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Bloomberg Law
Bloomberg Law is a legal research and intelligence platform combining primary law, dockets, expert practical guidance and a legal newsroom in one subscription. The vendor quantifies its content: more than 15.5 million court opinions, growing by about 50,000 a month; about 200 million dockets with over 21 million pleadings; more than 5 million statutes and regulations; 75 million EDGAR filings; and over 8,200 guidance documents by practitioners.
Machine learning runs through this content. BCITE, the platform's own citator, uses machine learning to show whether a holding is still good law; Docket Key classifies more than 200 motion and brief types across federal district courts; Litigation Analytics profiles courts, judges, firms and attorneys; Draft Analyzer compares agreement language with market standard; and Points of Law links related opinions. Generative tools sit on top: Bloomberg Law Answers gives sourced answers, AI Assistant is a chat that links answers to authorities, Complaint Summaries condense complaints in docket alerts, and Clause Adviser explains contract language.
A published AI page describes retrieval augmented generation with a proprietary guardrail service, grounding in the vendor's own content, and abstention when no content covers a question. Buyers are law firms, in house teams, government and law schools, on a flat, all inclusive subscription. Bloomberg Law is published by Bloomberg Industry Group, Inc., an affiliate of Bloomberg L.P., and launched in 2009.
Bloomberg Law, head to head
Side by side on the same 15 capability axes and 12 legal signals: where the two part company, and what neither one publishes.
Alternatives to Bloomberg Law
Vendors in the same categories as Bloomberg Law whose graded profiles look most alike, with a line on what each product does.
Similar is not the same, so check that each one does the job you need. No vendor pays for placement.
- TTuriviusTurivius is a Brazilian legal research platform combining case law search, jurimetrics and generative AI.
- IIntelligize+ AIIntelligize+ AI is the AI version of Intelligize, a LexisNexis platform for SEC disclosure, reporting and transactional research, run from New York.
- OOne LEGALEOne LEGALE is Wolters Kluwer Italia's online legal research platform for Italian lawyers, built on legislation, commented case law, doctrine, journals and practice tools from the group's…
- LLegitQuestLegitQuest combines an Indian legal research database with tools that retrieve authorities, break down judgments and display later case treatment. iSearch finds relevant material, iDRAF…
- OOmnilexOmnilex is an AI workspace for Swiss law that brings statutes, court decisions, commentary and a firm's own knowledge into one place for research and drafting.
- JJus MundiJus Mundi is a research platform for international law and arbitration.
Ordered by how closely the graded profiles match across the 15 capability axes, inside the same categories. See how each axis is graded in the methodology.
Capability grades
All 15 axes, graded from public sources on the date shown. 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 vendor's approach page lists differentiated content as one of four pillars and says the AI does not generate answers on its own but searches a large collection of vetted information. Without the models, 15.5 million court opinions, 200 million dockets, 5 million statutes and regulations, 8,200 practical guidance documents and a newsroom remain, which is what Bloomberg has sold since 2009. Some tools would not survive: BCITE, Docket Key, Points of Law and Litigation Analytics are built with machine learning and do not exist without it. The subscription is flat and all inclusive, and buyers do not buy or meter the AI separately.
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.
The vendor names retrieval augmented generation with a proprietary guardrail service, says output is grounded in its own expert or primary content rather than the internet, and says Bloomberg Law Answers cites and links the authorities behind each answer, with one click verification. BCITE, the platform's own citator, reports whether a holding is still good law. The system abstains when no content covers a topic or a question is outside legal scope.
No result, test set, sample size or error rate from the described benchmarking by former attorneys is published, although the same page commits to giving customers clear documentation of that benchmarking and validation. The vendor says plainly that AI answers are never fully trustworthy and carry a hallucination risk, and tells customers to check them against primary sources.
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 vendor says it wants its AI to make the job easier, not do it for the user, and describes building one task at a time rather than producing finished work product. Answers carry inline citations with one click verification, abstention is documented with two named triggers, all generative tools are beta tested before release, and customers are told to check answers against primary sources and use their own judgment.
Nothing on the approach to AI page, the AI product page or in either agreement describes what happens after an answer proves wrong; no correction or error reporting route is documented, and no confidence threshold or setting is offered. Deep Thinking, which the vendor says automatically plans and runs multistep research before synthesizing results, has no described checkpoint between request and finished answer.
