Covalynt vs Pattern Data: how they compare in 2026
Covalynt and Pattern Data both apply AI to large claimant populations, Covalynt scoring class action settlement claims for fraud and Pattern Data evaluating mass tort claimants against settlement criteria. Covalynt sits in the top two bands on seven of fifteen axes and Pattern Data on six of fifteen, identical on eight. Covalynt's lead is evidence and security. It lists matters by docket number, including cases against Apple and Oracle, states SOC 2 Type II with reports under a mutual NDA, and explains every score with deduction codes. Pattern Data's lead is its published oversight model: eligible claims are decided automatically and complex or outlier cases go to a person. Covalynt rejects claims below a fixed score and publishes no human review of those rejections. Its privacy policy, which covers its settlement services, allows personal information to be sold or shared with advertising partners. Pattern Data publishes no terms of service or customer agreement.
At a glance
All 15 axes, side by side
The same grid applied to every vendor in the index, graded from public sources. Hover a grade to see what the letter means on that axis.
AI Centrality
How much of the product is actually AI. Whether the machine learning is the mechanism the buyer is paying for or a feature layered onto conventional software, and whether the vendor is specific about which is which.
The models are the engine of the flagship product within a wider data engineering suite, which is the B band. ClaimScore's scoring runs on what the vendor calls a proprietary expert-system AI whose criteria are weighted by neural-network machine learning trained on cohorts of known valid and invalid claims, and every claim's score and rejection turns on it. The rest of the suite, ClassResolution's record reconciliation for certification, DeepValidation's contact enrichment for notice and bespoke data engineering, is described as data science and identity resolution rather than model-driven, and would function without the scoring models. Verified 18 September 2026.
The machine learning is the mechanism the buyer pays for. The product reads records, extracts exposure, injury and treatment data, scores every claimant against litigation criteria and auto-adjudicates eligible claims; remove the models and there is a docket spreadsheet. The company has sold nothing else since its founding and its litigation-specific models are the product it names. FAQ, platform page and home page read 6 September 2026.
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 scoring method and how accuracy is tested are described, with accuracy figures shown only inside an illustrative example, which is the B band. The scoring system page explains that each claim starts at 1,000 points, loses points on more than 65 weighted criteria, is tagged with deduction codes naming each failed criterion, and is rejected below 700, and that accuracy is measured through regular control studies with data split into learning and testing sets. The ROI page shows 99.5 percent valid-claim identification and 98 percent fraud identification accuracy, but inside a worked example case rather than as a labeled result with a sample, period and method, and the Accuracy Explained section was not reached. For this product the question is the reliability of its own eligibility scoring, which this row grades. Verified 18 September 2026.
Grounding is real and documented with sourcing, short of a first-party accuracy figure. The FAQ states the platform uses a retrieval-augmented generation approach over language models and that case reviewers validate AI-aggregated findings, the platform page states clear sourcing and human validation at every step and that every result can stand up to review, audit or settlement; a third-party review's ninety-eight percent accuracy figure is not credited. No first-party accuracy figure, test set or evaluation is published, and the primary-authority limbs do not apply to a record reader. FAQ, platform page and home page read 6 September 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.
Automated eligibility decisions are described with a threshold and reasons, but no human review position is published, which places this at C. The scoring system marks any claim that falls below 700 points as rejected, and the product page describes automated claims processing producing eligibility decisions, delivered in real time to claims systems through the API. Deduction codes give an administrator the reasons for each score, which is a real review surface. Nothing published says whether a person reviews rejected claims, whether rejection is final or a recommendation to the administrator, or whether a claimant flagged in error can cure, which matters because these decisions determine who in a class is paid. Verified 18 September 2026.
