Automations
This pillar addresses identity workflows that unify fragmented signals across online, offline, mobile, and partner surfaces into a usable operating profile for personalization and retention. Pages should explain how a custom graph-based workflow can improve targeting quality, campaign efficiency, and customer analytics while still respecting governance and consent boundaries.
This foundational workflow automates the construction and continuous update of a unified customer identity graph by stitching together signals from web, mobile, offline, and partner systems. It reduces manual data engineering overhead, improves match accuracy, and creates a single source of truth for downstream personalization and analytics, built on a scalable architecture with graph databases, real-time matching logic, and consent-aware governance.
This workflow automates the real-time linking of in-store transactions, call center logs, and loyalty card swipes to digital profiles, closing the attribution loop for retailers and brands. It eliminates batch-based reconciliation delays, enabling immediate personalization and campaign measurement, through an architecture that ingests POS and CRM events, applies probabilistic matching, and updates CDPs in sub-second latency.
This workflow automates the deduplication and unification of customer records trapped in separate systems like Salesforce, Marketo, and Shopify, where native integrations fail. It reduces manual list hygiene work, prevents campaign waste from duplicate sends, and is built as an orchestration layer that queries, matches, and merges profiles while preserving audit trails for marketing operations teams.
This workflow automates the enforcement of customer consent preferences (GDPR, CCPA) across the identity graph, dynamically suppressing or deleting profiles and signals based on real-time preference changes. It reduces compliance risk and manual opt-out processing by building a rules engine that sits between ingestion pipelines and the graph database, triggering automated suppression and audit logging.
This workflow uses specialized agents to infer and link individual customer profiles into household units or B2B decision units by analyzing shared attributes, transaction patterns, and network signals. It automates a manual analyst task to improve targeting for family plans or account-based marketing, using a multi-agent architecture where one agent finds patterns and another validates linkages against business rules.
This workflow automates the ongoing process of cleansing the customer master by finding and merging duplicates, appending missing attributes from third-party sources, and scoring profile completeness. It reduces the operational drag of decaying data on campaign performance, implemented as a scheduled agentic process that scans the CDP, calls enrichment APIs, proposes merges, and routes conflicts for human review.
This workflow automates the moment when an anonymous website visitor logs in or provides an email, stitching their previous anonymous browsing history to their known profile. It captures lost attribution and enables immediate personalized experiences, built with real-time event streaming, session cookie matching logic, and instant profile update triggers to systems like Braze or Adobe Experience Platform.
This workflow uses a resolved identity profile to dynamically assemble web pages, email bodies, or app content from modular components in real time. It automates manual content targeting rules, improving conversion through relevance, and is architected as a decisioning service that queries the identity graph, selects content variants, and renders them through CMS or email service provider integrations.
This workflow automates the selection of the optimal customer offer or message across channels by evaluating the unified identity profile against propensity models and business constraints. It replaces static journey maps with dynamic decisioning, boosting cross-sell revenue, and is built with a multi-agent system where one agent assesses eligibility, another predicts acceptance, and a third checks channel capacity.
This workflow automates the continuous re-evaluation and updating of marketing audience segments as customer identity profiles change in real time. It eliminates the latency and labor of nightly batch segmentation jobs, enabling always-accurate targeting, through an architecture that listens to profile update events, runs segment logic, and pushes membership changes directly to activation platforms.
This workflow automates the decisioning and rendering of personalized promotions at the moment of customer interaction in-store, online, or via mobile app. It synchronizes offer logic using the central identity graph to prevent conflict and over-exposure, built as a channel-agnostic microservice that evaluates eligibility, selects the offer, and formats it for the specific channel's API.
This workflow automates the coordination of customer touchpoints across email, SMS, push, and ads based on a unified view of identity and journey state. It replaces siloed channel campaigns with a single orchestration engine, improving cross-channel conversion rates, and is implemented using a workflow engine like LangGraph that triggers actions based on identity graph updates and external events.
This workflow automates the complex attribution of revenue to marketing touches by analyzing the complete, identity-resolved customer journey across devices and channels. It eliminates manual spreadsheet modeling, providing faster insight for budget optimization, through an architecture that ingests journey data, runs configurable attribution models (e.g., Shapley value), and outputs reports to BI tools.
This workflow automates the shifting of digital ad spend between campaigns and channels based on live performance data tied back to resolved identities. It improves ROI by cutting underperforming spend faster than human managers can, built as a closed-loop system that ingests conversion data from the identity graph, calculates efficiency, and adjusts bids via platform APIs like Google Ads or Facebook.