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.
The pricing page has three quotes. One names an organization and an outcome: a corporate counsel at Synovos, Inc. says the platform let the team pull state summaries of laws and regulations without calling outside counsel, cutting tens of calls on minor issues and lowering annual legal fees, though with no figure, timeframe or date. The other two are attributed only by role and segment, a small law practice area leader and a large law partner, and both concern the flat pricing rather than outcomes.
The home, AI product, approach to AI and pricing pages and the navigation carry no case study with a method, customer count, named law firm or quantified result.
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 privacy policy says collected information includes user content, usage, browsing and search history, reading habits and inferences used to profile a user's preferences and behavior. It describes advertising over that material: lookalike or matched audiences on social platforms, sharing a hashed email with ad companies, and sharing for cross context behavioral advertising under California and Virginia law, with opt outs.
The policy covers the marketing site and the products together and does not say whether in product search history is excluded. The subscription terms say the site is not meant to hold personal or health information, give no warranty on how it would be held, and reserve a right to audit and monitor use. The documents do commit to notice of subpoenas, Standard Contractual Clauses, an intragroup data protection agreement, and data centers used only for the vendor's operations.
No statement on training with customer input, retention period, separation between users or matters, or treatment of privilege or work product is published.
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.
Both the website terms and the subscription terms say in capitals that none of the services is legal advice, opinion or recommendation, that users needing legal help should consult a competent professional, that users take full responsibility for decisions and conclusions reached through the services, and that no attorney client relationship forms with the vendor or any content supplier. Eligible users are defined: eighteen or older, members of a professional, business or academic community doing professional research, working in a court, government agency, corporation or similar body, or law or business students, with bots excluded.
The public approach to AI page addresses competence and supervision, telling customers to check answers against primary sources and use their own judgment, and saying the AI makes the job easier rather than doing it. No bar or ethics guidance is named, including ABA Formal Opinion 512, and no jurisdictional limit on answers is stated.
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.
The approach to AI page, last modified 18 June 2026, sets out four pillars and gives the accuracy pillar substance: retrieval augmented generation plus a proprietary guardrail service, output grounded in vetted first party or primary material rather than the open internet, beta testing of every generative tool before release, and accuracy evaluation by former attorneys from development through internal, beta and customer testing and over the product's life, with continuous tuning against model drift.
It commits to documenting where and when products use generative AI, the sources used, and the benchmarking and validation process. No accountable owner is named, and no test result is published against that benchmarking commitment. Nothing addresses bias or uneven output across matter types, jurisdictions or populations, and the platform profiles named judges, attorneys and firms. Its trust center lists a document titled AI Frequently Asked Questions.
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 privacy policy, updated 9 July 2026, says personal information is held on secure servers under administrative, technical, personnel and physical measures, and sets retention by criteria rather than a number: the time needed for the purpose, whether the user has stopped using the products, any legal proceedings, and legal or regulatory requirements. It describes a personal data governance program, a data map of flows within the vendor's systems and to third parties, ongoing testing of safeguards, company wide privacy training, and data centers used only for the vendor's own products and operations with monitored access.
Standard Contractual Clauses and an intragroup data protection agreement cover transfers, with further processing terms available where the vendor acts as processor. The trust center adds data erasure, backups, access monitoring, least privilege and logging, and a full responsible disclosure policy. No retention period, named subprocessor or breach notification commitment 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.
The subscription terms, updated November 2025, cap total liability at 500 dollars whatever the form of action, disclaim all warranties including merchantability, fitness for purpose and noninfringement, provide the services as is and as available, and do not guarantee accuracy, correctness, completeness or timeliness. One exclusion covers research itself: no liability for any inability or failure to perform legal or other research, or to do it properly or completely, even with the vendor's help.
Users are solely responsible for the accuracy and adequacy of data used and the output, and any action must be brought within one year. Virginia law governs, with exclusive jurisdiction in Arlington County. The only exception to the cap is death or personal injury from negligence where the law forbids limiting it. Nothing runs to the customer: no indemnity, output warranty or AI specific term. Its trust center lists a certificate of liability.
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.
