Pre/Dicta
Litigation prediction platform that forecasts how a federal case will resolve motion by motion, from a case number alone. Its distinguishing method is compositional matching rather than keyword search: a matter is matched against historically comparable cases sharing the same judge profile, party dynamics and counsel configuration, and the outcome of those comparable cases drives the forecast. The model is built on roughly two decades of federal litigation data, described at around 20 million cases and 40 million judicial decisions, with 50 to 100 data points attached to each case, parties and law firms broken into dozens of variables, and judges profiled on biographical characteristics including educational background, appointment history, political affiliation, net worth and geographic location. The vendor states its predictions are independent of case facts and legal precedent. Modules cover ten motion types plus appeals and include Judicial IQ, Outcome Intelligence, Counsel Compare, Venue Strategist, Precedent Intelligence, Timeline Intelligence, Judge Performance Index and Firm Performance Index. Every forecast is presented as inspectable, with the underlying comparable cases visible and linked to their dockets. Pre/Dicta draws an explicit line between prediction and forecast: it claims 85 per cent accuracy for motions to dismiss, tested across all 94 federal district courts, and models rather than predicts the other motion types. Buyers are litigators, general counsel selecting outside counsel, private equity valuing law firm acquisitions, and insurers setting reserves. The company is based in New York, was founded by litigator Dan Rabinowitz, launched in summer 2022, acquired the state court analytics assets of Gavelytics in January 2023, and won ALM Legal Week's Innovator of the Year award.
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 model is the product. A user enters a case number and receives a forecast; there is no repository, workflow, drafting surface or document store underneath that would function if the predictive model were removed. Nothing remains but the training corpus. The vendor states the position directly, describing itself as neither a self-serve analytics tool nor a case law research vendor, and the interface is built around the prediction rather than around search. This is the clearest instance of an A on this axis in either pull: not a model layered on a platform, but a platform that exists to deliver model output.
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 only vendor located in either pull that publishes a measured accuracy figure rather than asserting accuracy. The claim is 85 per cent on motions to dismiss, with the test set described as more than 50,000 motions to dismiss across all 94 US federal district courts, drawn from roughly two decades of federal litigation data. Failure modes are named against the vendor's own commercial interest: the 85 per cent applies to motions to dismiss alone, and for the other nine motion types Pre/Dicta states it is modelling outcomes rather than predicting them, a distinction carried onto the home page where one motion receives a prediction and every other a forecast. One inconsistency a buyer should know about and reconcile with the vendor: an interview transcript published on Pre/Dicta's own site states 87 per cent, against 85 on every other surface, and nothing reconciles the two. Two limbs of this axis do not bite on this product rather than being failed by it. Grounding to openable primary authority does not apply because the product forecasts outcomes rather than producing cited legal assertions, and citator status does not apply for the same reason. The vendor addresses the equivalent question in its own idiom by disclosing that forecasts are generated independently of case facts and legal precedent, which tells a buyer what the number does not rest on. Separately, and not a deduction under this band, the method behind the figure is not described, so whether it was produced by holdout backtest, live tracking or in-sample validation is unknown and no third-party validation was located. Checked 31 Aug 2026.
Autonomy and Oversight Model
What the system decides on its own, what a lawyer must approve, and whether the vendor documents where the review point sits. A tool that drafts under review and a tool that files without one are different products and different risks.
A real review surface is published and the rest of the control structure is not. The vendor commits that every output is inspectable down to the underlying docket, that every underlying comparable case is visible, inspectable and linked, and that outcomes are verifiable, which gives a lawyer a genuine route from a forecast back to the matters that produced it. The platform also allows a user to re-weight or change the factors behind a prediction, which is a form of human control over model output rather than passive consumption. What is absent, checked 31 Aug 2026: nothing states that a lawyer must review before relying, no threshold or confidence gate is described, and nothing addresses what happens when a forecast proves wrong. On a product whose output feeds settlement valuation and venue strategy, the absence of any published statement about the weight a forecast should carry is the gap.
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.
