Automations
This pillar covers vendor, partner, and M&A diligence workflows that collect, verify, and score financial, legal, and operational risk indicators automatically. Content should show how a custom workflow architecture reduces onboarding and diligence delays while improving consistency, traceability, and escalated review across risk-sensitive organizations.
This foundational page details a custom, end-to-end orchestration workflow that automates the collection, verification, scoring, and escalation of vendor, partner, and M&A risk indicators. It explains how a multi-agent architecture reduces diligence cycle times from weeks to days, improves risk consistency, and creates a defensible audit trail, connecting disparate data sources and approval systems into a single operational model.
This page outlines a custom workflow where AI agents initiate onboarding, collect required documents, validate information against public registries, and populate vendor master data automatically. It covers the architecture for reducing manual follow-up, accelerating time-to-procure, and integrating with procurement and ERP systems like SAP Ariba or Coupa to ensure data quality from day one.
This page details a workflow that autonomously pulls and analyzes credit ratings, bankruptcy filings, news sentiment, and financial statement data to generate real-time supplier risk alerts. It explains the architecture for connecting to data providers, calculating financial stability scores, and triggering procurement or finance team interventions to prevent supply chain disruption.
This page describes a custom scoring engine that synthesizes financial, operational, compliance, and geopolitical signals into a single, dynamic risk score for each supplier. It covers the multi-source data ingestion, weighted scoring logic, and automated alerting architecture that enables procurement teams to prioritize reviews and mitigate exposure proactively.
This page explains a workflow where AI agents parse executed contracts, extract key obligations and SLAs, and continuously monitor performance data (e.g., delivery times, uptime) to flag non-compliance. It details the integration of document AI, performance management systems, and exception routing to legal or vendor management teams.
This page outlines a system that automatically validates supplier diversity status (e.g., minority-owned, women-owned) and ESG certifications against official databases and self-reported documentation. It covers the workflow's role in streamlining reporting for regulatory mandates and corporate sustainability goals, reducing manual verification overhead.
This page details a due diligence workflow where agents ingest and normalize financial statements from data rooms, calculate key ratios and trends, benchmark against peers, and flag anomalies for deeper review. It explains how this automation accelerates the initial screening phase, allowing deal teams to focus on high-value strategic analysis.
This page describes a workflow that automatically screens potential vendors, partners, and M&A targets against global sanctions lists, politically exposed persons (PEP) databases, and adverse media. It covers the architecture for batch and real-time screening, match resolution logic, and audit-ready reporting to strengthen anti-bribery and corruption (ABAC) programs.
This page explains a continuous monitoring workflow that uses NLP agents to scan news, social media, and industry reports for mentions of third parties, assessing sentiment and extracting risk events like lawsuits or scandals. It details the integration of news APIs, sentiment scoring models, and alerting systems to provide an early-warning layer for relationship managers.
This page outlines a legal due diligence workflow where AI parses thousands of contracts to extract and classify clauses related to liability, termination, indemnification, and data privacy. It explains how the system flags high-risk or non-standard language, summarizes findings, and routes contracts to appropriate legal counsel for review, drastically reducing manual triage time.
This page details a workflow that automatically checks the validity and status of licenses, permits, and registrations held by third parties by querying government and regulatory databases. It covers the architecture for scheduled validation runs, expiration alerting, and integration with compliance systems to prevent operational halts due to partner non-compliance.
This page describes a workflow designed to assess third-party data handling practices against privacy regulations. Agents analyze data processing agreements (DPAs), security questionnaires, and subprocessor lists to identify compliance gaps and generate remediation requirements, streamlining privacy impact assessments for vendors and processors.
This page explains a workflow that automates the distribution, analysis, and scoring of complex security questionnaires (like SIG or CAIQ). AI agents parse vendor responses, compare them against internal security baselines, flag deficiencies, and calculate a security risk score, reducing weeks of manual review to hours for cybersecurity teams.
This page details a proactive security workflow that, with consent, orchestrates external vulnerability scans of a vendor's internet-facing assets. It covers the scheduling, execution, analysis of scan results, prioritization of findings, and automated notification to both internal security and the vendor for remediation tracking.
This page outlines a workflow where AI agents verify the authenticity and scope of security certifications presented by vendors. The system checks issuing bodies, validation dates, audit periods, and included trust service criteria, flagging expired or insufficient certificates and updating the vendor's risk profile in the central register.
This industry-specific page details a workflow for healthcare organizations to automatically audit business associates and vendors for HIPAA compliance. Agents review BAAs, assess security controls, and verify breach notification protocols, creating a continuous assurance model that reduces manual audit burden and protects protected health information (PHI).
