, rejection and policy update and traces each to the source law, across United States federal law, all fifty states and international jurisdictions.
In summary
Norm Ai
Norm Ai converts laws, regulations and corporate policies into executable compliance agents for large regulated enterprises, with a concentration in financial services covering SEC, FINRA and CFPB rules. According to the AI Legal Index, its case rests on how a determination is reached. Regulations are encoded through a proprietary domain specific language into decision trees a human can read and a machine can execute. An agent traverses the tree and states which provision drove its determination. Agents are built by Legal Engineers, former attorneys given intensive language model training, on a platform whose deployment path includes automated evaluation pipelines. Norm Ai publishes no customer agreement and no pricing, and names no model, provider or retention position.
Regology
Regology is a regulatory intelligence platform for compliance, legal and risk teams. It is built on a proprietary Smart Law Library covering United States federal law, all fifty states and international jurisdictions, with three named agents covering regulatory change, compliance and research. The AI Legal Index records published Terms of Service that give a defense and indemnity for third party intellectual property claims and cap each party at the fees paid in the twelve months before a claim. Section 17 states plainly that Reggi AI responses cannot be guaranteed for accuracy and must be independently verified. Regology states SOC 2 Type II certification and names BARR Advisory as the auditing firm, with the examination scope given. Every alert, assignment, decision, approval, rejection and policy update is timestamped and traceable to the source law, which the vendor frames as defensible documentation for auditors. It names no model, provider or hosting region. Reggi is free for individual use, and Professional costs $1,700 per user a month on a three year contract.
Questions buyers ask
Norm Ai vs Regology: which is better for regulatory compliance?
They come at the problem from different directions, so the choice depends on what you need to show. Norm Ai publishes more about how a determination is reached and reviewed, through decision trees that an agent traverses and a human can read. Regology publishes more of what a procurement review asks for, naming its auditor and the systems it sits alongside. Regology's terms carry a liability cap and an IP indemnity besides, and Norm Ai publishes no customer agreement. Neither states a position on training. From the AI Legal Index, based on each vendor's own published materials as of September 14, 2026. No vendor pays for placement.
How does Norm Ai ground its compliance determinations?
By construction rather than by retrieval. Norm Ai encodes regulations through its own domain specific language into decision trees that a human can read and a machine can execute, so an agent traverses the encoded rule and reaches a determination rather than generating one from model memory, and every determination carries an explanation identifying the provision behind it. No accuracy figure, error rate or test set is published, so the grounding mechanism is documented and its performance is not. From the AI Legal Index, based on each vendor's own published materials as of September 14, 2026. No vendor pays for placement.
Does Regology replace an existing GRC system?
It says it does not. Regology positions itself as a regulatory intelligence and data layer beside existing governance, risk and compliance investments and states that no rip and replace is required. Its integrations sync authority documents, citations and regulatory content into ServiceNow, Archer, Diligent, Hyperproof and other GRC platforms, and an open API takes REST and JSON, XML, CSV or SFTP flat files for a homegrown system. Customers can import existing law libraries from spreadsheets and PDFs. What an administrator sets up for each connection is not described. From the AI Legal Index, based on each vendor's own published materials as of September 14, 2026. No vendor pays for placement.
Does either vendor name the auditor behind its security certification?
Regology does. It states SOC 2 Type II certification and names BARR Advisory as the auditing firm, with the examination scope stated. Norm Ai states SOC 2 compliance on a trust center hosted on a recognized compliance platform without saying whether it is Type 1 or Type 2, and names no auditor, coverage period or report date. Its trust center renders client side, so any further detail it holds is not reflected here. From the AI Legal Index, based on each vendor's own published materials as of September 14, 2026. No vendor pays for placement.
What do Norm Ai and Regology both leave unpublished?
Norm Ai publishes no customer agreement, so it states no indemnity, liability cap, warranty on output or insurance position, which matters because the product tells a regulated firm what its obligations are. Regology's Terms of Service do state an indemnity and a liability cap. Neither names a model or a provider. Neither states a retention period or a position on training, though Regology's terms let a customer have its information returned or destroyed on request. Norm Ai publishes no price. And neither describes what happens when applicability is uncertain. From the AI Legal Index, based on each vendor's own published materials as of September 14, 2026. No vendor pays for placement.
Neither vendor paid for inclusion, placement or a grade, and neither reviewed this page before it published. Everything on it comes from public material on the dates shown. How the index grades.
Two limits belong on this page. Norm Ai also runs a trust center at a stable URL, which may answer some of these questions. Its security certification and data stewardship details may be fuller than shown here. Regology's announcements naming BARR Advisory and the examination scope come from earlier certification cycles, and no current report date or coverage period is published, so the certification's currency rests on a standing claim. Neither vendor states whether customer content trains its models. Neither vendor reviewed this page.