Two integrations are described. The vendor's newsroom describes Bloomberg Law Contract Solutions as integrating with Microsoft Word alongside a central contract repository, and the subscription terms cover users reaching Bloomberg Law through the Bloomberg Professional service, the Terminal, whose own agreement then applies. The navigation, the AI product page, the approach to AI page and the footer show no integrations page, developer or API documentation, or named connection to document management, matter management, ebilling or court filing. Dashboard Legal is a project management and collaboration product, not a bridge into a firm's systems.
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.
The privacy policy's intragroup data protection statement says the vendor's data centers serve only its own products, services and operations, with secure, monitored access. Transfers are acknowledged: personal information may be processed in various countries with differing protection, including the United States, under Standard Contractual Clauses and an intragroup agreement naming exporters and importers. Nothing published offers a region or residency choice, describes the platform's tenancy, separates processing from storage, or says where generative features run.
Its trust center lists a data center item. These statements sit in a personal data annex, not in product or security material.
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.
Every page footer links to the trust center under Trust and Security, with a product identifier in the address. It is a SafeBase portal reachable without a sales call, listing a penetration test report, security whitepaper, SOC 1 and SOC 2, a SIG Lite self assessment, a certificate of liability, a business continuity management document and an AI FAQ, along with control areas for product, access, application and network security, business continuity and training, and a full responsible disclosure policy.
SOC 1 and SOC 2 are named without a Type, and no auditor, coverage period, report date or scope is published. Documents are split into public and private, with an access request whose terms are not stated.
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 vendor publishes retrieval augmented generation as the framework, a proprietary guardrail service over it, generation grounded in its own expert and primary content rather than the internet, and large language models trained on that content. It also cites machine learning, natural language processing, information retrieval, recommendation systems and large language models, and says Bloomberg Industry Group employs hundreds of AI researchers and engineers who publish research each year.
Nothing published says whether a third party foundation model sits underneath: the phrase used is our large language models, without saying they are built in house. No model name, version, inference location or subprocessor is given, and there is no commitment to notify customers of model or training changes. Its trust center lists a subprocessors item and an AI FAQ.
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 charging model is stated: one platform, one flat all inclusive price, with unlimited access rather than per search or per document charges. A customer quote on its pricing page says occasional use of a tool costs nothing extra, and the request form names two products, Bloomberg Law Research and Bloomberg Law Dashboard Legal. The pricing page, getting started entry, AI product page and navigation give no figure, unit of charge, tier names, feature splits, term length or implementation charge.
Every route to a number is a form, and the Request Pricing page is a lead capture form. Its firm size ranges, from solo to fifty or more practitioners, suggest price varies with headcount, but they are form fields, not a published price structure.
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.
Four buyer groups have solution pages: law firms, in house counsel, government and law schools, and the government page promises unlimited access to state and federal coverage. The vendor's forms list law firms, tax and accounting firms, corporations, nonprofits and associations, consulting firms, education institutions and government, with firm size ranges from solo to fifty or more practitioners. Content is quantified precisely: more than 15.5 million court opinions growing by about 50,000 a month, about 200 million dockets with more than 21 million pleadings, more than 5 million statutes and regulations, 75 million EDGAR filings and more than 8,200 practical guidance documents, with Docket Key covering over 200 motion and brief types across all federal district courts and Chart Builders comparing rules state by state.
Nothing says where coverage stops: no supported practice area list, no coverage table by jurisdiction, no statement of state court depth, and no acknowledged gap.
5 public documents
The public pages on file for Bloomberg Law, with the recorded signals each one supports and the date it was last read. Open any of them and check the reading against the record.
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Primary Law Corpus Provenance, Good Law Verification, Refusal and Uncertainty Behavior and 1 more
Read Aug 31, 2026
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bloombergindustry.com/privacy-policy4 signals
Client Data in Training, Prompt and Output Retention, Ethical Walls and Matter Segregation and 1 more
Read Aug 31, 2026
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Billing and Fee Posture
Read Aug 31, 2026
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Outside Counsel Guideline Readiness
Read Aug 31, 2026
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Fabricated Citation Record
Read Aug 31, 2026
No published figure
- Bloomberg Law will not tell you the price without talking to a salesperson.
- What it does tell you is how the charging works: you pay one price for everything, and you are not charged again each time you search or open a document.
- The main alternatives do charge that way, so a heavy research month can cost more with them; here it does not.
- There are two things you can buy: the main research platform, and a project management tool called Dashboard Legal.
