36 New Products at Bio-IT World 2026

AlphaLife Sciences | AuroraPrime | Booth 808 
https://alphalifesci.com/  

AuroraPrime is an enterprise-grade generative AI platform designed to transform how life sciences organizations create regulatory and medical documentation. Built specifically for pharmaceutical, biotech, and CRO environments, AuroraPrime enables teams to automatically generate complex R&D documents—including Clinical Study Reports (CSRs), study protocols, investigator brochures, safety narratives, and CTD summaries—while ensuring traceability, compliance, and scientific accuracy. 

The platform integrates large language models (LLMs), domain-specific knowledge bases, and structured data pipelines to orchestrate the entire document lifecycle—from data ingestion and drafting to automated quality control and content reuse. AuroraPrime connects with existing clinical and regulatory systems, allowing organizations to synchronize source data with document content and significantly reduce manual writing and review effort. 

The latest version introduces an advanced AI orchestration engine that coordinates document generation, AI-assisted quality checks, and structured data synchronization within a unified workflow. New capabilities include configurable low-code authoring workflows, expanded regulatory knowledge libraries, and intelligent cross-document consistency checks. These enhancements enable life sciences teams to accelerate document preparation, improve content quality, and scale regulatory authoring processes across global development programs. 

By combining generative AI with domain-specific regulatory intelligence, AuroraPrime helps organizations shorten document timelines, increase productivity for medical writers, and support faster drug development and regulatory submissions. 

BioTeam | DNAVault 1.0.0 | Booth 419 
https://dnavault.starfleetbio.com  

Genomic data is permanent. Unlike a password or a credit card number, it cannot be reset, reissued, or revoked. Every consumer genomics company today requires you to hand it over, which means it lives on their servers, under their control, subject to their business decisions. DNAVault was built on a different premise: import your data, and it is encrypted on-device immediately, stored locally, and never transmitted. That is not a privacy policy. It is a cryptographic guarantee. 

Users import raw data from AncestryDNA, 23andMe, or a Whole Genome Sequence file. Four analysis modules run entirely on your phone, against your data, without it ever leaving: 

Kinship: compare genetic data with someone nearby using encrypted proximity sharing, verify you are unrelated, or find relatives and determine your precise relationship from parent and child to distant cousins, including offspring carrier risk 

Origins: trace your paternal and maternal lineage back thousands of years 

Health: screen 81 medically actionable genes, check for variants linked to inherited conditions, learn how they affect drug metabolism, all against a monthly-updated clinical variant database that lives on your phone 

Ask: answer plain-language questions about your genetic data through an AI proxy that never sees your raw data 

Nothing is transmitted. Nothing is stored remotely. The cryptographic guarantee is architectural, not contractual. 

Coming soon: private Whole Genome Sequencing through a cryptographically attested enclave. Results are encrypted with your device key. The provider verifiably cannot read them. 

Built in collaboration with engineering partner BioTeam, who verified and hardened the privacy implementation, and the UNH Hubbard Center for Genome Studies. 

App Store. $2.99 for Kinship, Origins, Health forever. Ask: 30-day free trial, then $2.99/month or $19.99/year subscription. https://apps.apple.com/us/app/dnavault/id6756529024 

Boulder Biocomputing | Vivo Facile 1.0 | Booth 321 
https://boulderbiocomputing.com/  

For decades, in vivo efficacy studies have relied on manual tools like Excel and handwritten notes, which often fail to keep pace with the high-stakes environment of the vivarium. Built on Boulder BioComputing’s SciDataHub platform, Vivo Facile is an end-to-end solution designed by pharmacologists for pharmacologists to treat in vivo study execution as a primary scientific workflow and not an afterthought. 

Key features include automated data capture from animal pool registration and baseline measurement capture (read directly from connected calipers and scales) to complex dosing schedule definition, hands-free daily data entry, real-time visualization, and one-click export to Excel and PowerPoint. Vivo Facile covers the entire study lifecycle, including role-based data controls to ensure structured, clean data is captured at the source, feeding directly into pre-defined scientific data infrastructure. 

Unlike previous tools that retrofit pharmacology into husbandry or chromatography systems, Vivo Facile is specifically for the scientist. Where previous tools have retrofitted pharmacology workflows onto animal husbandry or chromatography systems, Vivo Facile is built from the ground up around the scientist doing the science. 

