Lawdify vs Merlin Search Technologies: how they compare in 2026
Merlin's Alchemy is designed to turn a large evidence collection into sourced answers and reports. Multiple retrieval and analysis engines work together, and ReviewPartner can support document review either within Alchemy or as a managed service. The AI Legal Index highlights the combination of question answering and specialized services for transcripts, medical records and scanned files as a way to address several kinds of evidence work. Lawdify organizes dispute material into Case Brain and uses that context for Lawri, customized skills and drafting. The AI Legal Index highlights its connections to Word, Outlook and Excel for teams that need the same case knowledge while reading correspondence, evaluating spreadsheets and preparing submissions. Both go beyond conventional retrieval. Merlin centers on discovery and document intelligence, with isolated sites and utility based hosting; Lawdify centers on an evolving dispute workspace that can be used through an assistant and familiar office applications.
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.
Lawdify is built around AI interpretation of evidence and the work that follows from it. Case Brain organizes parties, dates and issues from uploaded material, while Lawri uses that context to answer questions and prepare drafts. Expertise Engineering turns the team's own methods into reusable skills, and Smart Tables run specialized tasks as the dispute develops. Email instructions can start several agents and return their results to the sender. These functions depend on models understanding and generating content, rather than adding a conversational feature to an older records system. File storage and exports support the service, but the case intelligence is the product being sold.
The models are the engine of a core capability layered on a product that would still function without them. Alchemy's answering and analysis engines and ReviewPartner's AI reviewers sit on a full ediscovery platform with keyword and algorithmic search, review, tagging, production and multi-party workspaces that the company has sold since 2019 under the Sherlock and DiscoveryPartner names; the vendor's own framing is three engines to find and two to analyze and answer. Remove the answering models and a search-and-review platform remains.
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.
Evidence Check is designed to test whether a proposition has supporting material in the case file. Lawri can inspect case context, and the research interface presents authorities with relevance and application descriptions. The legal product also offers factual checking and argument strength assessment. Its visible examples do not establish a reproducible accuracy test, a citation status check or a consistent route from every generated assertion to an open source passage. The terms describe output as probabilistic and place evaluation with the user. These are useful verification intentions and review functions, with the detailed grounding method and measured failure behavior still unspecified.
Grounding is real and documented with sourced outputs, short of a testable accuracy figure on the surfaces read. The home page states that questions return comprehensive, sourced answers and reports across the document set rather than search hits, the launch coverage describes a dual-window architecture separating analysis prompts from formatting prompts to keep formatting from interfering with document analysis, and the AI services page states that every service undergoes rigorous validation for accuracy, completeness and consistency at scale. No accuracy figure, test set or evaluation is published in the material read; the founder's published book on generative AI for discovery was not opened. Questions about primary authority do not apply to a tool that reads the customer's own documents.
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.
The legal workflow gives the lawyer a first draft and a visible editing surface. In Word, proposed revisions appear with track changes so the user can review and accept individual edits. The terms also require evaluating probabilistic output for the intended use, including expert human review. Email agents can process attachments and reply with results, while specialized skills automate steps in case preparation. The distinction between a draft and professional judgment is therefore supported by an actual review mechanism. A threshold that stops an agent, a mandatory approval before consequential action and a recovery route after an incorrect result are not specified.
Review surfaces and constraints are published, short of the full control structure. Granular role-based access control exposes every function as an assignable permission, audit logs track each person's views, tags and searches, multi-party sites give user groups private tag sets and shared searches, and the platform delivers sourced reports a lawyer works from; ReviewPartner's AI reviewers are described as applying consistent judgment across files with validation. What is not published is the threshold at which AI review decisions stand without human sampling, what executes without approval, or a stated route back after a wrong classification.
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.
Lawdify identifies deployed use as well as product demonstrations. Its September 2026 account describes Zurich Insurance Malaysia moving from a three month proof of concept into a multiyear deployment. That is a named operational relationship in the insurance business, rather than a measured result for every legal workflow. The legal service also presents customer experience around evidence organization and case preparation. The broader claims about faster settlements, reduced loss ratios and fraud detection are not accompanied by a method tying each figure to a named deployment. The available customer evidence supports practical use without establishing a reproducible estimate of legal team productivity.
