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Enterprise Adobe Experience Platform

Adobe Experience Platform - Unified Customer Data and Journey Orchestration

We implement Adobe Experience Platform as the unified customer data backbone for enterprise organisations - from XDM schema design and Real-Time CDP audience segmentation to Adobe Journey Optimizer cross-channel journeys, Customer AI propensity models, and enterprise-grade privacy and consent management.

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500+
Enterprise Clients
10,000+
Engineers Deployed
50+
Countries Served
99.4%
CSAT Score
48h
Team Assembly

The Challenge

Siloed Customer Data Prevents Personalisation at Enterprise Scale

Enterprises with customer data fragmented across dozens of systems cannot build accurate audience segments, cannot personalise experiences in real time, and cannot measure customer journeys across channels. The gap between the personalised experience customers expect and what disconnected data infrastructure can deliver grows wider every year, costing revenue and customer loyalty.

80%
of enterprise customer data sits in siloed systems that cannot be joined at activation time
71%
of consumers expect personalised interactions and express frustration with generic experiences
5.8x
average ROAS improvement from Real-Time CDP first-party audiences versus third-party interest audiences
$3.5T
global revenue opportunity from personalisation at scale per McKinsey

Why QuickHire

Why Enterprises Choose QuickHire

01

XDM Schema Architecture

Every AEP implementation begins with a dedicated schema design workshop. We design XDM schemas for all data sources before ingestion begins, preventing the downstream re-ingestion and profile corruption that incorrect schema design causes.

02

Identity Graph Design

Our identity resolution architects design deterministic and probabilistic linking rules that maximise profile completeness while preventing incorrect identity collapse from shared device scenarios - the most common source of Real-Time CDP data quality failure.

03

Journey Optimizer Expertise

We implement Adobe Journey Optimizer for real-time triggered journeys across email, push, SMS, in-app, and web channels, using Real-Time Customer Profile data to personalise every message at the individual level within seconds of triggering events.

04

Customer AI Implementation

Customer AI churn and conversion propensity models are configured, trained, and validated against historical event data, then integrated into Real-Time CDP segments and AJO journey decisioning for AI-driven audience targeting.

05

Privacy and Governance Framework

We implement the full AEP data governance framework including field-level usage labels, policy enforcement for destination activation, consent management CMP integration, and Privacy Service API configuration for data subject request processing.

06

Multi-Source Data Engineering

Our data engineering team builds XDM-compliant ETL pipelines, source connector configurations, and streaming ingestion for any source system - CRM, analytics, mobile, call centre, POS, or offline files - with monitoring and data quality validation from day one.

Challenges

Common Enterprise Pain Points

01

Schema Design Irreversibility

XDM schema choices made at the beginning of an AEP implementation are very difficult to change once data has been ingested at scale. Incorrect field type choices, missing identity fields, or poorly designed custom field groups require full dataset re-ingestion - a costly and disruptive operation that makes upfront schema design workshops non-negotiable.

02

Identity Graph Accuracy

Incorrect identity linking - caused by shared device identifiers, missing namespace prioritisation rules, or aggressive probabilistic linking - produces inaccurate unified profiles that undermine the accuracy of every audience segment built on top of them. Identity graph design requires deep expertise in identity namespace hierarchy and linking rule logic.

03

Streaming Ingestion Costs

Real-time streaming ingestion into AEP is significantly more expensive per event than batch ingestion. Organisations that default to streaming ingestion for all data sources often face unexpectedly high AEP consumption costs. We design ingestion strategies that use streaming only for the real-time use cases that require it, and batch for all other data types.

04

Data Governance Complexity

Enterprise organisations with sensitive customer data must configure AEP data usage labels and policies to prevent regulated data from being activated to inappropriate destinations. Without governance configuration, AEP provides no automatic enforcement, creating compliance risk that is often not visible until an audit.

05

Organisational Adoption of CDP Model

Adopting AEP requires marketing, analytics, and data engineering teams to change how they think about audience management, campaign targeting, and personalisation. Without structured change management and training, AEP is often underutilised after implementation, with teams reverting to familiar but less capable legacy tools.

Our Approach

AEP Implemented for Real Business Outcomes, Not Platform Completion

Our AEP implementations are structured around specific business use cases - reducing churn, increasing conversion, improving customer lifetime value - rather than generic platform activation checklists. Each use case has a defined data requirement, segment definition, journey design, and success metric agreed before implementation begins, ensuring every workstream delivers measurable value.