What runs alone, what a person must approve, and the threshold between them are published. Modes: the FAQ states that eligible claims are auto-adjudicated by the proprietary AI while complex or outlier cases are flagged for human review, and that case reviewers validate AI-aggregated findings; the home page states that every extraction, classification and score is built to support the team's review rather than bypass it. Threshold: eligibility and allocation logic set by the settlement criteria, recalculated when criteria change. Review surface: validated data, record status and what each case needs next, with sourcing on every result. Route back: flagged outliers go to a person. Nothing states a confidence level below which auto-adjudication is withheld beyond the criteria themselves. FAQ, platform page and home page read 6 September 2026.
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.
Named deployments are published without measured outcomes from them, which is the B band. The case experience page lists about twenty matters with docket numbers and courts, including Lopez v. Apple, Katz-Lacabe v. Oracle, Brooks v. Thomson Reuters, In re Broiler Chicken Antitrust Litigation and Kessler v. Quaker Oats, names some thirty federal, state and Canadian courts where the vendor has served, and describes its work as court-appointed neutral and consultant; the home and product pages add further matters, including mass torts. None of these is paired with a result, and the ROI page's savings figures come from an illustrative example case, so no outcome is tied to a named matter with a method. Verified 18 September 2026.
Scale claims, unattributed figures and unnamed court appointments stand in for named deployments with figures. The FAQ states more than thirty litigations and 1.4 million cases on the platform, court-appointed roles in major settlement programs, settlement submissions in one day rather than fifteen and review costs cut by up to five times, none attributed to a named firm or program; home-page testimonials are unnamed; the chief executive's conference biography names the 3M Combat Arms Earplug and Philips CPAP litigations as deployments without figures. FAQ, home page and conference biography read 6 September 2026.
Privilege and Confidentiality Posture
How client confidences are handled: attorney client privilege and work product treatment, segregation of one client matter from another, whether client data trains any model, and what the vendor commits to in writing rather than in marketing.
Confidentiality rests on an audited control set while the only published policy covering the Services permits selling or sharing personal information, which places this at C. The security page says the SOC 2 Type II examination covers the Confidentiality trust services criterion and describes role-based access controls. The privacy policy, which states that it applies to the vendor's class action settlement services, says that identifiers such as name, postal address and email, along with commercial and online activity information and inferences, will be sold or shared with advertising and analytics partners, and permits de-identified use to improve the Services. No customer agreement setting confidentiality terms for class member data is published, and privilege and work product are not addressed. Verified 18 September 2026.
Confidentiality is addressed at the level of general assurance. The FAQ states TLS encryption, HIPAA-compliant environments for protected health data, multi-factor authentication, stringent access controls and staff training; no customer agreement is published, so nothing binds a training position, and no statement on training use, retention, deletion, matter or docket segregation, third-party model providers, or privilege and work product was located. FAQ and platform page read 6 September 2026; the security page was not opened and is the rebuttal route.
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.
Nothing published on the product's role relative to legal judgment. Searched the home, product, scoring, ROI and security pages, the Terms of Use and the Privacy Policy on 18 September 2026. The product produces claim eligibility decisions presented as able to withstand court scrutiny, but no statement addresses who is responsible for those determinations, whether they constitute advice to counsel or the court, or what counsel and administrators must verify. The published terms govern the website only. Verified 18 September 2026.
No advice line or supervision statement was located. The product is sold to law firms and to court-appointed administrators, and the home page states the AI amplifies rather than replaces human judgment, which is a design statement 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 to carry such a statement. Home page, FAQ and platform page read 6 September 2026.
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.
A testing practice is described without a governance framework or any disclosure on uneven impact, which places this at C. The scoring system page says accuracy is measured through regular control studies, with criteria weighted by machine learning on known valid and invalid claims and separate learning and testing sets, which is evidence of how the vendor tests. Nothing names an accountable owner or governance process, and nothing addresses whether fraud criteria such as shared contact attributes or name variations flag some groups of legitimate claimants more than others, which is the bias question a court or class counsel would ask. Verified 18 September 2026.