A named customer without figures, and figures without a named customer. Dara Tarkowski, managing partner of Actuate Law, is quoted by name on the 2023 cloud utility pricing launch about adopting the pricing model, with no measured outcome; the home page states most clients save fifty to sixty percent on hosting costs, unattributed, and the TransPerfect partnership announcement states Reef ECA reduces document sets by over ninety percent on average, which is the partner's figure. US Legal Support and TransPerfect are named partners rather than customers. Nothing joins a named customer to a figure.
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.
The February 2026 terms expressly exclude customer documents, legal files and case materials from model training and product improvement. They distinguish that material from anonymized, aggregated operational usage metrics. Customer information is subject to confidentiality obligations, and the hosting provisions describe logical separation between customer environments. Deletion after termination includes backups and a certificate with logs. These are explicit commitments for material entrusted to the service. The confidentiality section also uses pilot terminology, while specific treatment of legal privilege, internal matter walls and retention by individual model providers remains unclear. The promise covers more than a general assurance that information is secure.
Published commitments on separation and work product are substantial, short of a training statement and an agreement. Each client has its own AWS account with no shared database or infrastructure. Each matter has its own servers, database, storage, URL and login. Sites shared by several parties give each group private fields, tag sets and searches, which the security page describes as protecting each group's work product, so work product is addressed in writing. Confidentiality controls include document lockdown with time limited authorization tokens, encryption in transit and at rest, and audit logging. Not found: any statement on training, since no customer agreement is published and the privacy policy was not opened; any retention or deletion commitment; and the identity of the models the platform runs.
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.
Lawdify's legal workspace is aimed at law firms and internal legal teams handling contentious work. Its terms distinguish the application from legal advice and say that it does not create a lawyer and client relationship. The AI output provision describes generated content as probabilistic and calls for evaluation appropriate to the use case, including expert human review. Word editing and evidence checks support that professional review in the product. The legal service also reaches construction claims teams and other specialists. A jurisdiction by jurisdiction account of permitted professional use, competence obligations and limits for people who are not lawyers is not provided.
No advice line or supervision statement is published. The product is sold to law firms, corporate legal departments and investigators and its AI reviewers are described as applying senior attorney-level judgment, which is a capability claim rather than a position on where the lawyer's responsibility sits; no surface read states that outputs are not legal advice or how the product supports a supervising lawyer's duties, and no customer agreement exists on the site to carry such a statement.
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.
Lawdify identifies an AI Governance Framework and a prompt injection testing report in its trust center. Access to those materials requires a request, so their detailed controls and results are not available in the open view. The privacy policy recognizes a right to human review of certain automated decisions where applicable, and the service makes commitments about customer data use and human evaluation of output. These establish elements of a responsible AI position. They do not reveal who owns model governance, what release testing covers or whether uneven performance across languages and legal settings has been measured. The restricted framework's contents remain unknown.
Security governance is published and AI governance is not. The security page names a chief information security officer reporting to the chief executive and a Technology Governance Committee chaired jointly by the CISO and COO providing executive oversight, and the AI services page states rigorous validation of every service; security governance is a different subject from AI governance, and no responsible AI framework, ISO 42001 or equivalent, testing results or statement about uneven output across document types or languages is published.
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 service combines encryption in storage and transit with authentication, access logging and logical separation of customer environments. Its terms set deletion procedures for removed material and terminated accounts, including a deletion certificate and associated logs after service exit. A trust center names cloud and supporting service providers, while the privacy policy describes security monitoring, backups and incident response procedures. These provisions address how client material is handled beyond ordinary website privacy. The incident section does not set a customer notification timetable or describe the response workflow in detail, and the complete path through underlying AI providers is not mapped for each processing operation.
Substantive published policy covering most of the ground, with retention and notification not located. Access control: granular role-based access control, document lockdown by time-limited token, audit logs of every view, tag and search, private subnets with no public IP addresses, TLS and AES-256 encryption. Hosting and sub-processors: Amazon Web Services named as the infrastructure provider, with per-client accounts and regions chosen by the client; AI model providers are not named. Incident practice: the SOC 2 Type II scope covers monitoring and incident response, and the single-tenant design is described as limiting the blast radius of any incident to one environment; no customer notification commitment is published. Not located: a retention period or deletion commitment, since no customer agreement is published and the privacy policy was not opened.
