Haast vs Norm Ai: how they compare in 2026
Haast starts with the lifecycle of regulated communications. It checks drafts against configured requirements, routes unresolved issues to compliance and monitors published assets across company and partner channels. The rule specific controls identified by the AI Legal Index let an enterprise vary its tolerance across claims, products and markets. Authoring integrations then bring those checks into the place where content is revised. Norm Ai uses legal engineering to encode institutional standards into agents for several legal and compliance workflows. Alongside regulated content review, its questionnaire product drafts responses from approved answers, policies and earlier submissions, then routes them through staged review. The AI Legal Index identifies cited responses and export back to the original questionnaire format as practical features of that process. Haast's communication monitoring and Norm's reuse of approved institutional answers address different recurring workloads. Each still relies on customer standards and human decisions about the work it produces.
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.
Haast applies AI agents to marketing and communications compliance before publication and throughout the life of published content. Each agent handles a particular compliance task using regulations, internal policies and earlier review decisions. The customer sets a risk tolerance for individual rules, and reviewer feedback changes later analysis. Findings identify suspect content, explain the applied requirement and suggest a correction. Continuous monitoring extends the same work to websites, social channels and authorized partner material. Approval routing and audit history support that analysis. Removing the models would remove the automated interpretation and review service that Haast sells to enterprise compliance teams.
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.
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.
Findings trace back to configured rules and explain the text or claim that triggered a concern. Haast separates checks into dedicated agents and adjusts analysis through reviewer feedback. A September 2026 comparison claims 98.7% accuracy across more than 400,000 compliance checks, but gives no reproducible test set, error breakdown or evaluation protocol. Configured company standards can be inspected, yet an openable primary authority link for each legal assertion is not specified. Nor is there a described mechanism for checking subsequent legal treatment. The product provides reasoning about rules, without a public evaluation that establishes reliable citation accuracy across those rules.
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.
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.
Content creators can correct flagged wording, repeat a review and submit unresolved findings to compliance. Reviewers see the analysis, leave comments and approve or reject material within the existing approval process. Risk tolerance can vary by agent, product, region or campaign, including a strict setting for claim language. In Google Docs, an unresolved submission generates an email link for the reviewer to open the same sidebar. These are concrete review and escalation surfaces. The public control description does not specify an automatic stop for unsupported legal reasoning, a mandatory reviewer threshold or a recovery procedure after an incorrect approval reaches publication.
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.
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.
Equity Trustees uses Haast to precheck regulated materials, newsletters and fact sheets and to monitor fund manager and superannuation marketing online. Its November 2024 account describes more than 80 custom compliance requirements and monitoring across thousands of resources. Those details identify an operating workflow and the scale of its rule configuration. Wessel van Keulen also describes using Haast during regulatory change work at Zurich. Figures such as faster review and reduced manual effort accompany the broader product offering, but a dated baseline, comparable sample and measurement method are not supplied for the Equity Trustees deployment. Its account establishes use without a controlled outcome study.
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.
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.
Customer content is limited to running compliance reviews and improving accuracy within that customer's account. Haast expressly excludes training shared or third party models in its product policy. Google integration data has a further purpose restriction and passes to infrastructure providers only as necessary for enabled functionality. Encryption and authenticated access protect stored and transmitted information. The public usage agreement does not reproduce an express training prohibition, and attorney client privilege and work product treatment are not specifically addressed. Granular administrative controls are available, but matter level segregation and the retention terms of individual model providers remain outside the public confidentiality description.
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.
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.
The intended users include enterprise lawyers, compliance staff and marketers. Marketing teams receive an initial review, while compliance reviewers resolve findings and make publication decisions. Haast's implementation staff translate customer policies into automated checks, and the platform is described as an AI legal reviewer. A separate statement defining the boundary between software output and legal advice is not supplied in the current usage policy or product materials. Nor are lawyer competence duties, consumer disclosures or jurisdictional limits on legal services set out. The authorized content restriction governs what customers may monitor; it does not explain professional responsibility for an erroneous legal interpretation.
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.
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.