A design principle without a governance framework, testing regime or accountable owner. The home page states that the AI does not replace human judgment and that every output supports review, and the FAQ describes human-in-the-loop validation; no responsible AI framework, ISO 42001 or equivalent, pre-release testing results or statement about uneven output across litigations or record types is published on the surfaces read. Home page and FAQ read 6 September 2026.
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.
Security practices are listed while the data terms are generic and permissive, which places this at C. The security page describes encryption in transit and at rest, role-based access, WAF and DDoS protection, backups, disaster recovery, penetration testing, vendor risk management and an incident response framework. The privacy policy keeps personal information as long as reasonably necessary, names no subprocessors beyond categories, sets no incident notification timeline, and permits sale or sharing of personal information with advertising and analytics partners. No retention or deletion commitment for customer claim files is published. Verified 18 September 2026.
Some of the ground is covered. Access control: TLS in transit, HIPAA-compliant storage for protected health data, multi-factor authentication, stringent access controls and continuous staff training, with adherence to SOC 2 and regular audits stated. Not located: a retention period, a deletion commitment, a sub-processor list or an incident-notification practice; no customer agreement is published and the security and privacy pages were not opened and are the rebuttal route. FAQ read 6 September 2026.
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 published on liability for the product. Searched the Terms of Use, Privacy Policy, home, product and security pages on 18 September 2026. The Terms of Use govern the website and refer product use to a separate license agreement and ordering documents, which are not published; no warranty, indemnity, cap or insurance position for the scoring services was located. Verified 18 September 2026.
No liability position is published. The site's page inventory, taken from the navigation and footer on 6 September 2026, carries Privacy, Security, Careers and an AI-search page and no terms of service or customer agreement; the FAQ describes pricing as tailored per firm, which places the agreement in a negotiated document the vendor does not publish. This is an absence on the vendor's surfaces rather than a retrieval limit. FAQ and footer read 6 September 2026.
Practice Systems Integration Depth
How deeply the product reaches into the systems legal work already lives in: document management such as iManage and NetDocuments, Word and Outlook, contract lifecycle management, matter management, e-billing, and court filing systems.
The integration paths are described with what they move, short of documentation read, which is the B band. ClaimScore connects in real time through an API key to an administrator's digital claim form and sends results directly to claims or client management systems, and a retro review path takes bulk claim files by secure transfer and returns result files with match and validation fields. The developer page describes per-case API keys that keep each case's claim data separate, webhooks returning a score, determination and deduction codes for each claim, usage alerts, validation status updates, a mock server, and sandbox, staging and production environments. The API reference itself sits behind a login at docs.claimscore.ai, so it could not be read. No specific claims administration or case management system is named. Verified 18 September 2026.
Real integrations with depth described for one. The navigation carries an Integrations page and a dedicated Litify page, the FAQ states an open API with customizable scripts for automated actions, bulk imports from cloud storage systems and drag-and-drop upload, and the FAQ describes integration with existing case management systems; the platform page states that settlement packets are generated in the formats claims administrators require. The Integrations and Litify pages were not opened, so what syncs with Litify and in which direction is not recorded. FAQ, navigation and platform page read 6 September 2026.
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 described without a named host, tenancy or region, which is the C band. The security page refers to trusted cloud partners for redundancy and the privacy policy says the company and its sites are based in the United States for U.S. users, but no hosting provider, data location or tenancy model for claim data is stated. Verified 18 September 2026.
Cloud delivery is stated and neither tenancy nor region is addressed. The FAQ states storage of protected health data in secure HIPAA-compliant environments and bulk import from cloud storage, and no hosting provider, region, residency option or tenancy model is named on the surfaces read; the security page was not opened and is the rebuttal route. FAQ read 6 September 2026.