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 terms exclude warranties of accurate or reliable output and place professional evaluation with the user. They limit consequential losses and require the customer to defend Lawdify against several categories of claim arising from use or breach. The liability limitation contains exceptions for matters including fraud, intellectual property and confidentiality obligations, but its cap is internally inconsistent: the written amount corresponds to $100 while the parenthetical figure is $10. A specific promise to defend the customer against an AI output claim is not included. The result is a conventional allocation of risk away from the supplier, with an unresolved ambiguity in the stated monetary limit.
No liability position is published. The site's page inventory, taken from the navigation and footer on 6 September 2026, carries a privacy policy, a GDPR disclosure and a contact page and no terms of service or customer agreement; the security page describes controls and invites contact for security requirements without any warranty, indemnity, cap or insurance statement. This is an absence on the vendor's surfaces rather than a retrieval limit.
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.
Lawdify connects its case information to Microsoft Word, Outlook and Excel. The Excel application has a concrete setup path: sign in to a Lawdify account using email verification, select a case or matter, then use the chat panel or custom functions. LAWDIFY.ASK returns an answer in a cell, while LAWDIFY.EXTRACT pulls information from case material into the spreadsheet. The application can read and change the workbook and send information over the internet. A Lawdify subscription is required. This describes both the movement of case information into an existing work surface and the configuration needed to use it, beyond a list of compatible applications.
One integration is named with its function and no documentation an implementer could use was read. The April 2026 partnership release states that TransPerfect's Reef ECA is deployed as the processing engine across all Alchemy cloud instances, giving a unified workflow from ingestion through review; the security page offers hosting in a client's own AWS environment. No document management, review-platform or load-file integration is described with what moves and in which direction, and no integration documentation is published.
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.
Customer data is hosted in Singapore by default, with other jurisdictions available by arrangement where the cloud infrastructure supports them. The security offering describes regional support across Asia, the European Union and the United States. The terms also describe logical separation of customer environments, and the current certification scope identifies the legal platform on Azure and Google Cloud Platform. These details make the default hosting location and the existence of regional alternatives concrete. They do not provide a complete list of selectable processing regions, explain every AI inference location or map storage and processing differences to subscription tiers. Residency therefore remains partly specified.
Deployment options are published with tenancy, regions and processing location. The security page states a single-tenant architecture with a separate AWS account, network, servers, databases and storage per client and dedicated resources per matter, deployment into any AWS region to meet local residency rules within forty-eight hours, hosting stated across the United States, European Union, United Kingdom, United Arab Emirates, Australia and Singapore, and the options of hosting in a client's own AWS environment or a government-approved FedRAMP environment; all processing and communication with cloud services occurs inside the client's private VPC over private links. The vendor contrasts this explicitly with shared multi-tenant platforms.
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.
Prescient Security issued Lawdify's ISO/IEC 27001:2022 certificate on February 13, 2026. Certificate 122744, Issue 2, is valid through February 25, 2028, subject to satisfactory surveillance. Its scope covers the information security management system supporting the Legal AI Analyst platform hosted on Azure and Google Cloud Platform, including engineering, development, information security, legal and finance functions. The certificate is available directly in the trust center, with the issuer, dates and scope visible. Separate audit and penetration testing reports require access approval. The certificate establishes a current independent management system attestation; it does not certify the correctness of generated legal analysis.
Certification is real, dated and scoped, but the report is reachable only through sales. The security page says a SOC 2 Type II audit was completed in November 2025 by an independent service auditor. It covered the control environment and governance, risk assessment, logical access, system operations and change management, and monitoring and incident response. A penetration test was completed in September 2025 by AppSecure, a CREST accredited firm, with all findings fixed and verified. The vendor plans to pursue ISO 27001 in 2026, which is a plan, not a certification. The auditor is not named, and the SOC 2 report and penetration test summary are available to qualified prospects under NDA by contacting the team.
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.
Case Brain combines graph and vector databases to connect contract information with evidence and follow changes in a dispute. Lawri, specialized skills and the email agents operate on that case context rather than treating each request as an isolated conversation. Google Cloud Platform, Microsoft Azure and AWS support the infrastructure, with the legal platform's certification scope specifically covering Azure and Google Cloud Platform hosting. The foundation models and their versions are not individually identified. Nor is there a stated customer notification mechanism for model substitutions. The architecture is partly explained, while the full chain of model operators remains unspecified.
The vendor refers to several AI models without naming them on the pages read. The Alchemy AI page is headed three search engines and multiple AI models, and the home page describes five AI engines. A 2024 third party directory entry mentions GPT-4, but that is not vendor material. The vendor says all processing happens inside the client's locked down AWS VPC, which places inference in the client's environment without naming the provider or model. No commitment to notify customers of model changes is stated. The Alchemy AI page was not opened and may say more.