01
Data Foundation Design
XDM schema workshop, identity namespace strategy, source connector architecture, and ingestion pattern design documented and signed off before any ingestion begins.
02
Real-Time CDP Activation
Segment taxonomy design, streaming and batch segment evaluation configuration, and destination connector setup for all required advertising and engagement platform activations.
03
Journey Optimizer Implementation
AJO journey design for triggered, transactional, and campaign use cases across email, push, SMS, and in-app channels with Real-Time Customer Profile personalisation.
04
Customer AI and Decision Management
Propensity model configuration, training data validation, score integration into segments and journeys, and Decision Management offer catalogue setup for 1:1 personalisation.

Delivery Models

How We Deliver

Real-Time CDP Foundation

AEP data foundation covering XDM schema, identity graph, source connectors, Real-Time Customer Profile, initial segment library, and 2 to 4 destination activations for advertising and email platforms.

Timeline
16-24 weeks
Team Size
5-8 consultants
AJO and CDP Programme

Combined Real-Time CDP and Adobe Journey Optimizer implementation covering the data foundation plus triggered journey orchestration across email, push, SMS, and in-app channels with Customer AI integration.

Timeline
24-40 weeks
Team Size
7-12 consultants
AEP Managed Operations

Ongoing AEP platform operations covering ingestion monitoring, segment maintenance, journey optimisation, Customer AI model refreshes, and quarterly strategic advisory to evolve the platform with business priorities.

Timeline
Ongoing
Team Size
3-5 consultants

Capabilities

Technical Capability Matrix

Data Foundation
XDM Schema DesignIdentity Graph ConfigurationSource Connector ImplementationStreaming Ingestion (HTTP API)Batch Ingestion (S3, SFTP)Dataset and Dataflow Management
Real-Time CDP
Segment Builder DesignStreaming Segment EvaluationDestination Connector ConfigurationAudience Activation (Google, Meta, LinkedIn)B2B Edition (ABM)Audience Suppression Logic
Journey Optimizer
Event-Triggered Journey DesignEmail and Push Channel SetupSMS and In-App MessagingTransactional Journey TemplatesA/B Testing in AJODecision Management (Offer Decisioning)
AI and Intelligence
Customer AI Churn PropensityCustomer AI Conversion PropensityAttribution AIDecision Management RankingIntelligent SegmentationCustomer Journey Analytics Integration
Governance and Privacy
Data Usage LabelsPolicy EnforcementConsent Management (OneTrust)Privacy Service APIGDPR Data Subject RequestsData Governance Framework Documentation

Engagement Models

How We Engage

Choose the model that fits your programme governance, budget cycle, and team structure.

01

Staff Augmentation

Engineers embed directly under your management.

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02

Dedicated Developers

Full-time team aligned to your product roadmap.

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03

Managed Teams

End-to-end delivery with SLA-backed outcomes.

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04

Engineering Pods

Autonomous cross-functional pods per domain.

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05

Offshore Dev Centre

Permanent engineering base in India. Full IP ownership.

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06

Build-Operate-Transfer

We build and run it. You take ownership on schedule.

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Our Process

From Discovery to Delivery

1

Use Case and Data Discovery

Weeks 1-3

Business use case prioritisation, data source inventory, identity namespace mapping, data readiness assessment, and AEP sandbox governance design with technology and marketing stakeholders.

2

Schema and Identity Design

Weeks 3-5

XDM schema workshop, field group design, identity namespace priority rules, source-to-XDM field mapping specifications, and governance label schema documented with stakeholder sign-off.

3

Data Ingestion and Profile Build

Weeks 6-14

Source connector configuration, ETL pipeline development, streaming ingestion setup, identity graph population, and Real-Time Customer Profile validation across all connected data sources.

4

Segmentation, Journeys, and AI

Weeks 15-24

Segment library build, destination activation configuration, AJO journey design and testing, Customer AI model training and validation, and Decision Management offer catalogue setup.

5

Go-Live, Measurement, and Optimisation

Ongoing

Production segment activation, AJO journey activation, Customer AI score integration, privacy framework validation, ROI baseline measurement, and managed services handover.