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 named attestation with scope and a request route, short of auditor and period, which is the B band. The security page states SOC 2 Type II examination by an independent AICPA-accredited firm covering the Security, Availability and Confidentiality trust services criteria, with current reports available on request under a mutual NDA, and lists penetration testing and vulnerability assessment. The auditor and report period are not named. Verified 18 September 2026.
A standard is referred to without an attestation on the surfaces read. The FAQ states adherence to SOC 2 compliance and regular comprehensive security audits and full HIPAA compliance, which is a compliance claim rather than a statement that a SOC 2 report has been issued; no report type, auditor, coverage period or route to a report is stated, and the Security page in the footer was not opened and is the rebuttal route to B. FAQ read 6 September 2026.
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 model architecture is described as built in-house, without versions or change notification, which is the B band. The vendor says it owns its own data and builds its own models, and describes ClaimScore as a proprietary expert system of more than 65 criteria with weights learned by neural-network machine learning on cohorts of known valid and invalid claims. No third-party model provider is indicated, no model version or update cadence is published, and nothing commits to telling customers when scoring criteria or weights change mid-case. Verified 18 September 2026.
The vendor describes its architecture without identifying what sits underneath. The FAQ states a retrieval-augmented generation approach and large language models, and the litigation pages describe models tailored to specific litigations; no provider, model, inference location or change-notification commitment is named on the surfaces read. The security page is the rebuttal route. FAQ and platform page read 6 September 2026.
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.
Cost is described only relative to value, without a rate or unit, which places this at C. The ROI page says the cost of ClaimScore averages under five percent of the amount saved from fraudulent claims regardless of case type, and walks through an example case, but no price, unit or pricing model is published and buying runs through a demo request. Verified 18 September 2026.
The unit and structure are stated without the figure. The FAQ describes a transactional pricing model with fees based on case volume and the specific services used, naming case analysis, settlement award allocation and settlement packet generation as priced services, tailored per firm to caseload and budget, and claims review fees cut by up to five times against manual review; no figure, rate card or pricing page is published. FAQ read 6 September 2026.
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 segment and the litigation stages covered are described with substance, short of stated limits, which is the B band. The vendor serves settlement administrators, class counsel, defense counsel and courts across class actions, mass torts and mass arbitrations, covering class certification data, notice, claims processing and payment, and lists consumer, privacy, antitrust, securities and pharmaceutical matters. It does not state case sizes, claim types or jurisdictions it handles less well. Verified 18 September 2026.
Segment and coverage are described with substance and the boundary is the product's stated scope. Buyers are plaintiff firms, defense firms, settlement administrators and special masters; the litigation pages name Roundup, AFFF and PFAS, Camp Lejeune, Depo-Provera, hair relaxer, GLP-1, Paraquat, social media, talc and Zantac, with more than thirty litigations supported; the vendor states the platform is purpose-built for mass tort and multidistrict litigation, which is the limit. No jurisdiction outside the United States is addressed. Navigation, FAQ and platform page read 6 September 2026.
The 12 legal signals, side by side
Recorded rather than graded. These are the questions a practitioner has to answer before a tool touches a client matter, and the answers are taken from public material only.
Client Data in Training
Can material a lawyer puts into this product be used to train a model?
No customer agreement is published; the privacy policy covering the Services permits use bounded to improving them and never names training. It allows personal information in de-identified or aggregate form to be used to improve the Services, and the scoring system page says criterion weights are learned by machine learning on cohorts of known valid and invalid claims without saying whose claims. Nothing states whether a customer's claim files are used to train or reweight the models.
No customer agreement is published and no training statement was located. The site's page inventory on 6 September 2026 carries Privacy, Security, Careers and an AI-search page and no terms of service; the FAQ addresses encryption, HIPAA environments, access control and audits without stating whether customer records train any model, and the litigation pages describe models tailored per litigation without saying on what. The privacy and security pages were not opened and are the rebuttal route. Surfaces 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?
Retention is acknowledged without a stated period. The privacy policy keeps personal information as long as reasonably necessary for the purposes collected, including legal, accounting and dispute purposes. No retention or deletion terms for customer claim files are published.