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.
The current terms distinguish recurring self service subscriptions from enterprise arrangements governed by an order form. Additional processing pages and capacity for AI tasks can carry separate charges, so both the recurring structure and usage expansion are visible. Prices are presented during registration or contracting rather than in an open rate table. Cancellation deadlines differ between self service and enterprise subscriptions, and fees are generally nonrefundable. An earlier founder submission gave an annual range without identifying the currency, which does not establish a current comparable price. Implementation charges and the amount charged for each additional processing unit are also not separately stated.
The unit and structure of pricing are stated, but not the figure. Cloud Utility Pricing charges by the hour while a site is in use and a much lower rate, stated as seventy percent less, when it is switched off. Customers can schedule operating hours and use an energy saver power down, and the vendor says most clients save fifty to sixty percent on hosting. The 2023 launch describes a low hourly rate without naming it. A pricing page exists in the navigation and was not opened; it may show the figure.
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 legal platform serves law firms and internal legal teams preparing disputes, with construction and infrastructure claims prominent in its founder account. Claims consultants, expert witnesses and contractors also use the same kind of evidence preparation workflow. Support includes APAC languages and communication formats such as WhatsApp, WeChat and KakaoTalk, alongside email archives, spreadsheets and handwritten material. The company separately sells insurance claims assessment, including bodily injury work. That combination gives a specific picture of the intended settings and evidence types. An exhaustive jurisdiction and practice coverage table, exclusions for particular legal tasks and firm size limits are not supplied.
Segment and coverage are described with substance; the boundaries are left open. The platform is sold to law firms, corporate legal departments and investigators for investigations, discovery and litigation, with the Alchemy page naming complex criminal matters, multi-defendant civil cases and joint defense groups as design targets, AI services extending to medical records, transcripts and scanned documents, review stated in any language, and hosting across six named jurisdictions. No matter type, data type or jurisdiction is named as unsupported.
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?
The February 2026 terms explicitly prohibit using customer documents, legal files and case material to train models or improve products and services. Only anonymized, aggregated usage metrics, such as feature use and error rates, may support platform improvement. That exception concerns operational telemetry, rather than the substance of client matters. The privacy policy also excludes customer personal data from generalized public model training.
Reusable skills within the customer's workspace are a product function; they do not override the express restriction on training from customer content.
No customer agreement is published, and no training statement was found. The site's pages on 6 September 2026 include a privacy policy, a GDPR disclosure and a contact page but no terms of service. The security page, security FAQ, Alchemy page and AI services page cover isolation, encryption and validation without saying whether customer content trains any model. The privacy policy was not opened and may say more. Pages checked 6 September 2026.
Prompt and Output Retention
How long does the product keep what a lawyer typed, and can that be set to zero?
Account data remains subject to the privacy policy's service, legal and business purposes. The terms set a 30 day period after a user deletes customer material and a similar exit period before resources and backups are removed, subject to applicable requirements. A deletion certificate and logs follow service termination. These are deletion event rules, rather than a fixed lifetime for every prompt and answer during an active subscription. A customer selected zero retention option and a separate schedule for AI provider copies are not described.
No public material found says how long prompts, answers or matter data are kept. The security page describes data protection from ingestion through final disposition without a retention period or deletion commitment, and no customer agreement is published. The privacy policy was not opened and may say more. Pages checked 6 September 2026.
Ethical Walls and Matter Segregation
Does retrieval respect the firm’s ethical walls, or can the model read across them?
The hosting terms describe logical separation between customer environments, with authentication and access controls. The product also organizes information into cases and lets the Excel user select a matter. Those features do not specify how conflicting matters inside one firm are separated during retrieval. Inheritance of source system permissions, enforcement for each user and the treatment of shared skills across restricted matters are not described.
Customer isolation therefore has an express basis, while the behavior of ethical walls within an individual firm's workspace remains unspecified.
The product runs its own separation model and documents it down to the matter. The security page describes a separate AWS account for each client; separate servers, database, search and storage for each matter; a URL and independent login for each matter; and role based access control in which every function can be assigned as a permission. On sites shared by several parties, user groups keep private fields, tag sets and searches while sharing folders. The security FAQ adds that each case has its own encryption keys. Pages checked 6 September 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 confidentiality terms require prompt written notice of a compelled disclosure demand to the extent practicable, allowing the disclosing party to seek a protective order at its own expense. The receiving party also agrees to cooperate with those efforts. For a broad regulatory request not aimed at the customer, the agreement permits compliance with prompt notice where the regulator allows it. Disclosure may still proceed when legally required.