One named customer of real weight, with no figures attached to it. John B. Quinn, Executive Chairman and Founding Partner of Quinn Emanuel, is quoted on the record stating that all attorneys at the firm now have access to the platform, which is a named firm-wide deployment at a major litigation practice rather than a logo or an anonymous testimonial. The vendor also holds ALM Legal Week's Innovator of the Year award. What is missing is measurement of use: no figure for what changed at Quinn Emanuel or anywhere else, no case study with a stated method, no customer count, and no other named customer located on the surfaces read on 31 Aug 2026. The 85 per cent accuracy figure is a product measurement rather than deployment evidence and is graded on the Citation Accuracy axis instead.
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 carries a dedicated section on case reports that addresses the actual exposure this product creates, which is rarer than it sounds. It recognises that case numbers and reports may be sensitive, and commits that Pre/Dicta does not share, sell or disclose case numbers, case report contents or access history to third parties, that encryption and strict access controls prevent unauthorised viewing, and that case access logs are used only for security monitoring and troubleshooting rather than marketing or profiling. It also states that case numbers are not stored against a personal profile unless the user creates an account. That is a considered answer to the real risk here, which is that the matters a litigator looks up reveal the firm's docket and strategy. Three gaps: no statement on whether anything a user enters trains any model, no retention period, and privilege and work product are not addressed. Note the underlying exposure is lower than for a document platform, since the input is a case number rather than client files.
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.
Nothing published on the advice line, and nowhere it could sit. Searched the home page, the platform page, the privacy policy and the site footer on 31 Aug 2026. There is no disclaimer, no ethics or professional responsibility page, no bar or ethics guidance named including ABA Formal Opinion 512, and no statement that a forecast is not legal advice or should not substitute for professional judgement. There is also no terms of service to carry one: the footer link labelled Terms of Conditions resolves to the home page rather than to a document, and no terms were located anywhere. The exposure is among the highest on this index, because the published use cases are settlement valuation, venue selection, whether to move to dismiss and whether to challenge an expert, all decisions a client relies on counsel to make.
AI Governance and Bias Disclosure
Published governance over model behaviour: who owns it inside the vendor, what is tested before release, and what is disclosed about disparate output across matter types, parties, or populations.
No governance position is published for a model whose central design choice is the one this axis exists to examine. Pre/Dicta's forecasts are driven by judicial biography, and the vendor names the variables: educational background, appointment history, political affiliation, net worth and geographic location, with judges compared against cohorts of similar background. Its own marketing has promoted comparative dismissal rates for Republican appointees, and an enhanced biographical intelligence feature lets users isolate a judge's political affiliation to see how it bears on outcomes in cases like theirs. Searched the home page, platform page, privacy policy, news index and footer on 31 Aug 2026 and located no responsible AI statement, no accountable owner, no pre-release testing regime, no fairness or bias evaluation, and no discussion of what it means to model identifiable federal judges by politics and personal wealth. A vendor may well have defensible answers here; none is published, and on this product the question is not incidental but constitutive.
AI Safety and Data Stewardship
Retention, deletion, access control, and what happens to prompts and documents after they are processed. Whether the vendor states its subprocessors and its incident practice, or leaves the buyer to assume.
A general privacy policy covers the ground in principle without specifying any of it. It states industry-standard technical and organisational measures including encryption, secure servers and limited employee access, commits that case report access logs are automatically purged after a limited retention period unless legal obligations require longer, and addresses cross-border transfers through Standard Contractual Clauses. Searched the home page, platform page, privacy policy and footer on 31 Aug 2026 and located no security page of any kind: no encryption standard, no access control model, no named subprocessor, no incident or breach notification practice, and no stated retention period. The policy's own credibility is undercut by a defect recorded separately, which is that the document was published with unreplaced template placeholders in place of the company name and the privacy contact address.
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.