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Security & Compliance

Enterprise-Grade Security by Default

✓ISO 27001 Certified✓SOC 2 Type II Ready✓GDPR Compliant✓DPDP Act Ready✓NDA on Day 1✓MSA Available✓IP Assignment Clauses✓Escrow Options

Governance

Programme Governance

✓

AEP Governance Board

Monthly review of schema evolution requests, new dataset additions, destination connector additions, and policy enforcement configuration changes, ensuring all changes are impact-assessed before Production promotion.

✓

Data Quality Monitoring

Automated ingestion pipeline monitoring with alerting for batch job failures, streaming latency breaches, identity graph anomalies, and segment evaluation errors with documented SLA response times.

✓

Privacy Compliance Review

Quarterly review of consent management configuration accuracy, Privacy Service request processing logs, data governance label completeness across all active schemas, and policy enforcement test results.

✓

Sandbox Promotion Process

Formal schema and configuration change promotion process from Development to Staging to Production, with documented testing evidence required at each stage before Production promotion is authorised.

✓

Use Case ROI Reviews

Quarterly business impact reviews assessing segment activation performance, journey conversion metrics, Customer AI model accuracy, and Decision Management offer engagement rates against the business case targets.

Team Structure

Your Enterprise Team

AEP implementations are staffed with Adobe Experience Platform Solution Architects, XDM data engineers, Real-Time CDP specialists, Adobe Journey Optimizer developers, Customer AI analysts, and privacy and governance consultants. Our team holds Adobe Experience Platform certified practitioner credentials and has delivered AEP programmes across retail, financial services, healthcare, and telecommunications globally.

✓AEP Solution Architect
✓XDM Schema Engineer
✓Data Engineering Lead
✓Real-Time CDP Specialist
✓Adobe Journey Optimizer Developer
✓Customer AI Analyst
✓Decision Management Specialist
✓Privacy and Governance Consultant

Project Lifecycle

From Kickoff to Production

01
3 weeks

Discovery and Use Cases

Use case priority list, data source inventory, identity namespace map, sandbox governance model, data readiness report.

02
2-3 weeks

Schema and Identity Design

XDM schema specifications, field group designs, identity priority rules, source-to-XDM field mapping, governance label schema.

03
6-10 weeks

Ingestion and Profile

Source connectors, ETL pipelines, streaming ingestion, identity graph, Real-Time Customer Profile validation report.

04
8-14 weeks

Activation and AI

Segment library, destination activations, AJO journeys, Customer AI models, Decision Management offer catalogue.

05
Ongoing

Go-Live and Operations

Production activation, privacy framework sign-off, ROI baseline, monthly operational reports, managed services handover.

Case Studies

Enterprise Outcomes

Retail

A global fashion retailer with 28 million customers had loyalty, e-commerce, and in-store purchase data in four separate systems with no unified customer view, preventing accurate churn prediction and personalised retention campaigns.

We implemented AEP with XDM schema design for all four source systems, identity graph linking loyalty card to digital and in-store purchase behaviour, Customer AI churn model, and AJO retention journeys activated to email and push channels.

19%reduction in 12-month customer churn rate for high-propensity churn segments targeted by AJO retention journeys versus uncontacted control group
Financial Services

A global bank needed to activate first-party customer data for digital advertising without violating financial services data privacy regulations, as third-party cookie deprecation eliminated their previous audience targeting capability.

We implemented Real-Time CDP with data governance policies enforcing financial services usage restrictions, activated consented customer segments to Google Customer Match and Meta Custom Audiences using hashed first-party identifiers within the regulatory permitted use framework.

5.9ximprovement in advertising ROAS compared to the pre-implementation third-party interest audience targeting, measured over 6 months of Real-Time CDP audience activation
Telecommunications

A national telecoms operator was losing 18 percent of postpaid subscribers annually to competitors, with no predictive capability to identify at-risk subscribers before they initiated porting requests.

We implemented Customer AI churn propensity on AEP, training on 36 months of billing, usage, service interaction, and complaint history data, activating high-propensity churn segments to AJO retention journeys with personalised offer decisioning.

