Perficient

perficient.com

Perficient’s offerings extend beyond advisory work into application development, platform integration, deployment, and operations. Its AI practice explicitly offers model, API, and agent integration, supported by reported client implementations. Packaged engagements require closer scope review: AI AMP produces demonstration prototypes rather than production systems, while Agentforce Activation separates production deployment and support from its activation deliverables. ^[1] ^[2] ^[3] ^[4]

Services and delivery

Production AI and generative-AI engineering

Implementation. Architects, deploys, and integrates AI systems with existing applications. Deliverables include retrieval-augmented generation, intelligent search, automation, multi-agent orchestration, monitoring, and observability. ^[2]

AWS AI/ML platform engineering

Implementation. Delivers AI/ML workflows from model selection and enhancement through Amazon Bedrock architectures. Named technologies include AgentCore and SageMaker. Its AgentCore Accelerator targets a production-ready runtime with observability, identity controls, access boundaries, and audit trails. ^[5]

AI AMP assessment and planning

Advisory. Provides stakeholder workshops, technical assessment, prioritization of up to ten AI use cases, and a recommended execution plan. This is advisory work within a package that also includes limited prototyping, not a production deployment commitment. ^[3]

AI AMP demonstration prototyping

Implementation. Produces functional prototype code for one or two selected AI use cases. The package is ecosystem-agnostic; its prototypes are for demonstration and require further work before production deployment. ^[3]

Salesforce Agentforce Activation

Implementation. Offers two- or six-week packages covering one predefined use case, one agent with up to two topics, Salesforce-data-only RAG, limited actions, configuration, governance setup, and UAT support. Production deployment, custom authentication, and hands-on production support are additional services. ^[4]

PACE AI governance advisory

Advisory. Assesses AI readiness and develops policies, governance standards, training, and model-audit practices. Buyers should distinguish this advisory scope from PACE’s technical oversight and deployment work. ^[6]

PACE technical AI enablement

Implementation. Implements model oversight and safety systems, monitoring, reusable architectures, deployment patterns, and structured pilots. These technical deliverables should be specified separately from policy and training activities. ^[6]

Cloud modernization and migration

Implementation. Refactors and migrates applications across AWS, Microsoft Azure, and Google Cloud. Work includes containers, automated provisioning, recovery, CI/CD, and API integration. ^[7]

AWS cloud operations

Managed services. Provides ongoing cloud operations using CloudFormation and Terraform, monitoring, resilience engineering, and continuous optimization. Contractual support coverage and service levels require agreement. ^[5]

Software engineering and application integration

Implementation. Builds custom applications, native and cross-platform mobile apps, middleware, APIs, and event-driven integrations. Modernization includes portfolio assessment, refactoring, replatforming, and rebuilds, with Java, .NET, infrastructure as code, CI/CD, and observability among the documented environments and methods. ^[1]

Data platform engineering

Implementation. Implements, integrates, and modernizes data platforms. Deliverables include batch and streaming pipelines, DataOps automation, data architecture, master data management, and operational governance. This scope establishes data engineering, not AI/ML delivery by itself. ^[8]

Company details

Track record

For an unnamed automotive manufacturer, Perficient reports building a customer-facing assistant using Google Vertex AI, custom LLM training, Vertex AI Search, multilingual voice support, and dealer-system connections. It reports approximately 5,000 daily conversations and a 36% increase in quote requests. ^[9]

For an unnamed insurance broker, Perficient reports deploying multi-agent quote processing combining OCR, intelligent character recognition, and LLMs, with submission into Salesforce. It reports more than 90% less processing time. Model vendors and detailed evaluation methods are not disclosed in the notes. ^[10]

A financial-services case describes an Apigee Edge-to-Apigee X migration with identity integration, CI/CD, rollback, and blue-green/canary deployment. Perficient reports zero client impact while migrating a workload handling 26 million daily API calls. ^[11]

For an outdoor sports and retail company, Perficient reports implementing Coveo and a Next.js headless front end, using AI models for merchandising and product placement. It reports more than 10% higher conversions and halved page-load times. These are firm-reported outcomes, not general performance guarantees. ^[12]

Scope and limitations

Several case-study clients are anonymous, limiting independent reference validation; reported outcomes are not independently verified.
AI AMP demonstration code and Agentforce Activation prototypes should not be treated as commitments to production readiness.
The notes do not establish standalone licensing terms for the GenAI Integrity Accelerator; it is not classified here as a separately licensed product.
The supplied notes support AWS operations, but their verbatim AWS excerpt concerns AI/ML rather than operational scope. Buyers should verify operational responsibilities and support commitments.

Questions to ask

Who owns delivery, and which acceptance criteria distinguish a successful prototype from a production-ready system?
What model evaluation thresholds, human-review controls, monitoring, and incident procedures will be included?
Who owns the code, data, model configurations, and deployment assets, and what platform licenses are required?
Which integrations, authentication changes, production deployment tasks, and support services are excluded from the proposed package?
What support hours, SLAs, recovery commitments, and named references apply to the proposed workload?

Overview