Haast emphasizes explainable decisions, customer control of rule settings and ongoing feedback from legal reviewers. Each task has its own agent, and a finding exposes the rule applied and the reason for the flag. Those features support a customer's compliance governance, while legal engineers help encode the customer's standards during implementation. A separate vendor governance framework does not name an accountable AI officer, describe release approval testing or report uneven performance across industries, languages or protected groups. The aggregate accuracy claim also lacks a published evaluation protocol. The available commitments concern transparent operation and customization rather than a defined vendor assurance program.
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.
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.
The March 2026 privacy policy covers use of the compliance platform as well as the website. It specifies encryption, multifactor authentication for sensitive systems, intrusion detection, vulnerability assessments and an incident response plan with legally required breach notice. Information is retained while a legitimate purpose continues, then deleted or anonymized; backups may remain securely stored when deletion is not possible. Individuals can request access, correction or deletion by email. Google data has a separate restriction to enabled functionality and equivalent protections for infrastructure providers. No current list of subprocessors, prompt specific retention schedule or fixed backup deletion deadline accompanies these controls.
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.
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.
The public terms page contains a usage policy covering authorized users, lawful activity, prohibited content and restrictions on competitor monitoring. Haast may throttle or suspend access for violations and can modify that policy without advance notice. The policy does not allocate financial loss from an incorrect compliance finding or an approval that misses a regulatory issue. No output warranty, vendor indemnity, liability cap or contractual correction remedy is stated there. Customer support and reviewer feedback provide operational assistance, but their availability does not establish compensation rights. Any additional commercial allocation of those risks remains outside the publicly available 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.
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.
The Google Docs add on has a published installation and review sequence. A customer installs it from the Workspace Marketplace, grants permissions and signs in with Haast credentials or single sign on. Content is analyzed from a sidebar, with findings linked to the relevant passage. Authors edit in the original file and run another review; unresolved issues are submitted to compliance, whose reviewer receives an email link to that file and sees the same findings and comments. Haast retains the audit history. This describes configuration and movement of content and review work, beyond the separate lists naming Word, Figma and Workfront connections.
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.
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.
Haast is delivered as software as a service, with account login, browser access and connections inside content creation tools. Its Google Docs integration authorizes access to the specific content needed for the requested functionality. Customer rules can vary across brands, teams and geographic markets within a deployment. The published delivery description does not identify hosting regions, distinguish storage location from AI processing location or explain whether customers share underlying infrastructure. International regulatory coverage concerns the rules being applied, rather than where information is processed. Private cloud, installation on customer infrastructure and region selection are not specified as purchasing options.
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.
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.
The current privacy policy identifies ISO 27001 and SOC 2 Type II certifications and describes independent audits. Platform materials repeat ISO 27001 certification and SOC 2 compliance. The accompanying controls include encryption, authentication, vulnerability review and incident handling, while procurement teams can request security documentation through the RFP process. The public materials do not provide the audit period, certification scope, issuing assessor or an immediately accessible report. A reader can identify the standards Haast claims, but cannot establish their coverage of individual AI services from those statements alone. No specific model provider assurance is incorporated into that certification description.
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.
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 processing architecture assigns an individual agent to each compliance task instead of using one general agent for every rule. Customer policies, past decisions and risk settings shape those agents, and feedback from reviewers adjusts later work. This explains how analysis is organized and customized. It does not identify the underlying model families, their providers, versions or inference locations. Google APIs supply an integration channel, not an identification of the models performing compliance analysis. Shared model training is excluded by policy, but provider retention terms and a commitment to notify customers about model substitutions are not included in the public architecture description.
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.
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.
Haast charges according to usage rather than the number of individual users. Its enterprise pricing description permits the broader team to use the service without a separate subscription for every person. Rates and the exact metered unit are not published, and no named package table sets out included volumes, overage prices or minimum commitments. Implementation is tailored to users, rules, content and integrations with legal and technical staff involved. The associated implementation charge is not stated. The commercial structure is therefore a negotiated usage subscription with an identified basis, while the amount and the boundary of included deployment services remain private.
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.
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.