31%reduction in churn rate for high-propensity segments receiving AJO retention journeys versus historical average, generating $12M in retained annual recurring revenue

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Frequently Asked Questions

Adobe Experience Platform (AEP) is Adobe enterprise customer data platform and digital experience infrastructure layer that unifies customer data from all touchpoints - websites, mobile apps, CRM, call centres, in-store POS, and offline data sources - into standardised, real-time customer profiles that drive personalisation, analytics, and marketing activation. AEP includes the Experience Data Model (XDM) schema framework for data standardisation, the Identity Graph for cross-device and cross-channel identity resolution, the Real-Time Customer Profile for unified profile assembly, Real-Time CDP for audience segmentation and activation, Adobe Journey Optimizer for cross-channel journey orchestration, Customer AI for predictive scoring, and a Privacy and Consent management framework. AEP serves as the data backbone for the entire Adobe Experience Cloud, enabling coordinated experiences across Adobe Analytics, Adobe Target, Adobe Campaign, and Audience Manager.
The Experience Data Model (XDM) is the standardised open-source schema framework that Adobe Experience Platform uses to normalise all customer data into a consistent structure regardless of the source system. XDM schemas define the shape of data flowing into AEP, covering standard field groups for identities, events, profiles, and commerce interactions, as well as the ability to create custom field groups for organisation-specific data attributes. Schema design is one of the most consequential decisions in an AEP implementation because once data is ingested under a schema, changing the schema requires re-ingesting historical data. We conduct a dedicated schema design workshop as a foundational step in every AEP engagement, ensuring XDM schemas are designed to support all current and anticipated future use cases before data ingestion begins. Poorly designed schemas create data silos and prevent the cross-source joins that are the core value proposition of AEP.
Adobe Real-Time CDP is the audience segmentation and activation layer within AEP that assembles unified customer profiles from the Real-Time Customer Profile store and evaluates them against segment definitions in real time as new data events arrive. Audience segments built in Real-Time CDP can be activated to any destination - advertising platforms (Google, Meta, LinkedIn), email platforms (Adobe Campaign, Marketing Cloud), personalisation tools (Adobe Target), and custom API destinations - without requiring a data export or ETL process. Real-Time CDP is the appropriate product for organisations that need to activate audience segments for personalisation and advertising use cases in near-real time, as distinct from batch audience management tools that refresh daily. We implement Real-Time CDP segment definitions, evaluate streaming and batch segment evaluation trade-offs, and configure destination connectors for all required activation channels.
Identity resolution in AEP is managed by the Identity Service and the Identity Graph, which maintain a graph of all known identifiers for each customer across channels - email addresses, phone numbers, CRM IDs, cookie IDs, mobile advertising IDs (IDFA, GAID), loyalty numbers, and any custom identifiers defined in the schema. When a new identity is encountered (e.g., an authenticated login on a device previously tracked only by cookie), the Identity Graph links the authenticated identity to the existing cookie history, enabling the Real-Time Customer Profile to assemble a more complete view of that customer behaviour. Identity Graph collapse (where two different people are incorrectly linked due to shared device usage) is a design risk that we address through deterministic versus probabilistic linking rules and shared device detection logic. Identity resolution quality is the single most important factor in Real-Time CDP segment accuracy, and we invest significant effort in identity architecture design before data ingestion begins.
Adobe Journey Optimizer (AJO) is the cloud-native, AEP-native cross-channel journey orchestration tool that enables real-time, event-triggered journeys across email, push notification, SMS, in-app messaging, and web personalisation channels, drawing on the Real-Time Customer Profile for personalisation context. Unlike Adobe Campaign, which is a batch-oriented direct marketing platform with a separate database, AJO operates directly on AEP data and is designed for millisecond-latency triggered communications (e.g., a welcome message sent within seconds of account creation, or an abandonment message triggered by a real-time browse event). AJO is the strategic direction for Adobe cross-channel marketing, while Campaign Classic and Campaign Standard serve organisations with legacy investment in batch direct marketing programmes that are not yet migrated to the AEP architecture. We advise on the appropriate platform for each use case and design migration paths from Campaign to AJO where relevant.
AEP source connectors are pre-built or custom adapters that ingest data from source systems into AEP datasets mapped to XDM schemas. Adobe provides native source connectors for CRM systems (Salesforce, Dynamics), cloud storage (Azure Blob, AWS S3, Google Cloud Storage), databases (PostgreSQL, MySQL, Oracle), analytics platforms (Adobe Analytics, Google Analytics), and advertising platforms (Google Ads, LinkedIn). For source systems without a native connector, we build custom source connectors using the AEP Flow Service API or use the HTTP API Source for real-time streaming ingestion. Data ingestion can be batch (scheduled file-based) or streaming (event-based via Streaming Connection). We design the ingestion architecture to balance data freshness requirements against the cost of high-frequency streaming ingestion, with streaming reserved for the real-time personalisation use cases that require sub-minute data availability in the profile.
