CAMBRIDGE, Mass., Sept. 16, 2026 /PRNewswire/ — Nona Biosciences (“Nona”), a global biotechnology company advancing biologics discovery and development through innovative technology platforms, today announced the development of HCAbLM, the world’s first language model specifically trained on fully human heavy-chain-only antibodies (HCAbs). HCAbLM was built on a large-scale repertoire comprising 31.8 million fully human HCAb sequences from 73 independently immunized HCAb transgenic mice. The study, “A foundation model learns the sequence and functional grammar of fully human heavy-chain-only antibodies,” was published in bioRxiv.

From Binder to Drug Candidate: A Leap in AI Antibody Development

The main bottleneck in the current AI antibody field is no longer “whether an antibody that binds the target can be designed,” but rather “whether the designed antibody can be manufactured at scale, whether it will aggregate, and whether it is stable”—that is, developability. HCAbLM was created precisely to address this issue. The model has learned the “sequence grammar” unique to fully human HCAbs—a set of underlying rules that determine whether an antibody can fold, exist stably, and ultimately become a drug. Conventional general-purpose models cannot capture this set of rules because their training data contain almost no HCAb sequences. HCAbLM fully demonstrates that a dedicated model trained on unique antibody sequence data can better translate “sequence space cognition“ into “developability prediction.“

A Dedicated HCAb Model Outperforms General-Purpose Protein and Antibody Models

The representations learned by HCAbLM demonstrate transferability to experimentally measured antibody properties, including size exclusion chromatography (SEC) purity, hydrophobic interaction chromatography (HIC) behavior, and thermal stability, highlighting HCAbLM’s potential in the evaluation and optimization of fully human HCAbs, going beyond mere sequence analysis.

In public benchmarks for cross-project developability prediction, HCAbLM, with only 366 million parameters, surpassed general-purpose protein large models including Meta’s ESM-6B with 6 billion parameters, as well as previously leading antibody language models IgLM and AbLang, on two core metrics: SEC purity and HIC behavior.

The development of HCAbLM marks an important advancement in Nona’s application of artificial intelligence (AI) to antibody discovery and development. By combining large-scale antibody repertoires with AI-driven modeling, Nona is building a foundation for more efficient and data-driven antibody design and developability assessment.

“The development of HCAbLM represents an important step in our efforts to apply AI to specialized antibody formats and unlock the value of large-scale antibody repertoires,” said Dr. Di Hong, Chief Executive Officer of Nona Biosciences. “By learning the unique sequence characteristics of fully human HCAbs and demonstrating transferability to experimentally measured molecular properties, HCAbLM provides a new foundation for AI-enabled antibody discovery and development. We look forward to further integrating HCAbLM with Nona’s technology platforms, and leveraging these capabilities to accelerate antibody discovery and development while empowering our partners to unlock new opportunities for next-generation biotherapeutics.”

About Nona Biosciences

Nona Biosciences, a wholly owned subsidiary of HBM Holding Limited (HKEX:02142), is a global biotechnology company committed to advancing biotherapeutic innovation through cutting-edge technology platforms and integrated solutions. Nona supports programs with its I to I® framework, from early-stage ideas through preclinical research and advancement toward IND and early clinic, spanning target validation and antibody discovery to early development.

Nona’s proprietary Harbour Mice® technology platform generates fully human monoclonal antibodies in classical two light and two heavy chain (H2L2) format, and heavy chain-only (HCAb) format. The HCAb Harbour Mice® is the world’s first fully human HCAb transgenic mouse with clinical validation. This unique platform offers exceptional versatility for diverse applications using fully human VH single-domain antibodies as a plug-and-play system, including bispecific antibodies, multi-specific antibodies, CAR-T therapies, antibody-drug conjugates (ADCs), mRNA-based therapeutics, and more.

By integrating Harbour Mice®, single-B cell screening technology, NonaCarFx™ (a direct CAR-function-based screening platform), Hu-mAtrIx™ (an AI-driven drug discovery platform), Modalities-on-Demand® (a next-generation modalities solution), and end-to-end preclinical drug development services, Nona Biosciences is dedicated to driving the global invention of transformative next-generation drugs. For more information, please visit: www.nonabio.com.

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SOURCE Nona Biosciences

CHISINAU, Moldova, Sept. 16, 2026 /PRNewswire/ — Digital credentials from EVO, Moldova’s integrated government app, are now accepted by four of the country’s banks: Moldindconbank, FinComBank, EuroCreditBank and Victoriabank, allowing clients to identify themselves through EVO. This marks a broader expansion across the public and private sectors, as Moldova moves to make secure digital identity a standard way to access services nationwide while aligning with Europe’s emerging digital identity ecosystem. Developed in line with the European Digital Identity Wallet framework, EVO is being built to support secure, consent-based and interoperable digital identity transactions, including future cross-border use.