No located public material addresses how long records, extractions or settlement data are retained. The FAQ addresses storage security without a period, no customer agreement is published, and the privacy and security pages were not opened and are the rebuttal route. Surfaces checked 6 September 2026.
Ethical Walls and Matter Segregation
Does retrieval respect the firm’s ethical walls, or can the model read across them?
Role-based access is asserted without detail on how case data is separated. The security page says role-based access controls limit users to the data and systems their roles require. Nothing documents separation between matters, administrators or opposing parties sharing the platform.
Segregation is claimed without documentation of a permission model. The FAQ states multi-factor authentication and stringent access controls, and the platform holds each firm's docket as one live inventory; nothing describes how one firm's docket is walled from another's, how a court-appointed administrator's view is separated from the firms whose claimants it adjudicates, or how the models respect those boundaries. Surfaces 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?
Disclosure in response to legal process is addressed and customer notice is not. The privacy policy permits sharing personal information to respond to subpoenas, court orders, law enforcement or government requests, with no commitment to notify the customer.
No located public material addresses whether the customer is told when its data is demanded by a third party. No customer agreement is published, and the privacy page was not opened and is the rebuttal route. Surfaces 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?
Searched the home, product, scoring, ROI and security pages and the privacy policy on 18 September 2026. The vendor refers to its own proprietary data used to enrich and validate claims but names no data source or license; no legal research corpus is involved.
No located public material identifies a legal corpus behind the product's output, and the product is not built on one: it extracts facts from the customer's claimants' records and scores them against litigation and settlement criteria the firm or administrator supplies, citing no law. FAQ and platform page checked 6 September 2026.
Good Law Verification
Does the product tell you when the authority it just cited has been overruled?
Searched the same surfaces on 18 September 2026. The product does not cite legal authority, so no subsequent-history check arises and none is described.
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 case scores, valuations and settlement packets. Recorded as the honest value for a product without a citator function. Surfaces checked 6 September 2026.
Refusal and Uncertainty Behavior
What does the product do when the answer is not in the corpus?
Every output carries a score and reasons, with a fixed threshold rather than an abstention path. Claims start at 1,000 points, lose points on failed criteria tagged with deduction codes, and are marked rejected below 700. Nothing describes a middle band referred for human review or how the system handles claims it cannot assess.
An explicit path for cases the model does not decide is described: the FAQ states that eligible claims are auto-adjudicated while complex or outlier cases are flagged for human review, and that the platform flags missing documentation rather than filling gaps. The behavior is described rather than demonstrated, and the criteria that make a case an outlier are not stated. FAQ checked 6 September 2026.
Fabricated Citation Record
Does a public court record exist addressing fabricated or hallucinated legal citations in output from this product?
Searched the AI Hallucination Cases database maintained by Damien Charlotin and trade press reporting on 18 September 2026 for court records addressing fabricated or hallucinated content in output from Covalynt or ClaimScore. None located. This signal does not record litigation history of any other kind, including rulings on claims in settlements where the product was used.
No court order, opinion or disciplinary record naming Pattern Data was located as of 6 September 2026. The AI Hallucination Cases database maintained by Damien Charlotin was searched on the name together with a general search for court findings on mass-tort settlement administration; results returned directory entries, job listings and sanctions involving general-purpose chatbots, none of which names this product. This is a statement about the public record, not a finding about the product; a tool whose outputs feed court-supervised settlement adjudication carries exposure on fabricated facts rather than citations, and the check is worth repeating at re-verification.
Bar Guidance Alignment
Has the vendor engaged in public with the ethics opinions its buyers are bound by?
Searched the home, product and security pages, the insights listing and the policies on 18 September 2026. The vendor references Rule 23 duties and judicial scrutiny of settlements, which are procedural law, but names no bar ethics opinion or court rule on AI.