This page explains a workflow for financial institutions to automate due diligence on vendors handling non-public personal information. It covers the automated assessment of vendor controls against Gramm-Leach-Bliley Act (GLBA) and FFIEC guidelines, evidence collection, and reporting for regulatory examinations.
This page describes a workflow for validating third-party AI/ML models used in critical financial processes. Agents orchestrate the collection of model documentation, performance data, and validation reports, assessing them against SR 11-7 or other model risk management frameworks to ensure vendor models meet internal governance standards.
This page outlines a workflow for manufacturing firms to automatically evaluate a supplier's Quality Management System (QMS). AI agents analyze ISO 9001 certificates, audit reports, corrective action logs, and production defect rates to score quality risk and predict potential disruptions to production lines.
This page details a high-stakes due diligence workflow for screening partners against International Traffic in Arms Regulations (ITAR) and export control lists. It explains the automated checks for denied parties, end-use verification, and technology control plan compliance, which is critical for maintaining regulatory standing in defense contracting.
This page describes a workflow for corporate development teams to automate the initial screening of potential acquisition targets. Agents scrape public data, financials, and news to score targets based on strategic fit, financial health, and synergy potential, generating a ranked longlist and summary dossiers to focus manual effort on the most promising candidates.
This page explains a workflow that uses AI to automatically index, categorize, and build a searchable knowledge base from a virtual data room's contents during M&A due diligence. It also covers an agentic Q&A system that allows deal team members to ask natural language questions about the documents, retrieving precise answers and citations to accelerate review.
This page details a workflow that automates the analysis of IT architecture and application portfolios during M&A integration planning. Agents map systems, identify overlaps, assess technical debt, and estimate integration complexity and cost, providing data-driven insights to inform synergy realization and integration roadmaps.
This page outlines a novel workflow that uses AI to analyze external data (e.g., employee reviews, leadership profiles, news) to assess cultural alignment and organizational risk factors in a target company. It explains how this non-financial scoring can predict integration challenges and inform change management strategies.
This page describes an ESG due diligence workflow where AI agents engage with suppliers to request, validate, and normalize carbon emissions data. It automates the painful process of Scope 3 data collection, calculates footprint contributions, and flags suppliers that are lagging in sustainability reporting, supporting net-zero commitments.
This page details a workflow designed to screen suppliers and their sub-tier networks for modern slavery and human rights risks. Agents analyze geographic risk, audit reports, and news sources, generating risk scores and recommended due diligence actions to comply with legislation like the UK Modern Slavery Act or the Uyghur Forced Labor Prevention Act.
This page explains a workflow that continuously monitors geopolitical events, trade policy changes, and conflict zones to assess their impact on supplier locations and logistics routes. It details how AI scores and alerts procurement teams to potential disruptions, enabling proactive contingency planning and supplier diversification.
This page covers a process automation workflow that dynamically assembles customized due diligence questionnaires (DDQs) based on vendor type, risk tier, and spend. It then dispatches them via email or portal, tracks responses, and sends automated reminders, eliminating manual questionnaire management and follow-up.
This page details a workflow that acts as an intelligent router for risk findings. When an automated check flags an issue (e.g., an expired certificate), the system classifies its severity, determines the correct owner (e.g., cybersecurity, legal, procurement), and routes it into their workflow tool (e.g., ServiceNow, Jira), ensuring no exception falls through the cracks.
This page explains a workflow critical for compliance audits, where AI agents autonomously gather all evidence related to a third-party's due diligence process—questionnaire responses, validation reports, approval emails—and compile them into a coherent, time-stamped audit trail. This automation drastically reduces the manual effort required for internal and external audits.
This page describes a workflow where AI synthesizes data from all due diligence checks—financial, legal, cyber, compliance—into a comprehensive, narrative risk assessment report. It covers the template-driven generation, executive summarization, and distribution of these reports, providing stakeholders with a consistent, actionable view of third-party risk.
This page outlines the orchestration layer for due diligence approvals. It details how AI manages multi-stage approval workflows, escalates stalled items based on SLA breaches, and notifies stakeholders via email, Slack, or MS Teams. This ensures governance is enforced without requiring manual tracking of approval statuses.
This page details a workflow that moves beyond point-in-time assessment to continuous monitoring. AI agents periodically re-test key controls (e.g., certificate validity, financial health), identify newly opened issues, and track remediation progress by integrating with vendor and internal ticketing systems, closing the loop on risk management.
This page explains a two-sided workflow: first, AI customizes a standard DDQ by removing irrelevant sections based on vendor attributes; second, for returning vendors, it pre-fills the questionnaire with previously submitted, still-valid data. This significantly reduces the burden on both the assessor and the vendor, improving response rates and quality.
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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