The integration turns the digital documents already available in EVO into reusable credentials for everyday interactions with private-sector services. Identity is confirmed by scanning a QR code and choosing to share the information the bank requests. The verified data is transferred digitally, eliminating the need to present or photocopy a physical identity document and reducing manual data entry.

The eGovernance Agency opened EVO integration to public and private organizations on April 1, 2026. Since then, the ecosystem has expanded beyond the first banking integrations, with 30 relying parties at different stages of connection, including one of Moldova’s largest telecommunications operators and the National Bank of Moldova. At least 20 organizations are expected to be operationally integrated by the end of 2026.

“Digital credentials become valuable when people can actually use them in real-life scenarios, and we’re aiming for ubiquity. Banks are among the institutions where trusted identification matters most, so bringing EVO into everyday banking is an important milestone. Our next step is to expand this ecosystem across both the public and private sectors, while ensuring that what we build in Moldova is compatible with the digital identity infrastructure being developed across Europe,” said Nicoleta Colomeeț, Director of the Moldova eGovernance Agency.

From national rollout to European interoperability

Moldova is developing EVO Wallet in line with European Digital Identity Wallet standards, while contributing its experience in digital credentials to their implementation, testing and cross-border interoperability. As a member of the WE BUILD consortium, Moldova works alongside European partners to validate digital identity credentials and help identify and resolve differences between national implementations. EVO has successfully tested its digital ID in the European PID format and its digital driving licence in the ISO mDL format with partners from France, Hungary, Austria and Belgium, while further interoperability testing in Moldova brought together teams from France, the Netherlands, Croatia, Romania and Ukraine.

For Moldova, an EU candidate country, digital identity is part of a broader transition toward European digital infrastructure. Following integration into initiatives such as SEPA and “Roam Like at Home”, the development of an interoperable digital identity wallet extends that process to how citizens identify themselves and securely share verified information.

The Moldova eGovernance Agency leads the development of Moldova’s digital public infrastructure and the transformation of public services. It develops and operates the national digital platforms that let citizens, businesses and public institutions interact securely online. Its work supports Moldova’s European integration and transition to fully digital public services. EVO test certificates and the integration guide are available on the egov4dev public integration page.

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SOURCE Moldova eGovernance Agency

Nunery advocates for AI systems that evaluate Certificates of Insurance against an organization’s specific requirements, reason across connected documents, produce traceable decisions, and preserve human oversight

Key Takeaways

Grounding AI in an organization’s specific insurance requirements is essential, but it is only part of what reliable certificate of insurance compliance requires.AI systems must reason across contracts, certificates of insurance, endorsements, schedules, amendments, and exceptions to evaluate the complete body of evidence.Accurate document extraction does not guarantee an accurate compliance decision because compliance depends on the relationships among requirements and supporting documents.Every AI-supported compliance determination should be traceable, explainable, and subject to human approval so it can withstand an audit, dispute, or claim.Organizations evaluating grounded AI compliance platforms should measure both false passes that leave risk uncovered and false failures that create unnecessary work.

INDIANAPOLIS, Sept. 16, 2026 /PRNewswire/ — Kristen Nunery, CEO of illumend and founder of myCOI, is calling on business leaders to demand more from grounded artificial intelligence (AI) in Certificate of Insurance (COI) compliance. Nunery details her point of view in a new article titled “Grounding Is Half the Answer in COI Compliance.” The issue affects organizations that rely on vendors, contractors, tenants, franchisees, and other third parties to maintain contractually required insurance coverage, including businesses in construction, commercial real estate, retail and franchising, and government.

Grounded AI is an approach that bases its outputs on defined, authoritative information rather than relying solely on the general knowledge acquired during model training. In COI compliance, that information includes an organization’s contracts, leases, insurance requirements, vendor classifications, required endorsements and approved exceptions.

Nunery’s position is that grounding AI in an organization’s insurance requirements provides a standard for evaluating compliance, but it does not provide sufficient evidence to make a reliable decision. AI systems must also connect information across contracts, certificates, endorsements, schedules, and amendments—and explain how that evidence supports each determination.