- The form asks how many lawyers you have, from solo up to fifty or more, so the price probably depends on that.
The request pricing page, the getting started entry and the home page publish the charging model with no figure at any level. The model is stated consistently: one platform, one flat all inclusive price, with unlimited access rather than per search or per document charges. Published customer quotes say occasional use of a tool costs nothing extra and that a partner prefers the flat fee to competing products. The request form names two products, Bloomberg Law Research and Bloomberg Law Dashboard Legal, and asks for a practitioner count in ranges of solo, 2 to 5, 6 to 19, 20 to 49 and 50 or more, which suggests price scales with headcount, though that is a form field rather than a published price structure.
Not published: any rate, unit of charge, tier names, feature splits, contract length, renewal or cancellation terms, or implementation and training costs. The Request Pricing menu item leads to a lead capture form.
Note: No figure is published anywhere, so no entry price is recorded; this means the price is withheld, not that the product is free. The vendor publishes the charging model, and direct competitors charge per search and per document. No rate, unit, range, term or implementation charge is published. Every route to a number is a lead capture form.
Legal Signals
What each signal meansA signal records what public sources say on the date shown. It is not a grade and it is not a recommendation. Where a signal reads Not addressed, it means the index did not locate the material in public sources on that date, which is a statement about disclosure rather than about the product.
Client Data in Training
Can material a lawyer puts into this product be used to train a model?
A published agreement or policy exists and none of it addresses the question either way, or the document that would answer it could not be read and the summary names the retrieval limit. The summary states which shape the silence takes: an improvement right granted that never names training, or no improvement right granted at all.
No published term or policy states whether customer input is used to train or refine any model. The privacy policy lists improving, upgrading or enhancing the products and developing new ones among its purposes for collected information, which covers the ground without naming training. The approach to AI page says the vendor's large language models train on its own specialized content, meaning its court opinions, guidance and news, and says nothing about customer material.
The subscription terms prohibit a customer from using product content in prompts or for training, tuning or incorporating into any language model without the vendor's written consent, and define language model broadly enough to catch any generative system. The prohibition runs one way.
Prompt and Output Retention
How long does the product keep what a lawyer typed, and can that be set to zero?
Retention is acknowledged in public materials with no stated period.
The privacy policy states that personal information is kept for as long as necessary for the described purposes and legitimate business reasons, and names four factors used to set the window: the time needed to achieve the collection purpose, whether the user has stopped using the products, the existence of relevant legal proceedings, and legal or regulatory requirements. Search history and user content are expressly within the categories collected, so questions put to the AI features fall inside this framework, but no period attaches to them and no customer configurable or zero retention setting is offered.
The subscription terms point the other way on uploads, stating the vendor has no obligation to retain or store user generated content.
Ethical Walls and Matter Segregation
Does retrieval respect the firm’s ethical walls, or can the model read across them?
No located public material addresses walls or matter level segregation.
The privacy policy says personal information may be disclosed to whoever authorized the user's access, such as an employer or school, so a subscribing firm can receive information about what its own users did. The subscription terms treat each registration as single user and require credentials to be kept confidential, which is account security rather than a permission model. Nothing in the privacy policy, either agreement, the trust center landing page or the AI product and approach to AI pages addresses separation between customer organizations, access within a firm, or matters.
Third Party Request and Subpoena Notice
If someone subpoenas the vendor for a firm’s data, does the firm hear about it first?
Terms commit to notice where lawfully permitted. No transparency report located.
The privacy policy covers disclosure to unaffiliated third parties under a subpoena, court order or other legal process, and says the vendor will use reasonable efforts to give the user prompt written notice so the user can comment, object and protect the information's confidentiality, and will cooperate at the user's expense. Three qualifications apply: it is reasonable efforts rather than an unconditional promise; cooperation is at the user's cost; and the subscription terms separately reserve the right to use and disclose user information in any legal process or as law requires, with no notice, which nothing reconciles. 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 identified without stating the license or rights basis.
Bloomberg Law publishes counts: more than 15.5 million court opinions growing by about 50,000 a month, about 200 million dockets with more than 21 million pleadings, more than 5 million statutes and regulations, 75 million EDGAR filings and more than 8,200 practitioner guidance documents, covering primary law and the vendor's own editorial layer, with an update rate for opinions. No upstream supplier is named. The privacy policy mentions third party data suppliers only as a category and notes that databases available to registered users contain public records, publicly available information and nonpublic information from those suppliers.