These provisions establish a notice commitment with stated qualifications, without a transparency report describing requests actually received.
No public material found says whether the customer is told when a third party demands its data. No customer agreement is published, and the security page says nothing about legal process. The privacy policy and GDPR disclosure were not opened and may say more. Pages checked 6 September 2026.
Primary Law Corpus Provenance
Where does the law in this product come from, and does the vendor have the right to use it?
Legal research is described as covering cases, statutory material and other legal databases, alongside analysis of the customer's own evidence. The interface illustrates relevance and application summaries, but does not identify a complete set of source publishers, licensing arrangements or jurisdiction specific update schedules. The insurance offering separately refers to proprietary injury and settlement intelligence, which does not establish the rights supporting the legal research collection.
Customer uploaded evidence and external legal authority have different provenance, and the latter's acquisition and licensing terms remain unspecified.
No located public material identifies a legal corpus behind the product's answers, and the product is not built on one: it searches, reviews and answers over the customer's own collected documents and cites its sources to them. Home page and Alchemy page checked 6 September 2026.
Good Law Verification
Does the product tell you when the authority it just cited has been overruled?
Lawri can check facts against the case file, and Evidence Check examines whether evidence exists and supports a proposition. Those functions concern factual support. The legal research feature includes smart citations, case relevance and statutory analysis, but does not identify a licensed citator or explain its own subsequent treatment process. A case appearing relevant is different from confirming that it remains usable authority after later decisions.
The product's general verification functions therefore do not establish an automated good law check for authorities used in a draft.
No located public material addresses whether authority is checked for subsequent history, and the product does not retrieve or cite primary law; its output is sourced reports, review decisions and productions from the customer's documents. Surfaces checked 6 September 2026.
Refusal and Uncertainty Behavior
What does the product do when the answer is not in the corpus?
The interface includes a prompt optimizer, factual checking and evidence support checks. The terms warn that model output is probabilistic and should receive expert evaluation. None of those descriptions specifies a refusal path when the available material cannot support an answer. A stop condition, a visible uncertainty threshold and the response presented after grounding fails are not defined for Lawri or the email agents.
The question is how the product handles insufficient support before delivering a result, separate from the lawyer's opportunity to review it afterward.
No located public material describes what the answering or review models do when they cannot ground an answer or classify a document with confidence. The platform returns sourced reports and the services are described as validated, but no abstention path or confidence signal is described on the surfaces read; the Alchemy AI page was not opened. Home page and AI services page checked 6 September 2026.
Fabricated Citation Record
Does a public court record exist addressing fabricated or hallucinated legal citations in output from this product?
No matching entry was found in the AI Hallucination Cases database for Lawdify, Lawri, Case Brain or Smart Tables as of October 16, 2026. The check covered the company name and the named legal product components rather than treating a general account of AI misuse as an incident involving this service. This result records the absence of a matching case in that database on the stated date. It does not measure citation accuracy, establish that every generated draft is reliable or remove the need for professional verification.
No court order, opinion or disciplinary record naming Merlin Search Technologies, Alchemy, DiscoveryPartner or Sherlock was found as of 6 September 2026. The AI Hallucination Cases database maintained by Damien Charlotin was searched on the company and product names, along with a general search for court findings on ediscovery review. Results were directory entries and commentary, none of them a court record naming this product.
This describes the public record, not the product. A review platform that cites the customer's own documents has little exposure here.
Bar Guidance Alignment
Has the vendor engaged in public with the ethics opinions its buyers are bound by?
The legal workspace supports lawyers through first drafts, evidence analysis and tracked edits, and its terms retain expert review and disclaim legal advice. Those provisions do not connect the workflow to a named bar opinion or a jurisdiction specific framework for competence, confidentiality and supervision. The company's mention of inclusion in a government AI guide is a product listing rather than an explanation of professional obligations.
No mapped set of bar requirements accompanies the described legal workflow or its use by claims specialists who are not lawyers.