Bridge Informatics | Bridge Horizon | Booth 23 
https://bridge-horizon.com/bioIT  

Bridge Horizon is a unified collaboration platform for genomics teams, launched in 2026 to eliminate the broken handoffs that slow modern bioinformatics work. It brings bench scientists, bioinformaticians, NGS core facilities, and lab leads into a single shared environment where data, bioinformatics pipelines, and results live together. No more emailing FASTQs, uploading to Dropbox, or waiting days for an analysis and an incomprehensible folder of files. 

Unlike others, Horizon replaces fragmented workflows with controlled, traceable collaboration built for cross-role coordination. Shared datasets are accessible instantly to everyone who needs them, with granular per-dataset permissions that let teams decide exactly who can view, run, or share, and revoke access at any time. Every pipeline run is automatically logged and versioned, capturing every parameter, input, and output, so teams always know which version of which pipeline produced a given result. 

For research scientists, this means going from sequencing to insight in hours instead of weeks, with the ability to explore results directly rather than waiting in a queue. For bioinformaticians, it means less time spent on file logistics and more time on science. For NGS core facilities, it means faster turnaround, polished deliverables, expiring access links for clients, and a clean audit trail when questions arise. 

Horizon grew out of Bridge Informatics, a services firm embedded in production genomics teams since 2020. After 100+ client engagements revealed the same recurring friction, our team built the tool our clients wished existed. One where every step is visible and understandable as it runs. 

CCC (Copyright Clearance Center) | AI Systems Training License | Booth 608 
https://www.copyright.com/solutions-ai-systems-training-license/  

Developing robust AI systems requires large quantities of quality materials, including copyrighted content. Vast quantities of information are not only needed for foundational training efforts but also for fine-tuning, specialized pretraining, and retraining. And the content that is used to train these systems? It’s imperative that it’s high-quality, relevant, and updated regularly to ensure the trained model has a high level of accuracy and low level of bias in its outputs. 

As organizations in pharma and biotech increasingly build and train AI systems, scaling access to large quantities of quality content and securing those rights can be a complex challenge, particularly when licensing from multiple sources. 

Launched in October 2025, CCC’s AI Systems Training License helps solve this challenge. 

This new license provides the necessary rights to enable lawful use of diverse, high-quality content, enabling companies to build ethical, externally facing AI models while mitigating risk and securing a competitive advantage in today’s dynamic market. 

The AI Systems Training License helps organizations: 

• Efficiently obtain rights to train AI for external uses 
• Simplify copyright compliance and reduce infringement risk
• Practice responsible AI through the ethical training of AI systems 

As AI evolves, it’s crucial that organizations invest in foundational compliance strategies to support their innovation. The AI Systems Training License helps them do this – powering compliant innovation through licensing.

ChapsVision Americas Inc | ChapsAgents | Booth 512 
https://www.sinequa.com/visioncast-agentic-ai-you-can-actually-trust/  

ChapsVision introduces ChapsAgents, the latest addition to its Enterprise Agentic AI Platform for life sciences organizations looking to radically accelerate drug discovery, clinical development, and pharmacovigilance. 

ChapsAgents enables enterprises to build and manage trusted AI agents at scale. It allows any employee to discover, build (no-code), test, deploy, orchestrate, and monitor agents with robust governance and security across hundreds or thousands of agents in a controlled enterprise ecosystem. 

Built on Trust and Versatility, ChapsAgents delivers three core pillars: Instant Grounding, Total Governance, and Future-Proof Architecture. Instant Grounding provides secure access to enterprise knowledge across any system, format, or language using Enterprise Knowledge Base (Agentic RAG), grounding every agent in trusted data and reducing hallucinations. Total Governance delivers a centralized control plane for full observability, auditability, traceability, permission management, and cost control. Future-Proof Architecture ensures adaptability across evolving AI ecosystems while keeping proprietary data under customer control. 

In life sciences, ChapsAgents enables trustworthy AI for drug discovery and early research by accelerating hypothesis generation across multimodal scientific data. It expedites real-world evidence (RWE) generation through automated extraction and harmonization of clinical and observational data. It also enhances pharmacovigilance and safety intelligence via continuous agent-driven monitoring and triage of safety signals across structured and unstructured data. 