The subscription terms mention third party content providers without naming any. No license, public domain basis or rights statement is published.
Good Law Verification
Does the product tell you when the authority it just cited has been overruled?
The vendor computes and surfaces subsequent history itself, with the method described.
BCITE is described as Bloomberg Law's court opinion citator, using machine learning and natural language processing to show quickly whether an opinion's holding is still good law. It is the vendor's own citator with a stated method, not a licensed one or a prompt to check elsewhere. Not disclosed: coverage of courts or dates, an accuracy or error rate, how treatment is decided beyond the techniques named, and whether generative features show BCITE status beside the authorities they cite.
Refusal and Uncertainty Behavior
What does the product do when the answer is not in the corpus?
The vendor describes refusal or abstention behavior in public materials.
The vendor says Bloomberg Law Answers and AI Assistant will not answer when no content covers the topic or the question is outside legal scope. It also says plainly that AI answers are never fully trustworthy and carry a hallucination risk, and tells customers to check them against primary sources. No worked example of a refusal, measurement of how often abstention works or fails, or confidence score shown to users is published.
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 addressing fabricated or hallucinated legal citations produced by this product has been located as of the date shown. This is a statement about the public record on that one subject, not a finding about the product, and this signal is not a litigation history.
The AI Hallucination Cases database maintained by Damien Charlotin, which tracks court decisions on hallucinated AI content and names the tool where known, records no case naming Bloomberg Law or its publisher, and no court order, opinion or disciplinary record naming the product appears in 2026 sanctions trackers, law library commentary or trade press. This is a statement about the public record rather than a finding about the product.
The name appears often in this literature for other reasons: practitioner guidance names Bloomberg Law as a database for checking citations, and its newsroom reports on sanctions.
Bar Guidance Alignment
Has the vendor engaged in public with the ethics opinions its buyers are bound by?
No located public material engages with bar or ethics guidance.
The approach to AI, AI product, about and pricing pages, both agreements, the privacy policy and the trust center landing page engage no bar or ethics authority, including ABA Formal Opinion 512 or any state bar guidance, and nothing maps the product to a conduct duty. The disclaimers in both agreements and the validation advice on the AI page state the vendor's own position rather than the rules its buyers follow. Bloomberg Law's newsroom covers legal ethics and AI sanctions extensively; that coverage is journalism, not a statement about the product.
Billing and Fee Posture
Does the vendor address what happens to the bill when the work takes an hour instead of six?
Public materials claim time savings without addressing billing or disclosure, and the product sits inside a fee relationship between a lawyer and a client where those savings would change the bill.
The vendor's material centers on time and cost saved: AI that helps customers work efficiently and finish research faster, summaries to speed reading, and a flat subscription pitched on the basis that occasional tool use costs nothing extra. A customer quote describes lower annual legal fees from fewer calls to outside counsel. The pricing, AI product and approach to AI pages and both agreements describe no record of AI assisted work by matter for fee purposes and no guidance on billing or client disclosure where an AI answer informs advice.
Flat pricing removes the pass through question that metered research platforms raise; it does not address client disclosure.
Outside Counsel Guideline Readiness
Can a firm get this vendor through a client’s AI clause without a bespoke negotiation?
The material exists behind a sales conversation or an executed agreement.
The trust center portal, linked from every product page footer and reachable without a sales call, lists subprocessors, a data processing agreement, data subject requests, a security whitepaper, a SIG Lite self assessment and an AI FAQ. No subprocessor or model provider is named on the portal landing page or any Bloomberg Law page, and documents are split into public and private with an access request whose terms are not stated. Whether a firm can get the listed material without a signed agreement is not stated.
Court Disclosure Support
If a judge’s standing order requires an AI disclosure, can the product produce one?
Some elements of the record are available, short of a document level export.
The vendor commits that generative answers or suggested text include links to sources or an explanation of the result, that Bloomberg Law Answers cites and links supporting authorities, and that inline citations allow one click verification, so a lawyer can produce and check the authorities behind an answer. It also commits to documenting where and when products use generative AI. No model is identified or versioned, so which system produced a passage cannot be known; nothing records who verified an answer; and no export for AI use disclosure or certification is published.