AI Engine Assess the workflow
Identify the task, available data, review needs and intended outcome. Compare AI with rules, APIs and process redesign before selecting an approach.
Explore this stageAI implementation partner for enterprise operations
Enterprise AI implementation, built around the workflow.
We help teams with enterprise and agentic AI implementation for document-heavy and knowledge-driven work, with validation, human review and production ownership designed into the solution.
Discuss an AI WorkflowWhat the service covers
Connect AI capability with a usable business process.
Enterprise AI implementation connects models, including large language models (LLMs), with the data, systems, controls and people needed to use their outputs. The work includes selecting suitable tasks, designing integrations, evaluating performance and planning operations.
Our role as an AI implementation partner is to make those responsibilities explicit. A discovery engagement defines what to build, what needs human judgment and how success will be assessed.
From use-case assessment to production support
Engage for a scoped implementation or a specific stage in the delivery lifecycle.
Design the integration
Define document intake, retrieval where needed, system permissions, validation and reviewer interfaces around your existing operation.
Explore this stageEvaluate and implement
Test against representative examples and failure cases. Agree acceptance criteria for output quality, latency, escalation and human approval.
Explore this stageOperate with oversight
Plan monitoring, access controls, incident response and evaluation as data, prompts, models or connected systems change.
Explore this stageWhere AI assistance can be useful
These are illustrative workflow patterns. Suitability depends on your inputs, controls and evaluation results.
Intelligent document processing (IDP)
Extract and organize information from varied documents, with checks against source material and review for uncertain fields.
Knowledge and exception support
Where appropriate, use retrieval-augmented generation (RAG) to ground a summary or suggested next step in relevant reference material, with source checks and human review.
Agentic AI implementation & orchestration
Consider agents where tasks need dynamic sequencing or tool selection. Define tool permissions, allowed actions, stopping conditions and approval boundaries before deployment. Evaluate prompt injection and unintended tool use alongside task performance.
A model-assisted step does not automatically need an agent. Predictable transactions may fit RPA implementation, APIs or workflow automation. Explore the RPA and AI decision guide.
The people behind the approach
Process experience informs the design.
Our team's prior experience includes healthcare revenue cycle management (RCM), procure-to-pay (P2P), order-to-cash (O2C) and record-to-report (R2R), and manufacturing invoice and dispute workflows. That operating context helps us identify the exceptions and review responsibilities an implementation must address.
Read about the team's prior delivery experience or explore our RPA consulting and automation services.
AI implementation questions
Scope, integration and operating considerations for a first engagement.
What does agentic AI implementation include?
Agentic AI implementation connects an agent with approved tools and business workflows. The scope includes deciding whether dynamic sequencing is useful, defining permissions and approval boundaries, evaluating task performance and unintended actions, and planning production oversight.
What does an AI implementation partner do?
An AI implementation partner connects models with business workflows, data and existing systems. Our scope can include use-case assessment, integration design, evaluation, human review and production support, with responsibilities and deliverables agreed before work begins.
Does every AI workflow need an agent?
No. A task may need document extraction, a model-assisted step, a rules-based workflow or human judgment. Agentic tool use is considered where dynamic sequencing is useful and actions can be controlled and evaluated.
Can AI work alongside existing RPA automations?
AI-assisted steps can be integrated with RPA, APIs and workflow platforms where interfaces and controls allow. For example, a model may suggest an interpretation while validation rules and a reviewer control the action taken.
How do you assess implementation readiness?
We examine sample inputs, target outcomes, system access, data permissions, exception paths and review responsibilities. Representative evaluation cases and acceptance criteria are defined before a production rollout.
Who owns the workflow after launch?
Operating responsibilities are agreed during the engagement. The handover can include monitoring, incident escalation, evaluation procedures and support coverage appropriate to the workflow.
Explore managed support →