Key capabilities include: a no-code agent builder, workflow orchestration, agent monitoring and tracing, LLM-agnostic design, MCP tool integration, human-in-the-loop approval, and enterprise-grade security. Integrated with ChapsVision’s AI solutions—including Sinequa and ArgonOS—it grounds agents in trusted scientific and clinical knowledge. 

ChapsAgents delivers scalable, explainable, governed agentic AI for mission-critical life sciences transformation. 

Dantech Corporation Inc | MLADU Version 2 | Booth 523 
https://www.mladu.com/  

MLADU is a concierge data transfer service built for organizations that cannot afford failure, delay, or compliance risk. More than a file transfer tool, MLADU delivers secure, high-performance, and fully auditable data movement across cloud, hybrid, and on-prem environments. Whether consolidating systems during mergers and acquisitions, transferring sensitive clinical and omics data, enabling global research collaboration, or fueling large-scale analytics, MLADU handles gigabytes to petabytes with speed, precision, and zero disruption. 

Compliance is foundational. Every transfer is encrypted, logged, checksum-validated, and audit-ready, supporting regulatory frameworks including HIPAA and GDPR. This release expands MLADU’s capabilities with enhanced security controls, strengthened role-based access control, expanded data integration patterns, and new Data Stations and Datasets for structured organization. Users can now fully customize workflows through an upgraded portal experience designed for greater visibility and control. In direct response to customer feedback, we’ve introduced flexible pricing models, including low-cost monthly storage options and a CRO-specific plan, moving beyond one-size-fits-all to precise alignment with operational and regulatory needs. 

Datagrok | Datagrok | Booth 720 
http://datagrok.ai  

Datagrok is a browser-native scientific data platform purpose-built for pharma and biotech — unifying data integration, advanced analytics, cheminformatics, bioinformatics, and enterprise application development in a single, zero-install environment. Powered by proprietary in-browser compute technology, Datagrok outperforms desktop competitors in interactive data exploration, delivering structure searches, sequence alignments, and statistical computations on millions of rows in milliseconds. 

Scientists work across every data modality natively: small molecules, biologics, peptides, sequences, high-content imaging, plate data, omics, and clinical datasets. Datagrok offers the fastest and most powerful peptide SAR analysis on the market — with sequence activity relationships, positional variability analysis, mutation cliffs, and scaffold decomposition running interactively at scale, directly in the browser. 

Connect to any data source — databases, ELNs, LIMS, cloud storage, REST APIs — through a unified semantic layer. Extend the platform with a rich plugin SDK, embedding proprietary models, custom visualizations, and domain workflows without forking core infrastructure. Governance is built in at every layer: fine-grained access control, data lineage, audit trails, and on-premise deployments. 

AI orchestrates the entire platform: natural language querying, agentic workflow execution, and autonomous hit triage — all within a scientist-in-the-loop, auditable framework. 

Deployed at 4 out of 10 biggest pharma companies. 

New (2025–2026): Interactive visualizations, support for peptides, pharma application suite (Compound Registration, Hit Design, Plate Management), and agentic AI.

Deloitte | Lab of the Future 1.0 | Booth 807  
https://www.deloitte.com/us/en/what-we-do/capabilities/converge/services/future-of-life-sciences-solutions.html  

Deloitte's Lab of the Future solution is a cloud-native, modular accelerator co-developed with AWS that modernizes end-to-end laboratory data operations from instruments-to-insights. It eliminates manual file movement and disconnected workflows by connecting instruments and core lab systems, including ELN, LIMS, and LIS, into a unified, governed data flow, enabling labs across biopharma R&D, manufacturing quality, clinical diagnostics, and MedTech to generate reusable, AI-ready data products that improve speed, traceability, and decision-making. 

Lab of the Future includes a Data Mover/Monitor and centralized Control Tower supporting scalable onboarding, configurable transfer rules and triggers, real-time alerts, and operational observability. It uses intelligent pipelines to parse raw instrument outputs, extract instrument-generated metadata, and standardize metadata and measurement payloads to open, standards-based target models. It supports ontology and semantic mapping, bi-directional metadata exchange with ELN and LIMS, a governed metadata repository, and a secure data catalog for discovery, access, and sharing, with auditable lineage to strengthen reproducibility and inspection readiness. 