Modern AI can accurately extract policy numbers, coverage limits, carriers, and expiration dates from certificates. The distinction, Nunery argues, is that document extraction determines what an insurance document says, while compliance analysis determines whether the full body of evidence satisfies the requirements for a specific vendor, contract, project, or business relationship.

“COI compliance is a document-relationship problem before it is a document-reading problem,” Nunery said. “An AI system can read every individual document correctly and still reach the wrong conclusion if it cannot understand how the agreement, certificate, policy, endorsement, and schedule relate to one another.”

What Grounded AI Means for COI Compliance

Grounded AI is anchored to a defined source of truth rather than relying on general information absorbed during model training. In COI compliance, the relevant source of truth includes an organization’s contracts, leases, insurance requirements, vendor classifications, project standards, required endorsements, and approved exceptions.

Grounding changes the question the system answers. An ungrounded system may assess whether a vendor appears adequately insured, even though “adequately insured” has no universal definition. A grounded system evaluates whether submitted evidence satisfies the requirements governing that specific third-party relationship.

The key difference is that grounded AI evaluates insurance evidence against requirements established by the organization rather than relying on a generalized model-generated standard of adequate coverage.

“Grounding gives the AI the standard instead of asking it to invent one,” Nunery said. “A system that cannot tell you where its definition of compliant came from does not really have one.”

Why Is Grounded AI Only Half the Answer?

Even a system perfectly grounded in an organization’s requirements can reach the wrong decision if it fails to connect all relevant evidence.

For example, a certificate may indicate that additional insured coverage is present while an attached endorsement covers only ongoing operations. If the governing agreement also requires completed operations coverage, approving the vendor based solely on the certificate could leave the organization exposed if a claim arises after the work has ended.

Every document in that example could be read accurately. The incorrect decision would result from failing to connect the certificate and endorsement with the requirement established by the contract.

For businesses, this means accurate extraction is necessary but insufficient. A reliable COI compliance system must determine how information in one document changes, supports, limits, or contradicts information in another.

“Cross-document reasoning tells the system what the complete evidence means, while grounding tells it which requirements that evidence must satisfy,” Nunery said. “Remove either one, and the resulting determination is a guess wearing a confident tone.”

Nunery advocates for grounded AI systems that evaluate related insurance documents as one body of evidence. A system should determine whether a required endorsement exists, applies to the correct policy, names the appropriate legal entity or project, and satisfies the governing contract. It should also recognize when amendments, schedules, project requirements, or approved exceptions change the compliance decision.

In practical terms, grounding establishes the compliance standard, while cross-document reasoning determines whether the available evidence actually meets that standard.

Why Traceable AI Decisions Matter in COI Compliance

Nunery argues that every AI-supported compliance determination should identify which requirement applied, which documents were evaluated, and why the system reached its conclusion.

“A compliance determination that cannot be reconstructed is not defensible, however confident the system sounded when it made it,” Nunery said. “Auditability should be treated as a fundamental requirement for grounded AI, not an optional reporting feature.”

Traceability becomes critical during audits, contractual disputes, and claims. A confidence score or pass-fail label does not establish that the system applied the correct agreement, reviewed the relevant endorsements, considered approved exceptions, or evaluated the complete evidence.

A traceable COI compliance decision should allow a reviewer to reconstruct the path from the governing requirement to the supporting documents and the resulting determination.

Human oversight must also remain part of the process. AI can accelerate reviews and identify potential deficiencies, but a person should approve a determination before a third party is declared compliant—particularly when evidence is incomplete, policy language is unusual, or an exception applies.

Seven Questions to Ask a Grounded AI Compliance Vendor

Nunery recommends focusing less on the underlying AI model and more on the requirements, evidence, and controls surrounding it. Buyers should ask:

Who defines “compliant” in the system?Are requirements configured before the system operates at scale?Does the system connect certificates with contracts, endorsements, and schedules?Can it distinguish missing evidence from deficient coverage?Is every determination supported by documented reasoning?Does a person approve the decision before a vendor is declared compliant?Which capabilities are available today rather than planned for the future?

Nunery also recommends measuring both false passes and false failures. A false pass leaves uncovered risk in an organization’s portfolio, while a false failure creates unnecessary work by prompting teams to pursue vendors that were not out of compliance.

The two measures reveal different types of system error: false passes indicate potential risk exposure, while false failures indicate unnecessary operational work and third-party friction.

“A system that reports only one of those numbers is telling you half of what happened,” Nunery said.

How illumend Uses Grounded AI for COI Compliance

illumend is an AI-powered third-party insurance compliance and risk management platform backed by myCOI’s institutional data foundation. Nunery founded myCOI more than 16 years ago, and the company’s experience includes reviewing 45 million documents, managing 1.2 million agreements, and identifying 2 million coverage gaps before they became claims.

