Automations
This pillar covers protocol-design workflows that ingest historical trials, inclusion criteria, and current regulatory guidance to draft and refine trial protocols with minimal manual rework. Pages should explain how a custom implementation would combine LLM orchestration, rule-based validation, medical review checkpoints, and document systems to reduce costly amendments and accelerate trial setup.
This page details the end-to-end automation workflow for generating complete clinical trial protocols, from ingesting historical data and regulatory guidelines to drafting structured documents. It explains how a custom multi-agent architecture reduces manual drafting time by 60-80%, accelerates trial setup, and integrates validation checkpoints to minimize costly amendments. Implementation focuses on orchestration layers connecting LLMs, document systems, and medical review queues.
This page covers the automation workflow for generating and optimizing patient eligibility criteria by analyzing historical trial data, real-world evidence, and target product profiles. It shows how a custom agentic system reduces enrollment delays and protocol amendments by creating more precise, feasible criteria, with architecture details on data ingestion, logic validation, and simulation of recruitment impact.
This page explains the workflow for autonomously assessing protocol feasibility by modeling patient population availability, site capabilities, and operational complexity. It demonstrates how custom AI agents reduce months of manual assessment, improve site selection accuracy, and integrate with country-level regulatory databases to support faster go/no-go decisions for sponsors and CROs.
This page details the automation of drafting Statistical Analysis Plans directly from protocol objectives and endpoint definitions. It outlines how a custom workflow ensures statistical rigor, reduces biostatistician drafting time by over 50%, and creates audit-ready documents by integrating with clinical data standards (CDISC) and validation rules.
This page covers the workflow for autonomously generating comprehensive Safety Monitoring Plans, including pharmacovigilance procedures and Data Safety Monitoring Board (DSMB) charters. It shows how custom AI reduces medical writing burden, ensures regulatory compliance (ICH E2A, GVP), and integrates adverse event reporting workflows for faster protocol finalization.
This page explains the automation of rule-based QC checks for protocol consistency, logic contradictions, and regulatory alignment. It demonstrates how a custom validation layer flags errors before review, reduces QC cycles by 70%, and integrates with document management systems (Veeva Vault, SharePoint) to maintain version control and audit trails.
This page details the workflow for generating line-item budgets and payment schedules by analyzing protocol activities, site costs, and vendor quotes. It shows how custom AI translates protocol design into financial models, reduces budgeting time from weeks to days, and supports scenario modeling for amendments, directly improving cost forecasting accuracy for finance and outsourcing teams.
This page covers the automation of IP management sections, including kit design, logistics, and accountability procedures, based on protocol visit schedules and randomization plans. It explains how a custom workflow reduces manual coordination errors, integrates with IVRS/IWRS specifications, and optimizes supply forecasting to prevent trial delays and waste.
This page details the therapeutic-area-specific workflow for autonomously drafting oncology protocols, including complex endpoint definitions (RECIST, iRECIST), biomarker plans, and combination therapy safety monitoring. It shows how custom AI incorporates oncology-specific guidelines (NCI, ESMO) to accelerate design, reduce therapeutic expertise bottlenecks, and improve protocol quality for sponsors in high-stakes cancer drug development.
This page explains the specialized workflow for generating protocols for rare and orphan drug trials, addressing small patient populations, adaptive designs, and pediatric considerations. It demonstrates how custom AI leverages historical orphan drug approvals and real-world data to design feasible studies faster, reducing the administrative burden of navigating complex regulatory pathways (FDA Orphan Drug Designation).
This page covers the automation of creating risk-based monitoring plans and source data verification strategies directly from protocol complexity and endpoint risk assessments. It shows how a custom workflow reduces CRA manual planning time, optimizes monitoring resource allocation, and generates actionable triggers for centralized monitoring dashboards, lowering operational cost and site burden.
This page details the workflow for autonomously generating EDC system specifications, including CRF design, edit checks, and data validation rules, from the finalized protocol. It explains how custom AI eliminates weeks of manual translation between clinical and data management teams, reduces build errors, and accelerates database lock timelines through direct integration with EDC platforms (Medidata Rave, Oracle Clinical).
This page explains the automation of creating targeted recruitment plans, messaging, and site support materials by modeling patient demographics and behavioral data. It demonstrates how a custom AI workflow reduces recruitment planning cycles, improves enrollment forecast accuracy, and integrates with digital health platforms to pre-screen candidates, directly addressing the most costly bottleneck in clinical trials.
This page covers the continuous automation workflow for monitoring FDA, EMA, and ICH guideline updates and mapping their impact onto in-progress protocol drafts. It shows how custom agents reduce compliance risk, automatically flag required protocol changes, and maintain an audit trail of regulatory alignment, saving weeks of manual tracking for regulatory affairs teams.