Customer AI is an AEP intelligent service that uses machine learning to generate propensity scores for each customer in the unified profile based on their historical behavioural data. Standard Customer AI models include churn propensity (likelihood to cancel or stop purchasing within a defined time window) and conversion propensity (likelihood to purchase or complete a target action within a defined time window). These scores are stored as profile attributes and can be used in Real-Time CDP segment definitions, Journey Optimizer personalisation, and Adobe Analytics cohort analysis. Customer AI requires a minimum volume of historical event data to train models accurately, and we conduct a data readiness assessment before configuring Customer AI instances to confirm the training data volume meets Adobe minimum thresholds.
AEP Privacy and Consent management is implemented through a combination of Platform-level data governance tools and schema-level labelling that enforces data usage restrictions automatically. Data Governance labels applied to schema fields are evaluated by Policy Enforcement before data can be used in a destination activation, preventing sensitive data from being sent to destinations where its use would violate the organisation policy. Consent management integration with a CMP (OneTrust, TrustArc) populates a consent field in the customer profile that journey and segment policies evaluate before including a profile in an activation. Privacy Service processes data subject access and deletion requests by searching across all AEP datasets for records matching the requester identity and returning or deleting them within documented SLA windows. We configure the full privacy and consent framework as part of every AEP implementation, producing a data governance policy document mapping each data type to its permitted uses.
The AEP Segment Builder is the interface for defining audience segments based on profile attributes, events, and time constraints that are evaluated against the Real-Time Customer Profile. Segments can be defined using streaming evaluation (continuously evaluated as new events arrive, adding or removing profiles within seconds) or batch evaluation (refreshed on a defined schedule, typically hourly or daily). Segment definitions can combine demographic attributes (age, location, industry), behavioural events (product views, add-to-cart, purchases), time-bounded conditions (visited within the last 7 days), and identity-linked attributes (loyalty tier, predicted churn score from Customer AI). We design segment taxonomies in a workshop with marketing and analytics stakeholders, ensuring segments are built against a consistent naming convention and documented business purpose, with the evaluation type matched to the activation use case and latency requirements.
AEP integrates with non-Adobe marketing technology through Destinations, which are pre-built or custom outbound connectors that activate Real-Time CDP audiences to external platforms. Adobe maintains a growing catalogue of native Destinations including Google Display and Video 360, Meta Custom Audiences, LinkedIn Matched Audiences, The Trade Desk, Pinterest, Amazon Advertising, Braze, Twilio, Salesforce Marketing Cloud, and Marketo. For platforms without a native Destination, the Custom HTTP API Destination allows activation to any webhook-based endpoint. We also use AEP Server-Side Forwarding via Adobe Experience Platform Data Collection (Launch Server-Side) to relay AEP events to third-party platforms in real time. For organisations with significant investment in non-Adobe marketing platforms, we design a hybrid architecture that uses AEP as the central data layer and profile store while activating to the best-fit execution platform for each channel.
Real-Time CDP B2C Edition is designed for consumer-facing organisations, assembling unified person-level profiles from cross-channel behavioural data and activating person-level audience segments to advertising and engagement platforms. Real-Time CDP B2B Edition extends this to account-based marketing (ABM) use cases, adding B2B-specific data models for accounts, opportunities, and buying groups, and enabling segment definitions that combine person-level behaviour with account-level attributes such as industry, ARR, and opportunity stage. B2B Edition integrates with B2B CRM systems (Salesforce, Dynamics) and intent data platforms to assemble account-level engagement scoring models. We implement the correct edition based on the organisation business model, advising on the data model differences and the activation use cases that each edition enables.
AEP sandbox governance is critical for enterprise organisations with multiple teams and use cases sharing a single AEP contract. We design a sandbox strategy that reserves the Production sandbox for validated, live use cases; maintains a Development sandbox per major workstream (analytics, CDP, journey orchestration); and uses a shared Staging sandbox for cross-team integration testing before Production promotion. Access control is configured using AEP permission sets mapped to roles (data engineer, data analyst, marketing operator, administrator), ensuring each team has access to only the data and tools their function requires. Schema and dataset naming conventions are documented and enforced through an AEP governance board that reviews all new schema field additions and dataset creations before they are promoted to Production.