During onboarding, illumend configures an organization’s insurance requirements based on standards derived from its contracts and leases. The platform then evaluates submitted evidence against the defined requirements, reviews endorsements with each submission, identifies specific deficiencies, manages renewals, and documents the reasoning behind each determination. A person signs off before a third party is notified of its compliance.

The workflow is designed to connect four components of a compliance decision: the organization’s requirements, the submitted insurance evidence, the reasoning used to evaluate that evidence, and human approval of the final determination.

“Grounding AI in an organization’s requirements is the right place to start, but it is only half the answer,” Nunery said. “Risk and compliance leaders should demand systems that understand the complete compliance picture, apply the correct requirements consistently, and produce decisions that can withstand scrutiny when an audit, dispute, or claim occurs.”

Frequently Asked Questions

How should an organization prepare its insurance requirements before implementing grounded AI for COI compliance?

Organizations should first document the requirements contained in their contracts and leases, including differences by vendor type, project, division, and jurisdiction. They should also identify required endorsements and approved exceptions. The goal is to establish a defined source of truth before AI begins making compliance evaluations. During onboarding, illumend configures each organization’s requirements so the platform evaluates evidence against its actual standards rather than assumptions generated by an AI model.

How should grounded AI distinguish missing insurance evidence from deficient coverage?

Grounded AI should treat missing evidence and deficient coverage as separate compliance states. Missing evidence means the system lacks the documentation needed to complete an evaluation; deficient coverage means the available evidence does not satisfy a defined requirement. The distinction matters because missing documentation does not, by itself, prove that the underlying coverage is deficient. The illumend platform identifies deficiencies with sufficient specificity for third parties to understand what needs to be corrected, without treating every documentation gap as proof of noncompliance.

What records should an AI-powered COI compliance platform maintain for audits and claims?

An AI-powered compliance platform should record the applicable requirement, the documents evaluated, the deficiencies identified, and the reasoning supporting each determination. Those records help an organization reconstruct how a decision was made during an audit, contractual dispute, or claim. illumend maintains requirements, documents, deficiencies, renewals, resolution activities, and decision reasoning within a single connected workflow.

How can risk leaders determine whether a grounded AI compliance platform is working accurately?

Business leaders should evaluate both false passes and false failures rather than rely on a single accuracy percentage. False passes leave potential coverage gaps in the organization’s portfolio, while false failures create unnecessary work and vendor friction. A meaningful accuracy assessment should therefore measure both risk-related errors and operational errors. illumend recommends assessing whether a platform consistently applies the correct requirements, evaluates the complete evidence, and produces decisions that people can review and reconstruct.

What is the difference between grounded AI and cross-document reasoning in COI compliance?

Grounded AI establishes the requirements an insurance submission must satisfy, while cross-document reasoning evaluates how contracts, certificates, endorsements, schedules, amendments, and exceptions work together as evidence. Reliable COI compliance requires both: grounding defines the standard, and cross-document reasoning determines whether the evidence meets it.

Why is document extraction alone not enough for COI compliance?

Document extraction identifies information such as policy numbers, limits, carriers, and expiration dates, but compliance depends on how that information relates to contractual requirements and other insurance documents. A system can extract every field correctly and still make an incorrect compliance determination if it fails to evaluate those relationships.

To read Nunery’s full article, visit https://www.linkedin.com/pulse/grounding-half-answer-coi-compliance-kristen-nunery-wxaye/.

About illumend
Founded in 2025, illumend™ is the AI-powered platform redefining how businesses manage third-party insurance compliance and risk. Backed by myCOI, the leader in third-party insurance compliance management with more than 16 years of expertise, illumend reimagines compliance by guiding every step of the process—from document review and expiration tracking to risk flagging, communication, and resolution—within one intuitive system. Built on myCOI’s institutional foundation—having processed more than 45 million documents, managed over 1.2 million agreements, cleared more than 750,000 third-party partners, and identified more than two million coverage gaps before claims—illumend brings this depth of compliance intelligence into an AI-native platform. At its core is Lumie, illumend’s conversational AI guide that reads complex insurance documents, flags issues in real time, and explains them in language anyone can act on. To learn more, visit https://www.illumend.ai.

Media contact:
Michael Tebo
Gabriel Marketing Group (for illumend)
Phone: 571-835-8775
Email: michaelt@gabrielmarketing.com

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SOURCE illumend