This page details the workflow for autonomously drafting protocol amendments and assessing their cross-functional impact on budgets, supplies, and site contracts. It explains how custom AI reduces amendment cycle time from months to weeks, ensures consistency across document versions, and triggers necessary operational updates, minimizing disruption to ongoing trials.
This page covers the automation of creating patient-facing ICF templates that are tailored to protocol-specific risks, procedures, and regulatory requirements. It demonstrates how a custom workflow ensures language consistency, improves readability scoring, and accelerates IRB/EC submissions by generating submission-ready documents that reduce rework and ethical review delays.
This page explains the workflow for generating site-level Clinical Trial Agreements and budgets by extracting protocol activities, payment milestones, and sponsor boilerplate. It shows how custom AI reduces legal and contracting delays, ensures fair market value compliance, and creates negotiation-ready packages that accelerate site activation timelines.
This page details the specialized workflow for autonomously drafting clinical investigation plans for medical devices, incorporating unique elements like biocompatibility, usability testing, and PMA/510(k) regulatory pathways. It demonstrates how custom AI reduces design time for device sponsors, ensures alignment with ISO 14155 and FDA guidance, and integrates with quality management systems.
This page covers the automation workflow for designing hybrid trials that integrate RWE study arms, leveraging electronic health records and claims data. It shows how custom AI models accelerate the design of pragmatic trials, improve external validity, and generate protocols that meet FDA RWE guidance, enabling faster, lower-cost evidence generation for label expansions.
This page explains the specialized automation for drafting age-appropriate pediatric study plans, including dosing, formulation, and safety considerations per ICH E11. It demonstrates how custom AI incorporates pediatric development guidelines, reduces design complexity, and helps sponsors meet regulatory requirements for pediatric investigational plans (PIPs) more efficiently.
This page details the workflow for autonomously generating specialized safety monitoring and long-term follow-up plans for advanced therapy medicinal products (ATMPs). It shows how custom AI addresses unique risks like cytokine release syndrome and genomic integration, ensuring protocols meet stringent EMA/FDA expectations while reducing medical writing time for this complex therapeutic area.
This page covers the automation of creating data-driven centralized monitoring plans that define key risk indicators and statistical triggers for site intervention. It explains how a custom workflow reduces manual plan development, improves data quality oversight, and integrates directly with clinical data lakes to enable proactive risk management throughout the trial.
This page details the workflow for autonomously generating live project management dashboards that track protocol milestones, enrollment, and key risks. It demonstrates how custom AI pulls data from protocol design systems to create sponsor/CRO-facing operational views, reducing manual reporting overhead and improving real-time decision-making for trial leadership.
This page explains the automation of assembling final protocol documents from approved modules, managing cross-references, and maintaining a definitive version history. It shows how a custom orchestration layer eliminates copy-paste errors, ensures formatting compliance with sponsor style guides, and exports submission-ready PDF/eCTD packages, streamlining the finalization process.
This page covers the workflow for autonomously generating site feasibility and qualification reports by analyzing investigator profiles, past performance data, and therapeutic area expertise. It demonstrates how custom AI reduces manual site outreach and assessment time, improves site selection quality, and creates actionable reports that accelerate the study start-up phase.
This page details the automation of drafting detailed lab manuals and biospecimen management plans based on protocol-defined assays and sampling schedules. It shows how custom AI ensures technical accuracy, reduces manual coordination with central labs, and generates vendor-ready specifications that prevent pre-analytical errors and sample rejection.
This page explains the workflow for autonomously creating comprehensive data management plans, including data flow diagrams, reconciliation procedures, and privacy safeguards. It demonstrates how custom AI reduces the time from protocol finalization to database build, ensures alignment with data standards (CDISC, GDPR), and creates clear documentation for data management teams and auditors.
This page covers the advanced automation workflow for simulating adaptive design scenarios (sample size re-estimation, arm dropping) and directly authoring the corresponding protocol sections and statistical considerations. It shows how custom AI enables sponsors to rapidly model and implement complex designs, reducing statistical consultancy time and improving trial efficiency and success probability.
This page details the therapeutic-area-specific automation for drafting cardiovascular trial protocols, including complex endpoint definitions (MACE, echocardiography) and procedure standardization. It demonstrates how custom AI incorporates cardiology guidelines (ACC/AHA) and historical trial data to accelerate protocol development for sponsors in the high-volume cardiovascular drug market.
This page explains the specialized workflow for autonomously generating protocols for infectious disease and vaccine trials, including challenge studies, immunogenicity assessments, and variant surveillance plans. It shows how custom AI incorporates rapidly evolving pathogen-specific guidance from WHO and CDC, enabling faster response to public health needs and outbreak scenarios.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
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We understand the task, the users, and where AI can actually help.
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We define what needs search, automation, or product integration.
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We implement the part that proves the value first.
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We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
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