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 stageYour AI implementation partner
AI & Agentic AI Implementation Services
Connect AI to a defined business task and the systems it needs. We assess the use case, build integrations and test the workflow. For agents, we agree which tools they may use, which actions need approval and when they must stop.
Discuss an AI ImplementationAgentic AI implementation
Give an agent a defined task and clear limits.
An AI agent uses a model to choose its next steps and tools as it works toward a task. An implementation connects those tools to your systems and defines what the agent may read, draft or change.
Start with a task that needs that flexibility. A fixed workflow, a document extraction step or RPA implementation may be enough for predictable work. We compare the approaches before agreeing the design.
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 stageThree implementation patterns to assess
These examples illustrate possible designs. Suitability depends on your inputs, system access and evaluation results.
Agentic workflows
Use approved tools to gather information and choose the next step. For example, an agent could check supporting records for an invoice exception and draft a review packet. Payment approval stays with the authorized person.
Intelligent document processing
Extract and organize information from varied documents. Check fields against the source and route uncertain values for review before updating a system.
Knowledge and exception support
Ground a summary or suggested next step in approved reference material. Retrieval-augmented generation (RAG) can provide relevant sources for a reviewer to check.
Read the RPA and agentic AI decision guide for the questions behind a technology choice.
Deliverables to agree
Define the evidence for a production decision.
The proposal identifies the task, connected systems and allowed actions. Acceptance criteria determine whether the workflow is ready for release.
- Task and integration design: inputs, approved tools, data access and action limits.
- Evaluation evidence: representative cases, source checks, failure cases and quality measures.
- Control checks: human approval, stopping conditions, escalation and tests for unintended tool use or misleading instructions.
- Operating handover: monitoring, incident owners and a plan to reassess results when models, prompts or systems change.
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 context helps us identify exceptions and review responsibilities for an implementation.
Read about the team's prior delivery experience or explore our RPA and AI implementation services.
Before you build
Check that one use case is ready.
For example, an invoice assistant could draft fields for review. Payment approval stays with the authorized person.
- Inputs: collect sample invoices, including poor scans and missing fields.
- Quality: agree which fields must be correct. Test normal cases and failures.
- Review: name the person who checks uncertain outputs.
- Actions: define what the system may read, draft or change.
- Support: agree how to detect errors, stop the workflow and recover.
Use an opportunity assessment to choose the task. A feasibility review checks the evidence before investment.
AI and agentic 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.
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