This workflow automates the calculation and updating of CLV scores for every identity-resolved customer by ingesting transaction history, engagement signals, and churn risk. It provides a constantly refreshed metric for tiering and retention efforts, implemented as a scheduled pipeline that pulls from the unified profile, runs forecasting models, and writes scores back to the CDP and CRM.
This workflow automates the connection between marketing touchpoints (tracked to a resolved identity) and downstream sales in ERP or CRM systems. It solves the manual stitching of marketing and sales data for accurate ROI reporting, built with data pipeline agents that match identity keys to opportunity IDs and generate pre-built dashboards for leadership.
This workflow automates the discovery and visualization of common customer paths by analyzing sequences of events in the identity-resolved interaction log. It replaces manual qualitative research with data-driven journey maps, identifying drop-off points for optimization, using sequence mining algorithms and visualization agents that output to tools like Microsoft Power BI or Tableau.
This workflow uses multiple agents to monitor unified identity profiles for early warning signs of churn, such as declining engagement, support ticket sentiment, and payment issues. It automates manual health scoring, enabling proactive retention, through an architecture where specialized agents analyze different signal types and a coordinator agent triggers alerts to CRM or customer success platforms.
This workflow automates the analysis of customer support interactions, social mentions, and survey feedback, linking sentiment and intent signals back to the unified identity profile. It provides a real-time view of customer mood for service and marketing teams, built with NLP agents that process unstructured text, classify intent/sentiment, and update the profile in the CDP.
This workflow automates the monitoring of identity-resolved engagement metrics (logins, page views, purchases) to detect statistically significant drops or spikes that indicate issues or opportunities. It replaces manual dashboard monitoring, enabling faster response, using time-series anomaly detection models that trigger alerts to business teams via Slack or email with contextual data.
This workflow automates the ingestion, routing, and enforcement of customer consent choices collected from web forms, preference centers, and regulatory requests. It ensures compliance across all systems using the identity graph, reducing legal risk, and is built as a central consent hub that receives preferences, translates them into suppression flags, and propagates them to marketing and analytics platforms.
This workflow automates the response to GDPR/CCPA access and deletion requests by using resolved identity keys to find all customer data across operational systems. It reduces the manual, error-prone process from days to hours, using agents that query databases, compile reports, redact third-party data, and generate audit-ready documentation for legal review.
This workflow automates the validation of data usage against privacy regulations in real-time, blocking campaigns or data flows that would violate rules based on a customer's consent profile and jurisdiction. It embeds compliance into operations, built as a gatekeeper service that intercepts marketing activation requests, checks the identity graph for consent flags, and approves or denies the action.
This workflow automates the post-signup experience by using the initial identity to trigger personalized welcome communications, guide feature adoption, and capture progressive profile data. It increases activation rates and reduces manual campaign setup, orchestrated through a workflow that reacts to the new identity event in the graph and executes a multi-step, multi-channel sequence.
This workflow automates the strategic collection of missing customer attributes over time by triggering micro-surveys or form fields in contextually relevant moments, based on the existing identity profile. It improves data completeness without annoying customers, implemented as a decision engine that identifies profile gaps and injects smart forms into web, email, or app experiences.
This workflow automates the identification and re-engagement of customers who abandon carts, using identity resolution to recognize the same user on a different device and serve a consistent recovery message. It recovers lost revenue from fragmented sessions, built with event tracking, real-time identity matching, and personalized email/SMS triggers via platforms like Klaviyo or Braze.
This workflow automates the entire process from churn prediction to intervention: it scores churn risk using the unified profile, segments at-risk customers, selects the optimal retention offer, and triggers the outreach across the best channel. It replaces siloed data science and campaign execution, built as an end-to-end LangGraph workflow that integrates ML models, business rules, and channel APIs.
This workflow automates the re-engagement of lapsed customers by analyzing their past identity-resolved behavior to select win-back offers and sequence communications across email, paid social, and direct mail. It systematizes a manual, sporadic process, improving reactivation rates, through a multi-step workflow that queries lapsed segments, personalizes creative, and manages suppression lists.
This workflow automates the recommendation and fulfillment of loyalty rewards by analyzing the unified customer profile for purchase history and preferences. It enhances the redemption experience to boost program engagement, built as a service that interfaces with the loyalty platform, suggests relevant rewards at point-of-sale or in-app, and processes the redemption transaction.
This retail-specific workflow automates the creation of a single customer view that links online browsing, app activity, in-store purchases (via loyalty or payment), and BOPIS transactions. It breaks down channel silos to enable true omnichannel personalization, requiring integration with POS, OMS, e-commerce, and CDP systems via real-time data pipelines and matching logic.