The product serves enterprise legal, compliance and marketing departments reviewing regulated communications. Financial services coverage includes FINRA, SEC, FCA and consumer protection requirements; separate industry workflows address retail, telecommunications, pharmaceuticals, gaming and other regulated businesses. Teams can apply different rule sets across products, regions, campaigns and brands. Supported material includes text, images, presentations, audio and video, with monitoring of websites and authorized partner channels after publication. The published scope explains these supported compliance tasks but does not offer a definitive jurisdiction exclusion list or limits for unusual content. It is an enterprise review platform rather than a general case management service for law firms.
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.
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?
Haast's platform policy excludes use of customer content to train shared or third party AI models. Content can support compliance reviews and improve accuracy inside the customer's own account, and reviewer feedback teaches that account's rules. Google user data has a separate restriction to enabled features. The published usage agreement does not contain a matching express training prohibition. This commitment therefore concerns shared training at the policy level; it does not prohibit all adaptation of a customer's dedicated review configuration or describe every provider's retention arrangements.
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.
Prompt and Output Retention
How long does the product keep what a lawyer typed, and can that be set to zero?
Personal information stays for as long as a legitimate service or legal purpose requires it. Haast then deletes or anonymizes it, while information in backups can remain securely stored if deletion is not feasible. Google content is retained only as necessary for the requested integration. No fixed active retention period for prompts, findings or uploaded materials is stated, and no administrative retention selector is described.
Email requests support individual access and deletion rights, subject to applicable law, rather than an automatic schedule for every compliance asset.
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.
Ethical Walls and Matter Segregation
Does retrieval respect the firm’s ethical walls, or can the model read across them?
Administrative access controls and organization scoped sharing are part of Haast's service. Its privacy policy says contributions are typically visible to other users inside the same organization, and the homepage identifies granular user administration and single sign on. These measures do not describe retrieval authorization for each user, segregation between client matters or inheritance of permissions from a source document system.
The Google integration requires authorization, but its public instructions do not explain how later access changes propagate to stored analysis. Detailed ethical wall enforcement remains unspecified.
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.
Third Party Request and Subpoena Notice
If someone subpoenas the vendor for a firm’s data, does the firm hear about it first?
The privacy policy permits processing to meet legal obligations and sets out sharing with service providers, transaction counterparties and other authorized organizational users. It does not describe a process for responding to subpoenas for customer content, or promise notice to the customer before compelled production. The public usage agreement supplies no additional notice procedure. An incident response commitment concerns a data breach, which is a different event from lawful compulsory disclosure. No transparency report or published process for contesting an overbroad demand is identified.
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.
Primary Law Corpus Provenance
Where does the law in this product come from, and does the vendor have the right to use it?
Haast names regulatory frameworks including FINRA, SEC, FTC and FCA requirements and applies them alongside customer policies and prior decisions. Its horizon scanning feature tracks changes across the United States, Europe and Asia Pacific, with daily monitoring described for regulatory updates. The product does not identify the publishing or licensing basis for the underlying regulatory texts, or a complete list of source feeds and their individual update delays.
Customer rule configuration supplies additional internal material, but does not establish rights in every external authority used during analysis.
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.
Good Law Verification
Does the product tell you when the authority it just cited has been overruled?
Regulatory horizon scanning alerts teams to changed obligations and connects those changes with policies and content reviews. That is a regulatory monitoring function, rather than a described determination of the subsequent history of an opinion cited in an answer. Haast does not identify a commercial citator or its own method for flagging overruled or limited judicial authority. Traceability to a configured rule explains why a marketing item was flagged, but does not demonstrate that any case supporting that rule remains legally valid.
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.
Refusal and Uncertainty Behavior
What does the product do when the answer is not in the corpus?
Findings include an explanation and reference to the configured rule, and users can adjust risk tolerance or return a decision for further compliance review. Haast does not describe a specific refusal response when no supporting rule exists, a no answer state for an unsupported legal assertion or a threshold that suppresses uncertain analysis. A strict setting for claim language controls the customer's compliance standard, rather than evidencing an abstention mechanism.
Human review remains available, but the public workflow does not show what the model returns when relevant support is missing.
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.
Fabricated Citation Record
Does a public court record exist addressing fabricated or hallucinated legal citations in output from this product?
Damien Charlotin's AI Hallucination Cases database contains no Haast entry concerning fabricated legal citations as of October 16, 2026. Haast primarily reviews enterprise communications and monitors published content, rather than preparing briefs for filing. That operating context does not establish that every regulatory assertion is accurate or that unsupported references cannot occur. The absence of a matching court finding concerns fabricated citations specifically.