New in this release, the Parser Agent, built on Amazon Bedrock AgentCore and Strands Agents, monitors structural changes in incoming instrument data files and automatically triggers parser code creation via an agentic AI code writer. It reviews new file structures, recommends parser selection from the Parser Library, or initiates new parser development based on specified input and output structures. This reduces onboarding from weeks to hours, eliminates manual coding overhead, and ensures pipeline resilience as formats evolve with human-approved releases, version control, and audit history maintaining enterprise-grade data integrity 

Discngine SAS | Peptide Analytics v1.0 | Booth 618 
https://www.discngine.com/  

Peptide therapeutics are rapidly gaining momentum across pharmaceutical companies. As a modality between small molecules and biologics, peptides pose unique challenges that traditional tools cannot address. Scientists often rely on manual work and spreadsheets, which are inefficient. 

Discngine's Peptide Analytics is a newly developed platform built specifically for peptide structure–activity relationship (SAR) analysis and reporting. Its unique technology sits at the intersection of bioinformatics (sequence-centric) and cheminformatics (structure- and chemistry-centric) approaches – an area where no existing tools currently offer a comparable solution. 

Once a dataset is loaded, the platform automatically performs global sequence alignment with position numbering, detects chemical modifications, and identifies structural bridges—all without manual annotation. Users can define reference peptides for comparative analysis. 

Through clustering and hotspot detection, users can explore SAR patterns and identify key positions or modifications that influence activity. Enhanced visualization spans atomic, monomer, region, and sequence-level views, with support for multi-sequence alignment. Detailed position views enable zooming, single-position focus, and atomic-level hybrid visualization for granular analysis. Interactive tools allow users to filter, customize, and generate ready SAR reports. 

The platform accommodates all peptide modalities - linear, cyclic, multicyclic, macrocyclic, branched, and chemically transformed - and supports both natural and non-natural amino acids. 

Peptide Analytics is a cloud-based, intuitive platform that scales to large datasets, supports users with varying levels of expertise, and integrates with existing research workflows via a robust API. 

As the first peptide-ready SAR tool of its kind, researchers can automate analysis, reporting, and lead optimization to accelerate discovery. 

EDETEK Inc | EDETEK CortexAI | Booth 801 
https://edetek.com/  

Clinical development faces a fundamental scaling constraint: study knowledge is not standardized. Protocols, amendments, schedules of activities, and monitoring plans remain largely unstructured, forcing each new study to reinterpret intent, reassess risk, and repeat operational decisions. This slows execution, increases variability, and prevents AI from scaling beyond isolated pilots to enterprise impact. 

EDETEK CortexAI addresses this challenge through a Digital Data Flow platform built on a neuro symbolic AI–driven Digitization Core and Thin Engine architecture. A deterministic digitization core ingests clinical study artifacts in any format and converts them into USDM compliant structured study definitions. Document intelligence, controlled terminology, schema validation, provenance capture, and amendment lineage are applied at ingestion, producing governed, machine readable study objects from day one. 

Key benefits: 

• USDM first: Standardization at ingestion enables consistent, computable study definitions.
• Digitize once, reuse broadly: Structured knowledge persists with full provenance and amendment history.
• Scalable AI, no lock in: Thin, interchangeable engines evolve independently of digitization.
• Inspection ready by design: Outputs are fully traceable, explainable, and standards aligned.
• Operationally embedded: Intelligence integrates directly with RBQM, CTMS, EDC, and eTMF workflows. 

What is new: CortexAI extends EDETEK’s R&D Cloud with a Clinical AI Fabric that digitizes legacy trials and delivers thin AI engines, such as Biostat.AI, on a shared USDM foundation. 

An AI that forgets yesterday cannot guide clinical trials of tomorrow. CortexAI fixes that problem.

Expert.ai | EIX-Submission Readiness | Booth 205 
https://www.expert.ai/  

Regulatory submission workflows are highly fragmented and manual. Teams must reconcile data across clinical, scientific, and regulatory sources while ensuring consistency, traceability, and compliance. This process is time-consuming and prone to inconsistencies, leading to delays, rework, and regulatory risk. 