Nothing is published, because there is no agreement to publish it in. The footer of every page carries a link labelled Terms of Conditions, and it resolves to the home page rather than to any document. Searched for terms of service, terms and conditions and any subscription agreement on 31 Aug 2026 across the site and the wider web and located none. The consequence is total: no indemnity, no liability cap, no warranty or disclaimer of warranty on output, no insurance position, no allocation of risk of any kind between vendor and customer. This is a product that forecasts whether a motion will be granted and is marketed for settlement valuation and reserve-setting by insurers, sold without published terms addressing what happens when a forecast is wrong. Recorded as a vendor-side absence rather than a retrieval failure: the link exists and points to the wrong place.
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.
No integration into any practice system was located. Searched the home page, platform page, support page link, privacy policy and footer on 31 Aug 2026. No API is offered or referenced, no document or matter management system is named, no billing or filing system connection appears, and there is no integrations page. What the vendor publishes in this area are workflow conveniences rather than integrations: the platform is cloud-based and reachable from any location, reports can be shared under the firm's own letterhead, matter numbers can be assigned to reports for cost tracking, and no technical training is required to implement. Useful to a buyer, but none of it moves data between Pre/Dicta and the systems a litigation team already runs.
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.
Cloud delivery is implied and neither the tenancy model nor the region is stated. The only infrastructure statements located on 31 Aug 2026 are that the platform is cloud-based and accessible from any location, and that data may be transferred to and processed in other countries where the vendor's servers or partners operate, with Standard Contractual Clauses and other lawful safeguards applied to those transfers. That acknowledges cross-border processing while naming no country, no region, no hosting provider and no customer-selectable option. Nothing describes single or multi-tenancy, and nothing distinguishes where data is stored from where it is processed. No residency commitment of any kind is offered.
Security Certifications and Trust Center
Independent attestation a buyer can pull without a sales call: SOC 2, ISO 27001, penetration test summaries, a trust center with current reports and named scope rather than a badge image.
A badge with nothing behind it. The footer of every page carries an AICPA SOC for Service Organizations mark, in the non-CPA form licensed to service organisations, which asserts a SOC examination without stating which one. No accompanying text names SOC 1 or SOC 2, no Type I or Type II is specified, no auditor is identified, no coverage period or report date is given, no scope is described, and no route to request a report exists. There is no trust centre or security page anywhere on the property. Checked the home page, platform page, privacy policy and footer on 31 Aug 2026. A mark of this kind is meaningful only alongside the report it refers to, and the report is neither published nor offered.
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.
An unusual case: the vendor describes its own model in more detail than most of this pull and says nothing about anything it might depend on. What is disclosed is substantial for a first-party system, covering machine learning models trained on roughly two decades of federal litigation data at around 15 to 20 million cases, 50 to 100 data points per case, dozens of variables for parties and firms, and biographical profiling across hundreds of federal judges. Nothing published identifies a third-party model provider, and the architecture as described appears to be proprietary machine learning rather than a licensed foundation model, though no statement confirms that either way. Three gaps checked 31 Aug 2026: no model version or release identifier, so a buyer cannot tell which model produced a forecast or when it changed; no commitment to notify customers of changes to the model or its training data; and no subprocessor or infrastructure provider named.
Commercial Transparency
Whether a buyer can learn what this costs without entering a sales process: published rates, the unit being charged, what sits behind an enterprise tier, and what implementation adds.
No pricing information is published at any level, including the unit of charge. Searched the home page, platform page, privacy policy, the full primary navigation covering platform, about, research centre, news, support and contact, and the footer on 31 Aug 2026. There is no pricing page, no tier structure, no per-seat, per-case or per-report unit, no volume banding and no statement of what implementation adds. Every call to action is a demo booking or a case consultation request. The one adjacent statement is that the platform requires no technical training to implement, which speaks to effort rather than cost. Nothing published would let a buyer form any view of price before entering a sales process.