It does not establish the quality of the review engine, validate its aggregate accuracy claim or determine the outcome of any customer's compliance program.
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.
Bar Guidance Alignment
Has the vendor engaged in public with the ethics opinions its buyers are bound by?
Legal and compliance professionals configure Haast's rules, review findings and approve content, but the public materials do not connect those practices to a named bar ethics opinion. There is no product mapping to lawyer competence, client consent, confidentiality or supervision guidance across jurisdictions. References to FINRA, FCA and other advertising requirements concern the advertising rules applied during content review.
They do not establish engagement with professional conduct rules for lawyers using AI. The available implementation assistance focuses on encoding company standards and maintaining the organization's review process.
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.
Billing and Fee Posture
Does the vendor address what happens to the bill when the work takes an hour instead of six?
Haast is sold to enterprise marketing, legal and compliance teams reviewing their organization's communications. Its promised reductions in manual review and faster approvals concern internal operating effort, rather than a charge from outside counsel to a client. The usage subscription is the enterprise's software cost. Approval histories can show work performed, but the product is not described as a legal fee authoring or benchmarking service.
No conversion of saved review hours into a client invoice is part of the stated workflow, and no law firm fee guidance accompanies it.
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.
Outside Counsel Guideline Readiness
Can a firm get this vendor through a client’s AI clause without a bespoke negotiation?
Procurement teams can request security documentation and responses to an RFP alongside a tailored demonstration. Public privacy and security statements also describe encryption, authentication and restrictions on shared model training. The promised procurement material is accessed through Haast's sales process rather than supplied as a published client disclosure pack. A current model provider list, subprocessor inventory and standard explanation for outside counsel to forward to a client are not openly available. The RFP offer establishes a route to request material without specifying every artifact that route will provide.
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.
Court Disclosure Support
If a judge’s standing order requires an AI disclosure, can the product produce one?
Haast keeps a history of AI findings, reviewer comments and compliance approvals. The Google Docs workflow links the analysis to the content under review, and the audit trail remains within Haast as teams edit and resolve concerns. For this compliance product, that history is relevant to an examiner or regulator asking how an asset was approved. A complete export that identifies the model version, retrieved authorities and human verification for each item is not specified. Existing review records supply part of that account without a published disclosure package.
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.
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.
- AI Liability and Recourse
- Third Party Request and Subpoena Notice
- Good Law Verification
- Refusal and Uncertainty Behavior
- Bar Guidance Alignment
Which one fits
Choose Haast if
- The main workload spans draft marketing assets and continuing checks of live content.
- Different rules need separate risk tolerances across brands, products and markets.
- Marketing and compliance teams need an authoring sidebar for findings, corrections and reviewer comments.
Choose Norm Ai if
- Questionnaires should reuse approved answers, policies and prior submissions with source references.
- Responses need staged business, legal and management review before export.
- The organization wants agents encoded from institutional legal and compliance standards across several workflows.
In summary
Haast
Haast connects configurable content checks with approval workflows and monitoring of published communications.
Norm Ai
Norm Ai applies agents built by legal engineers to compliance work, including content review and sourced questionnaire completion.
Questions buyers ask
What supports a Norm questionnaire answer?
Norm Ai describes an Answer Bank, firm approved materials and prior questionnaires. Reviewers can jump to supporting excerpts, inspect citations and resolve responses marked for action. From the AI Legal Index, based on each vendor's own published materials as of October 16, 2026. No vendor pays for placement.
Does Haast continue checking published material?
Yes. Its monitoring covers live websites, social channels and authorized partner content, with alerts when content or regulatory requirements change. From the AI Legal Index, based on each vendor's own published materials as of October 16, 2026. No vendor pays for placement.
Is Norm Ai itself the affiliated law firm?
No. Norm Ai identifies itself as a technology and service provider. Norm Law, LLP is an independent law firm that supplies legal services through its attorneys. From the AI Legal Index, based on each vendor's own published materials as of October 16, 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.
The software comparison concerns Haast and Norm Ai. Norm Law is a separate legal services provider affiliated with Norm Ai.