No located public material names an ethics opinion, bar rule or professional responsibility framework. The vendor's material addresses court-appointed adjudication and settlement compliance, which are procedural, and no guidance from any bar or regulator on lawyers' use of AI is named on the surfaces read. FAQ and platform page 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?
Savings are claimed for settlement funds and class members, without billing or disclosure guidance. The ROI page says the product's cost averages under five percent of the amount saved and increases payouts in common fund cases, and the home page pitches protection of class counsel's fee award. Nothing addresses how the cost is borne or disclosed to the court or class.
Law firms are the primary buyer and the published position on the bill is a savings claim: case review fees cut by up to five times, settlement submissions in one day rather than fifteen. Nothing addresses how AI-assisted review is recorded or disclosed on a client's bill or in a contingency settlement, or how a court-appointed administrator's AI adjudication cost is allocated. FAQ checked 6 September 2026.
Outside Counsel Guideline Readiness
Can a firm get this vendor through a client’s AI clause without a bespoke negotiation?
Searched the security page, the privacy policy and the product pages on 18 September 2026. No subprocessor list, hosting provider or data processing agreement is published; the security page refers only to trusted cloud partners and vendor risk management.
No sub-processor list, model provider list or forwardable disclosure material was located. The FAQ describes retrieval-augmented generation over language models without naming a provider, no customer agreement or DPA is published, and the security and privacy pages were not opened and are the rebuttal route. Surfaces checked 6 September 2026.
Court Disclosure Support
If a judge’s standing order requires an AI disclosure, can the product produce one?
A per-claim record of the automated decision is described, short of model identification or a human verification step. The product page offers case-ready explanations documenting every flag, scoring input and eligibility decision, deduction codes explain each score, and DeepValidation documents each step in a reproducible process. Nothing records the model or criteria version applied to a claim or who reviewed the decision.
Some elements of a disclosure record are available and no export of an AI-use record is described. The platform page states clear sourcing and human validation at every step and that every result can stand up to review, audit or settlement, and the product generates settlement packets in administrator-required formats from validated data, which is a per-claimant record of what was extracted and checked; nothing states that a record of the model used and the reviewer's verification can be exported for a court, and court-appointed adjudication is performed by the vendor under the court's program rather than certified by the firm. Platform page and FAQ 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.
- AI Liability and Recourse
- Primary Law Corpus Provenance
- Good Law Verification
- Bar Guidance Alignment
- Outside Counsel Guideline Readiness
Which one fits
Choose Covalynt if
- Your problem is fraudulent claims in a class settlement. Covalynt's ClaimScore starts each claim at 1,000 points, deducts on more than 65 weighted criteria covering bots, synthetic identities, duplicates and solicitation schemes, and tags each deduction with a code explaining it.
- You need class member data for certification and notice. Covalynt's ClassResolution reconciles defendant records into audit ready class data, and DeepValidation validates and enriches contact data for notice.
- Your security review needs a report. Covalynt states SOC 2 Type II covering security, availability and confidentiality, available under a mutual NDA, and its API uses per case keys that keep each case's claim data separate.
Choose Pattern Data if
- Your problem is evaluating a mass tort docket against settlement criteria. Pattern Data screens exposure and injury, develops valuation from full records, calculates settlement points and builds submission packets in the formats claims administrators require, recalculating as criteria change.
- You want outliers kept for a person. Pattern Data states that eligible claims are auto adjudicated while complex or outlier cases are flagged for human review, and that case reviewers validate AI findings.
- Your firm runs Litify or needs an open API. Pattern Data lists a Litify integration and an open API with scripts for automated actions, and prices transactionally by case volume and service.