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 buyer segmentation is more precisely reasoned than most and the coverage boundary is only half stated. Four distinct buyers are named with the question each brings: litigators setting strategy and timing motions, general counsel choosing outside counsel on fit and proven results, private equity valuing law firm acquisitions on portfolio-level performance, and insurers setting reserves against how comparable matters resolve. Each is given its own surface. Coverage is stated concretely on the federal side, spanning all 94 district courts, the appellate layer, and ten named motion types from motions to dismiss through Daubert challenges, temporary restraining orders and motions to remand. The limit is where it thins: state coverage is described only as select state courts, with no list of which, no county or court detail and no depth statement, and the January 2023 acquisition of Gavelytics brought state trial court assets whose current coverage is not published anywhere. Checked 31 Aug 2026.
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?
No located term or policy addresses the question either way.
Searched the home page, the platform page, the privacy policy in full and the site footer on 31 Aug 2026. No located term or policy addresses whether anything a customer enters is used to train or refine the forecasting model, either way. The closest statement is that the vendor improves the service based on aggregated, anonymised usage data, which covers behavioural telemetry rather than model training on customer input. Two things reduce the exposure without answering the question: the input is a case number identifying a public federal docket rather than client documents, and the privacy policy commits that case numbers, report contents and access history are not shared, sold or disclosed to third parties. No terms of service exists in which a training term could sit.
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.
Retention is acknowledged and left unquantified. The privacy policy states that case report access logs are automatically purged after a limited retention period unless legal obligations require them to be held longer, and that personal data is retained only as long as necessary for the purposes described. No period is given for either, no retention position is stated for the case reports themselves as distinct from the access logs, and no customer-configurable setting or zero-retention option is offered. Read 31 Aug 2026.
Ethical Walls and Matter Segregation
Does retrieval respect the firm’s ethical walls, or can the model read across them?
Segregation is asserted in public materials with no published detail on how it is enforced.
Protection against unauthorised viewing is asserted without a described model. The privacy policy commits to encryption and strict access controls to prevent unauthorised viewing of case reports, and states that case numbers are not stored against a personal profile unless the user chooses to create an account, which is a real minimisation step. What is not published is any permission structure: nothing describes separation between customer organisations, nothing addresses access between users inside a subscribing firm, and nothing states whether saved or linked case reports are visible firm-wide. On a product where the set of case numbers a firm looks up maps directly onto its live docket, internal visibility is the question a buyer would ask, and it is unanswered.
Third Party Request and Subpoena Notice
If someone subpoenas the vendor for a firm’s data, does the firm hear about it first?
Published terms or policy address disclosure to authorities or in response to legal process, and no commitment or reservation regarding customer notice is located anywhere. The vendor has told the customer that data can leave and has said nothing about whether the customer hears of it.
Disclosure to authorities is permitted and notice is not addressed. The privacy policy states that limited data may be shared with legal authorities if required by law, court order or government request. No commitment to notify the customer accompanies it, no undertaking to seek a waiver where notice is prohibited, no minimisation obligation and no transparency report. That sits against a stronger commitment elsewhere in the same document, that case numbers, report contents and access history are not disclosed to third parties, and nothing reconciles the two. Whether a firm would learn that its access history had been demanded therefore rests entirely with the vendor. Read 31 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?
Sources are identified without stating the licence or rights basis.
The corpus is described by type and volume in more detail than most of this pull, and its rights basis is not stated anywhere. Published figures cover roughly two decades of federal litigation data at around 20 million classified cases and 40 million judicial decisions, with 50 to 100 data points per case and several million parties and firms, drawn from federal dockets across all 94 district courts. A second corpus goes undiscussed as such: the judicial biographical data driving the forecasts, covering educational background, appointment history, political affiliation, net worth and geographic location for hundreds of federal judges. No source is named for that material, no licence or public-record basis is given for either corpus, and no update cadence or data vintage date was located on 31 Aug 2026.
Good Law Verification
Does the product tell you when the authority it just cited has been overruled?
No located public material addresses whether authority is checked for subsequent history.
Searched the home page, the platform page, the privacy policy and the footer on 31 Aug 2026. Nothing addresses whether authority surfaced through the platform is checked for subsequent history, and no citator or treatment signal is claimed. The product forecasts outcomes rather than retrieving law, and the vendor states that its forecasts are generated independently of legal precedent, so the question sits outside what it sells. One feature is adjacent: Precedent Intelligence surfaces prior matters selected by compositional similarity rather than citation, and the vendor notes these often fall outside the scope of traditional research tools. No currency or good-law check is described for them.