This workflow automates the critical moment when an online order is picked up in-store, definitively linking the digital identity to the in-store customer. It captures invaluable offline behavior data for future targeting, using agents that monitor order status, match pickup transaction to loyalty account, and update the central identity graph with the confirmed link.
This workflow automates the recognition of a customer entering a store (via app geofencing or WiFi) and triggers personalized staff alerts, digital signage, or offers to their mobile device based on their unified profile. It bridges digital intelligence to physical retail, built with mobile SDKs, real-time location triggers, and a low-latency API that fetches profile data from the identity graph.
This workflow automates the aggregation of a client's financial footprint—across banking, investment, and credit accounts—into a single advisor dashboard, using identity resolution to link accounts held under slight name variations. It replaces manual data gathering, improving advisor efficiency and client service, through secure integrations with core banking systems and data normalization agents.
This workflow automates the periodic re-screening of customer identities against watchlists and the monitoring of transaction behavior for anomalies, using the continuously updated identity graph. It moves compliance from batch to continuous, reducing risk, built with scheduled agents that pull profile data, screen against external databases, and flag discrepancies for investigator review.
This workflow automates the tracking of HNWI client interactions across private bankers, relationship managers, and digital channels into a unified engagement profile. It provides a holistic view for better service and cross-sell, integrating data from CRM, email, and event attendance systems using identity resolution to link personal and corporate entities.
This media workflow automates the creation of a single viewer profile by resolving identities across smart TVs, mobile apps, and web browsers, linking viewing history, preferences, and subscription status. It enables accurate recommendations and churn prediction, requiring integration with device graph providers and content management systems to unify session data in real time.
This workflow automates the prediction of subscription cancellation by analyzing identity-resolved signals like viewing frequency, content genre drop-off, and payment history. It triggers targeted retention campaigns before the customer churns, built as a pipeline that extracts features from the unified viewer profile, runs a ML model, and outputs risk scores to marketing automation tools.
This workflow automates the decision of which ad to show to a resolved viewer across different devices and sessions, while respecting cross-channel frequency caps. It improves ad relevance and prevents over-exposure, built as a server-side ad decisioning service that queries the identity graph for viewer profile and past ad exposure before making the insertion call.
This workflow automates the merging of guest stay records, preferences, and loyalty activity across different brands and properties within a hospitality group. It enables personalized service and targeted offers regardless of where the guest stays, requiring integration with multiple Property Management Systems (PMS) and the loyalty platform to create a master guest graph.
This workflow automates the creation of day-by-day itineraries for travelers by synthesizing their unified profile (past trips, preferences) with real-time context like weather and local events. It replaces manual travel agent curation, enhancing the booking experience, using agents that fetch profile data, query activity APIs, assemble options, and present them via email or app.
This workflow automates the real-time evaluation of a guest's loyalty status and point balance to trigger personalized room upgrade offers or redemption opportunities at check-in or during booking. It increases loyalty program value perception and utilization, built as a microservice that interfaces with the PMS and loyalty platform to make offer decisions at key journey points.
This telecom workflow automates the unification of a subscriber's identity across their mobile line, home internet, TV service, and connected IoT devices. It creates a holistic view for bundle optimization and support, requiring integration with billing systems, network authentication logs, and IoT platforms to resolve identities using account, device, and usage data.
This workflow automates the detection of service degradation (e.g., slow internet) specific to a resolved customer identity and triggers proactive notifications, troubleshooting steps, or technician dispatch before the customer calls. It reduces call volume and improves satisfaction, built by correlating network telemetry with the customer identity graph and using decision rules to initiate actions.
This workflow automates churn prediction for telecom by analyzing unified customer data: call detail records, billing history, support interactions, and network quality. It identifies at-risk subscribers for retention teams, implemented as a data pipeline that aggregates features from multiple source systems, scores churn risk daily, and pushes alerts to CRM for agent action.
This automotive workflow automates the linking of vehicle telemetry, connected app usage, dealership service history, and customer account data into a single owner profile. It enables personalized in-car experiences and predictive maintenance, requiring integration with telematics platforms, DMS (Dealer Management Systems), and CRM to resolve identities using VIN, email, and app IDs.
This workflow automates the analysis of vehicle sensor data linked to the owner's identity to predict maintenance needs, then proactively schedules service appointments at the preferred dealership. It improves customer experience and service revenue, built with a pipeline that ingests telemetry, matches it to the owner profile, runs predictive models, and interfaces with dealership scheduling software.
This workflow automates the collection of driving behavior data from connected vehicles, links it to the policyholder's identity, and calculates dynamic insurance premiums on a monthly or quarterly basis. It enables accurate, behavior-based pricing, built with a secure pipeline from telematics providers to the policy administration system, with calculation logic governed by actuarial rules.
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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.
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