No located public material names an ethics opinion, bar rule or professional responsibility framework. The founder publishes books and webinars on generative AI in discovery, which is education rather than guidance alignment, and no customer agreement exists to carry a reference. Home page, security page and headlines navigation checked 6 September 2026.
Billing and Fee Posture
Does the vendor address what happens to the bill when the work takes an hour instead of six?
Lawdify presents faster case preparation and less repetitive evidence work as benefits for disputes teams. Its agents can examine files, produce first drafts and bring case information into spreadsheets, which may change the time spent on a matter. The legal workflow does not include a record separating AI effort from lawyer time for a client invoice. Guidance on passing through productivity gains, recording review time or explaining AI use in an engagement is also not supplied. The cost argument remains an efficiency claim rather than a billing method.
Law firms are a named buyer, and the published position on the bill is a savings claim. Most clients are said to save fifty to sixty percent on hosting under hourly on and off pricing, and AI review is offered at a fraction of the cost of manual review. Hosting is a technology cost a firm may pass on to its client, and nothing addresses how AI assisted review is recorded or disclosed on a client's bill. Home page and 2023 pricing release checked 6 September 2026.
Outside Counsel Guideline Readiness
Can a firm get this vendor through a client’s AI clause without a bespoke negotiation?
The named service providers are Google Cloud Platform, Microsoft Azure, AWS, Cloudflare, GitHub and Vanta, covering hosting, monitoring, version control and compliance. Customers can also inspect the current terms, privacy policy and ISO certificate, while additional assurance materials require an access request. This provides identifiable suppliers for part of a client's vendor review. A full model provider inventory and a prepared client disclosure or consent package are not presented with the list.
The available material therefore supports supplier identification without establishing every fact needed for a particular client's AI clause.
No subprocessor list or model provider list was found. Amazon Web Services is named as the infrastructure provider, which says where data is hosted, not whose models see it, and the vendor refers to several AI models without naming them. No customer agreement or DPA is published, and the SOC 2 report is available only under NDA to qualified prospects. The privacy policy and GDPR disclosure were not opened and may say more. Pages checked 6 September 2026.
Court Disclosure Support
If a judge’s standing order requires an AI disclosure, can the product produce one?
The Excel connection retains chat history organized over time, with controls to revisit, rename and delete conversations. Word drafting uses tracked changes, and the legal workspace can export organized bundles for filings. These preserve parts of the work and review context. They do not form a single export identifying the model used for each passage, all retrieved sources and the lawyer who verified them. A court specific disclosure or verification certificate is not described. The available record is partial and should not be treated as an automatically completed filing declaration.
Some elements of a disclosure record are available and no export of an AI-use record is described. The security page states extensive audit logging of every person's document views, tags, searches and other actions, and the platform's answers are sourced to documents, which together record what was reviewed and by whom; nothing states that a record of the model used, its classifications and the human verification can be exported for a court, and the product's outputs are productions and reports rather than court-facing certifications. Security page and home page checked 6 September 2026.
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.
- Primary Law Corpus Provenance
- Good Law Verification
- Refusal and Uncertainty Behavior
- Bar Guidance Alignment
Which one fits
Choose Lawdify if
- Office tasks should use a common understanding of the dispute.
- Custom workflows need to capture and reuse the team's own expertise.
- APAC language and messaging support matters alongside conventional legal files.
Choose Merlin Search Technologies if
- The team wants sourced reports across a large discovery collection.
- Document review or specialist transcript and medical record work may be delivered as a service.
- An isolated site with utility based hosting fits the matter's operating pattern.
In summary
Lawdify
Lawdify brings structured dispute knowledge into Lawri and connected office applications.
Merlin Search Technologies
Merlin combines Alchemy's sourced reports with AI review and specialized evidence services.
Questions buyers ask
Does Merlin only return a list of matching files?
No. Alchemy produces sourced reports such as timelines, witness analyses and arguments, with links to the supporting material. From the AI Legal Index, based on each vendor's own published materials as of October 16, 2026. No vendor pays for placement.
How does Lawdify use institutional knowledge?
Expertise Engineering creates specialized skills from company material, with options to retain a learned task and share it with teammates. From the AI Legal Index, based on each vendor's own published materials as of October 16, 2026. No vendor pays for placement.
Can ReviewPartner be used separately from Alchemy?
Merlin offers ReviewPartner inside the platform and as a standalone managed review service. 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.
Merlin's managed AI services can be purchased separately from its platform. Lawdify's insurance claims offering is outside this legal workflow comparison.