EIX–Submission Readiness is an AI-powered framework that unifies document generation, quality control, and source data verification across the submission lifecycle. The 2026 release introduces a modular agentic architecture that separates content generation and validation into coordinated AI workflows seamlessly integrated into an easy-to-use solution for research and regulatory teams. The solution assists users in generating structured regulatory documents such as Investigator’s Brochures, CMC sections, study reports, and others. Specialized agents continuously compare generated outputs against source materials in real time, detecting inconsistencies as they emerge. 

The solution supports multimodal ingestion of clinical and regulatory data and performs automated cross-document comparison with full audit trails. A unified reasoning layer ensures that all output remains grounded in verified data, enabling inspection-ready traceability across documents. 

Key features include: 

(1) automated content generation from heterogeneous data sources 

• 200+ authoritative sources (PubMed, ClinicalTrials.gov, primary literature) 

• Multi-model consensus with cross-validation 

• Source-aware retrieval with iterative validation loops 

• Every claim backed by verifiable citations, ready for peer review and grant submission 

MADEAI, INC | MadeAi | Booth 5 
https://madeai.com/  

MadeAi is an AI-native evidence synthesis platform purpose-built for life sciences organizations to accelerate and scale literature reviews and broader evidence generation work with precision, transparency, and compliance. Developed by CapeStart, MadeAi combines advanced AI capabilities with expert human oversight and validation to streamline the entire evidence generation process—from protocol creation through submission-ready outputs. 

The platform enables teams across HEOR, Medical Affairs, Market Access, and RWE to reduce literature review timelines by up to 60% while maintaining the rigor required for regulatory and HTA submissions. With capabilities spanning intelligent search, automated de-duplication, AI-assisted screening, structured data extraction, quality appraisal, and report generation, MadeAi eliminates fragmented workflows and manual inefficiencies. 

What differentiates MadeAi is its commitment to explainability and auditability along with its seasoned services team. Every AI-driven decision is transparent and traceable, ensuring outputs bring confidence to everyday research questions as well as withstand regulatory scrutiny. The platform is LLM-agnostic, supports multi-agent orchestration, and integrates seamlessly with enterprise systems to support scalable deployment. 

Whether deployed as a SaaS solution or paired with expert services, MadeAi empowers life sciences teams to move faster, generate higher-quality evidence, and confidently deliver submission-ready insights in an increasingly complex regulatory landscape. 

OpenEye, Cadence Molecular Sciences | Target X, Version 1.0 | Booth 721 
https://www.eyesopen.com/targetx  

Target X is a first‑in‑class target exploration solution that transforms proteins from static structures into dynamic, druggable landscapes. By combining enhanced molecular dynamics, pocket detection, and rigorously validated AI-enhanced ligandability prediction, Target X enables confident discovery and prioritization of cryptic, transient, and allosteric binding sites, and expansion of known pockets including on historically undruggable targets. 

At its core, Target X employs weighted ensemble molecular dynamics (WE‑MD) guided by intrinsic protein motions to efficiently sample rare but therapeutically meaningful conformations. Single‑ and mixed‑solvent simulations with probe occupancy analysis reveal emergent binding sites spanning local fluctuations and large‑scale rearrangements. Identified pockets are then quantitatively ranked with AI using a ligandability model trained on 1,847 non‑redundant binding pockets, curated using true 3D pocket similarity to eliminate bias and data leakage. This approach delivers 91% accuracy, 97% precision, and correct identification of 81% of FDA‑approved drug binding sites, with >90% success in detecting known cryptic pockets from apo structures alone. 

Target X supports two independent workflows: ligandability ranking of MD‑derived pockets or direct scoring of static protein structures, enabling immediate use at any project stage. Delivered as a fully automated, end‑to‑end workflow on the cloud‑native Orion Molecular Design Platform, Target X scales across hundreds to thousands of GPUs and produces pocket geometries ready for virtual screening and structure‑based design. 

By uniting physics‑based dynamics, unbiased AI, and massive cloud scalability, Target X sets a new best‑in‑class standard for early‑stage target exploration. 

Orchestra Bio | Orchestra AI agents | Booth 708 
http://www.orchestra.bio  

Most AI tools today optimize individual productivity like helping someone write a document, summarize a meeting, or draft an email faster. Orchestra is working on AI that makes the entire organization run better, automating operations to deliver outcomes, not just documents. 