In summary
Covalynt
Covalynt, formerly ClaimScore and operated by ClaimScore LLC of St. Petersburg, Florida, is a data science platform for class actions, mass torts and mass arbitrations. ClaimScore scores each settlement claim for fraud on more than 65 weighted criteria with explained deductions, ClassResolution reconciles class member data for certification, and DeepValidation validates contact data for notice. The AI Legal Index grades it in the top two bands on seven of fifteen capability axes. It lists matters by docket number, states SOC 2 Type II and builds its own models. As of 18 September 2026 the index located no customer agreement, retention period or price.
Pattern Data
Pattern Data, based in Charlotte, North Carolina, is an AI case evaluation platform for mass tort litigation, sold to plaintiff and defense firms, settlement administrators and special masters. It screens, develops and settles whole dockets against litigation criteria, with tailored models for litigations including Roundup, AFFF, Camp Lejeune and Depo-Provera, auto adjudicating eligible claims and flagging outliers for review. The AI Legal Index grades it in the top two bands on six of fifteen capability axes, with A grades on AI centrality and oversight. It states more than 1.4 million cases on the platform and integrates with Litify. As of 6 September 2026 the index located no customer agreement, named model or price figure.
Questions buyers ask
Covalynt vs Pattern Data: which is better for mass claims work?
They address different problems: Covalynt scores class action settlement claims for fraud and prepares class data, while Pattern Data evaluates mass tort claimants against settlement criteria. On the AI Legal Index Covalynt sits in the top two bands on seven of fifteen capability axes and Pattern Data on six of fifteen, identical on eight. Covalynt publishes more evidence and a security report route; Pattern Data publishes a clearer oversight model.
Does a person review claims these tools reject?
Pattern Data states that eligible claims are auto adjudicated and complex or outlier cases are flagged for human review. Covalynt marks any claim scoring below 700 as rejected and explains each deduction with a code, but publishes nothing on whether a person reviews rejections or whether a claimant can cure. Graded by AI Legal Index against 15 capability axes and 12 legal signals, including privilege handling and citation accuracy, from each vendor's own published materials, verified September 27, 2026. No vendor pays for placement.
How do Covalynt and Pattern Data handle claimant data?
Covalynt states SOC 2 Type II and role based access, but its privacy policy, which applies to its settlement services, permits selling or sharing personal information with advertising and analytics partners. Pattern Data states HIPAA compliant storage, encryption and multi factor authentication, and publishes no customer agreement or retention position. Graded by AI Legal Index against 15 capability axes and 12 legal signals, including privilege handling and citation accuracy, from each vendor's own published materials, verified September 27, 2026. No vendor pays for placement.
How are Covalynt and Pattern Data priced?
Neither publishes a rate. Pattern Data prices transactionally by case volume and by service, naming case analysis, settlement allocation and packet generation. Covalynt says its cost averages under five percent of the amount saved from fraudulent claims, with buying through a demo request. Graded by AI Legal Index against 15 capability axes and 12 legal signals, including privilege handling and citation accuracy, from each vendor's own published materials, verified September 27, 2026. No vendor pays for placement.
What do Covalynt and Pattern Data both leave unpublished?
A liability position, a retention period and a bias disclosure. Neither publishes terms on who bears the loss when a claim is wrongly scored, neither states how long claimant records are kept, and neither addresses whether its criteria affect some groups of legitimate claimants more than others. Graded by AI Legal Index against 15 capability axes and 12 legal signals, including privilege handling and citation accuracy, from each vendor's own published materials, verified September 27, 2026. No vendor pays for placement.
Three readings to weigh. Covalynt's published accuracy figures appear only inside an illustrative example, its terms cover only its website, and nothing states whether a person reviews claims it rejects. Pattern Data publishes no terms of service or customer agreement, describes SOC 2 as adherence rather than a stated report, and its security and privacy pages were not read by this index. Covalynt was verified on 18 September 2026 and Pattern Data on 6 September 2026. Neither vendor reviewed this page.
Neither vendor paid for inclusion, placement or a grade, and neither reviewed this page before it published. Everything above comes from public material on the dates shown. How the index grades.