Adobe Journey Optimizer Decision Management (formerly Offer Decisioning) is an AI-powered offer selection and personalisation engine within AJO that determines the best offer, content, or action for each individual customer at every interaction based on their Real-Time Customer Profile, eligibility rules, and business constraints such as budget caps and frequency limits. Decision Management uses a ranking strategy - either a priority score defined by the marketer or an AI-powered ranking model trained on historical offer interaction data - to select the single best offer from a managed offer catalogue. Decisions can be called from AJO journeys, Adobe Target experiences, edge personalisation endpoints, or via the Decisioning API for real-time integration with non-Adobe channels. We implement Decision Management for advanced AJO engagements where 1:1 offer personalisation at scale is a core business requirement.
ROI measurement for AEP implementations is designed during the business case phase and tracked against pre-agreed KPIs after go-live. Common ROI metrics include audience segment activation efficiency (reduction in audience build time and increase in audience match rates for advertising platforms), personalisation lift (percentage improvement in conversion rate or email engagement for AJO journey recipients versus control groups), data consolidation savings (reduction in third-party data and tool costs), and churn reduction (revenue retained from Customer AI-identified at-risk customers successfully targeted by retention interventions). We establish measurement baselines and control group frameworks before campaign activation begins, and deliver quarterly business impact reports in the first year after go-live to validate the ROI case and identify optimisation opportunities.
Data engineering is the most substantial technical workstream in an AEP implementation. Key tasks include ETL pipeline development to transform source system data formats into XDM-compliant schema structures before ingestion, data quality validation rules that prevent malformed records from corrupting the unified profile, identity stitching logic that correctly assigns identity namespaces to each identifier type from each source, historical data migration to backfill AEP datasets with sufficient event data for Customer AI model training, and monitoring pipelines that alert when ingestion jobs fail or data freshness SLAs are breached. We provide a dedicated AEP data engineering team for the implementation programme and transition operational responsibility to the client data engineering team with a documented runbook and monitoring dashboard after go-live.
Whether AEP can replace an existing CDP or data warehouse depends on the specific use cases each system serves. Real-Time CDP is designed to replace customer data platforms focused on audience management and activation for marketing use cases. However, AEP is not a general-purpose data warehouse - it lacks the SQL query flexibility, BI tool connectors, and cost economics that make cloud data warehouses (Snowflake, BigQuery, Redshift) the right choice for analytical and operational reporting. Most enterprise AEP implementations operate AEP alongside a cloud data warehouse in a complementary architecture: AEP manages the real-time customer profile, audience activation, and journey orchestration, while the data warehouse handles complex analytical queries, finance reporting, and operational data feeds. We design this architecture during the data strategy phase, defining which use cases belong in AEP versus the data warehouse to avoid duplicating infrastructure cost.
A focused Real-Time CDP implementation covering XDM schema design, 3 to 5 source connector configurations, identity graph setup, initial segment library, and 2 to 3 destination activations typically takes 16 to 24 weeks. Adding Adobe Journey Optimizer for triggered journey orchestration adds 8 to 16 weeks depending on the number of journey types and channels. Customer AI model training and validation adds 4 to 6 weeks per model instance after sufficient ingestion data is available. Full-scope AEP programmes covering CDP, AJO, Customer AI, Decision Management, and AEM integration commonly run 30 to 48 weeks. We use a phased delivery approach that activates the first audience segments and journeys at the 16 to 18 week mark, delivering business value while advanced capabilities continue in parallel workstreams.
Our AEP managed services cover data engineering operations (ingestion pipeline monitoring, schema evolution management, and data quality incident response), CDP operations (audience segment maintenance, destination connector management, and activation performance reporting), journey operations (Adobe Journey Optimizer journey monitoring, content updates, and A/B test management), and strategic advisory (quarterly platform health reviews, new use case scoping, and AEP release impact analysis). All managed service clients receive a named AEP Technical Account Manager with deep knowledge of the specific implementation, monthly operational health reports, and bi-annual strategic advisory sessions to align the AEP roadmap with evolving business priorities. AEP is a rapidly evolving platform with three major releases per year, and our managed services team maintains current certification on all new capabilities to advise clients proactively on adoption opportunities.
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