Building a biotech company is as much an operational challenge as a scientific one. Founders and management teams must continuously translate scientific progress into financial plans, board updates, regulatory timelines, and investor narratives. This work is time-consuming, error-prone, and typically disconnected from the underlying R&D. 

Orchestra solves this with AI agents to manage drug development operations. Orchestra enables companies to map their scientific and strategic plans like programs, milestones, scenarios, and assumptions into a structured knowledge base that reflects how the company actually works. 

This plan becomes the grounding context for Orchestra's AI agents that continuously process the work that flows through any life sciences company: scientific reports, meeting transcripts, CRO updates, emails, and other documents. The agents interpret updates in the context of the company's actual programs, workflows, and goals to automatically track progress against plan, surface deviations, and keep finance and operations in sync with the science in real time resulting in a living operational knowledge base across the entire company. 

PhaseV Trials | AI Conductor, v3.0 | Booth 711 
https://www.phasevtrials.com/ 

PhaseV's AI Conductor is a unified clinical trial management platform, a single source of truth for all trial knowledge, orchestrating protocol design by generating regulatory-aligned documentation, code, and data assets. It addresses decision fragmentation by bringing stakeholders together in a single environment, ensuring decisions are made transparently, pressure-tested, and informed. Changes are seamlessly propagated throughout the workflow, reducing weeks of manual reviews and audits. Add-on modules further optimize trial design and operational parameters, ensuring a strong starting point and minimizing the risk of costly amendments. 

Integrated pillars: 

1. Pre-Trial Documentation: Generates protocols, SAPs, CRFs, and schedules of assessment. GenAI assistants leverage a pre-configured template library or user-defined templates to streamline document creation. The system can initialize trial design parameters using reference NCT codes and provide recommendations to ensure alignment and consistency with the specific nuances of the trial.
   
2. During- and Post-Trial Analytics: Automates code generation and the production of SDTM and ADaM datasets, TLFs, and CSRs, reducing reporting cycle times. nd-to-end traceability and audit readiness across all outputs ensure consistency and regulatory compliance. 

Technical differentiators: 

1. Cross-document synchronization: Changes to design parameters automatically propagate across dependent documents and sections, ensuring consistency and alignment throughout.
   
2. Integrated optimization layer: Connects natively to PhaseV's Trial Optimizer (causal ML-powered design simulation), ClinOps Optimizer (patient-level site selection), and Response 3. Optimizer (responder subgroup identification). Optimization outputs flow back into AI Conductor and update the protocol automatically.
   
3. Regulatory-grade infrastructure:  Allows teams to iterate and converge on an execution-ready protocol from the outset. 21 CFR Part 11, ISO 27001, HIPAA, and GDPR compliant. 

Tech Mahindra | Clinical Protocol Writing (K-Scribe) | Booth 313 
https://www.techmahindra.com/  

Tech Mahindra launches an agentic AI–powered clinical trial operations platform - Clinical Protocol Writing (K-Scribe), available on AWS Marketplace, that automates execution from protocol authoring through site selection and patient readiness. Our solution generates ready-to-review draft protocols, aligns content with regulatory standards, and maintains full audit trails across reviews and approvals. Using specialized AI agents, we aggregate real-world data, competitive insights, site capabilities, and de-identified patient data to drive protocol feasibility, intelligent site selection, and automated patient pre-screening. By orchestrating sponsors, CROs, and sites through secure, automated workflows, we replace fragmented manual processes. 

TetraScience | Lead Clone Selection Assistant v0.2.0 | Booth 315 
https://www.tetrascience.com/solution-brief/lead-clone-selection-assistant  

TetraScience’s Lead Clone Selection Assistant uses AI to compress biologics lead optimization from six months to three, unlocking more candidate lifecycles and accelerating IND timelines, but the real breakthrough is what makes that speed possible: eliminating the data preparation bottleneck that precedes every decision cycle. 

In traditional workflows, scientists wait weeks for data engineers to manually aggregate multi-assay results such as expression titers, and stability profiles from disparate instrument outputs or use Excel and other tools, before ML ranking can even begin. That manual aggregation consumes 40% of a scientist's time and throttles the entire DMTA cycle. 