Refusal and Uncertainty Behaviour
What does the product do when the answer is not in the corpus?
No located public material addresses what the product does when it cannot ground an answer.
Searched the home page, the platform page, the privacy policy and the footer on 31 Aug 2026. No explicit no-answer or abstention path is documented, and nothing states what the platform does when comparable cases are too few to support a forecast, which matters given that state court coverage is described only as select. Two features sit adjacent to this without meeting it. The vendor draws a published distinction between a prediction, offered only for motions to dismiss, and a forecast for the other nine motion types, which tells a user where confidence is lower. And output for the modelled motions is expressed as outcome likelihoods, grant rates and denial patterns rather than as a binary answer. Both are calibration disclosures rather than an abstention behaviour.
Fabricated Citation Record
Does a public court record exist involving output from this product?
No court order, opinion or disciplinary record naming this product has been located as of the date shown. This is a statement about the public record, not a finding about the product.
No court order, opinion or disciplinary record naming this product has been located as of 31 Aug 2026. Instrument searched: the AI Hallucination Cases database maintained by Damien Charlotin, which tracks decisions worldwide where a court addressed hallucinated AI content and records the tool implicated where known, searched on both the product name and the unspaced variant, alongside 2026 sanctions trackers, law firm commentary indexes and trade press summaries. This is a statement about the public record on the date shown rather than a clearance, and it is bounded by what that database covers. The product's output is a statistical forecast supported by real dockets the user can open rather than generated citations to authority, so the failure mode this signal tracks is not the one the product is exposed to.
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.
Searched the home page, the platform page, the privacy policy, the full primary navigation and the footer on 31 Aug 2026. No engagement with any bar or ethics guidance was located, including ABA Formal Opinion 512 and any state bar material. Nothing addresses the professional conduct dimension of the product at all, which is notable on a tool built to inform whether to move to dismiss, how to value settlement and which venue to choose, and one that models judicial behaviour by political affiliation. No terms of service exists in which such a statement could otherwise sit.
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.
Public materials are framed around cost control and predictability rather than time saved: more accurate litigation budgets, alternative fee arrangements and cost forecasting, clearer visibility into litigation risk and exposure, more competitive responses to requests for proposal, and optimised resource allocation. One feature is closer to this signal than most vendors get: reports can be assigned matter numbers to simplify cost tracking, which supports allocating the tool's cost to a client matter. Searched the same surfaces on 31 Aug 2026 and located no per matter record of AI-assisted work intended for fee purposes and no guidance on billing, fee or client disclosure treatment where a forecast informs advice.
Outside Counsel Guideline Readiness
Can a firm get this vendor through a client’s AI clause without a bespoke negotiation?
No located public material supports a client side disclosure obligation.
Searched the home page, the platform page, the privacy policy and the footer on 31 Aug 2026. Nothing that would support a client-side disclosure obligation was located: no subprocessor list, no model provider or infrastructure provider named, no trust centre, no data processing agreement, no named certification behind the SOC mark in the footer, and no client-facing consent or notification material. The privacy policy refers to service providers covering cloud hosting, analytics and payment processing under confidentiality terms, which acknowledges third parties in the chain without identifying any of them. A firm asked by a client which vendors see its matter information would find nothing to answer with.
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 sources behind an output are traceable and the AI provenance is not. The vendor commits that every output is inspectable down to the underlying docket, that each comparable case is visible, inspectable and linked to its docket, and that outcomes are verifiable, which means a lawyer can produce the real matters a forecast rests on. Reports can also be shared under the firm's own letterhead, which is a distribution feature rather than a disclosure record and arguably works against provenance by stripping the vendor's mark. Two elements are missing: no model is identified or versioned anywhere, so which system produced a given forecast cannot be established, and no export designed for a court disclosure or AI-use certification was located on 31 Aug 2026.