Lead Clone Selection runs directly on the TetraScience Data Foundry, where biologics assay data from any vendor instrument is normalized, context enriched, and available in analytics-ready Lakehouse tables without any data prep. No waiting for engineering resources. The ML ranking engine applies multi-criteria classification across the full candidate space, surfacing a prioritized shortlist with supporting evidence and enabling deeper analysis on cell morphology via NVIDIA VISTA-2D models. 

Human review is a design requirement, not an afterthought: scientists inspect ranked candidates, interrogate the underlying data, and approve selections before any downstream action. No decision is automated without scientific sign-off. 

The result is a compressed, auditable lead selection workflow that connects raw instrument outputs to justified, human-approved candidate shortlists — without requiring data engineering involvement at each cycle. This is the first ML-assisted clone selection application that inherits enterprise GxP controls and runs natively on harmonized, vendor-agnostic scientific data. 

UsefulBI Corporation | RegXP 1.2 | Booth 613 
https://usefulbi.com/  

RegXP by UsefulBI is an AI-powered medical and regulatory authoring solution designed for the Pharma and Healthcare Life Sciences community. The platform accelerates the creation of high-quality regulatory documents across multiple domains, including Clinical Study Reports, Informed Consent Forms, DSURs, eCRFs, Plain Language Protocol Synopsis documents, and other submission-ready content. It combines generative AI, regulatory intelligence, reusable templates, and human-in-the-loop review workflows to improve speed, consistency, and compliance across document authoring processes. 

Key differentiators include context-aware templates aligned with global regulatory guidelines, smart clause suggestions with confidence scoring, role-based access controls, version control, audit trails, and cross-document consistency checks. The platform also supports integration with enterprise data sources such as Veeva Vault, EDC databases including Medidata Rave, SharePoint, Google Drive, Oracle Argus, and publishing tools, enabling seamless collaboration across medical writing, regulatory affairs, QA, and submission teams. 

The latest release introduces multimodal document intelligence, agentic workflow automation, adaptive regulatory rulebooks, and Retrieval-Augmented Generation with citation anchoring. These capabilities allow the platform to extract and synthesize information from source documents, automate multi-step authoring workflows, flag content impacted by new regulatory guidance, and ensure that AI-generated claims remain traceable to approved source material. 

This innovation is valuable because regulatory authoring remains a major bottleneck in bringing therapies to patients. By reducing manual effort, improving document quality, supporting compliance readiness, and enabling faster submission preparation, RegXP helps Pharma and HCLS organizations move from fragmented authoring to intelligent, traceable, and scalable regulatory content generation. 

ZONTAL | ZONTAL Integration Factories 2026 | Booth 600 
https://zontal.io/products/integration-factories/  

Large pharmaceutical R&D organizations operate thousands of instruments from hundreds of vendors, each generating proprietary data. Traditionally, connecting these instruments requires bespoke IT projects—weeks of custom coding, manual validation, and siloed expertise that doesn’t scale. This creates onboarding bottlenecks, delays lab readiness, slows post-M&A site expansion, and limits cross-program analytics and AI initiatives. 

ZONTAL Integration Factories replace this fragmented model with an industrialized approach that reduces onboarding time from weeks to days. Two coordinated factories enable this shift: 

The Data Converter Factory standardizes vendor-specific outputs into analysis-ready data aligned with the Allotrope Simple Model (ASM). Each converter is a governed, version-controlled, and automatically tested asset—built once and reused across all sites using that instrument. 

The Instrument Adapter Factory connects instruments to enterprise systems via standard protocols like OPC UA and SiLA 2, replacing fragile point-to-point integrations with scalable, governed adapters. 

New in 2025–2026 is AI-assisted generation: users upload raw vendor outputs or protocols, and AI drafts complete converters or adapters including audit-ready evidence, which engineers review and validate. Each implementation builds on prior work—reusing patterns, templates, and validation rules—continuously reducing integration cost and effort. 

The impact is significant. Data previously trapped in silos becomes accessible for cross-program analytics, stability trending, method transfer validation, and regulatory submissions. OOS investigations drop from weeks to hours. IND timelines compress by months. Technology transfers retain full method lineage, and scientists can query data across sites and programs using scientific language instead of navigating file systems.