Workflow and success criteria
A defined process, baseline, pilot boundaries, and measures for quality, time, and cost.
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AI Workflow Pilot
Prove the value of one AI workflow.
Take one repetitive business process from idea to a measurable pilot. Real integrations, a test set, and human review where the workflow needs it.
Operations and product teams with a concrete automation use case
Sound familiar?
AI experiments look impressive but never reach a useful workflow.
Your team keeps copying information between documents and systems.
You need evidence of quality and value before a wider rollout.
What you take away
Deliverables you
can actually use.
The starting package covers one workflow and agreed data or tool integrations. We confirm the exact boundary together.
Working integrated pilot
An agent or assisted workflow connected to the agreed systems, with controlled tool access.
Evaluation and safeguards
Representative test cases, failure handling, human review steps, and logging appropriate to the workflow.
Go / no-go recommendation
A demonstration, measured findings, operating notes, and the next steps for a production rollout.
A practical guide
Test one useful AI workflow before expanding it.
A good AI pilot starts with a process you can describe and a result you can judge. Choose one repetitive task, measure how it works today, and test whether assistance improves it under realistic conditions. That gives you a basis for a practical decision.
How to do it yourself
Begin by following a real example from start to finish. Identify the input, the decision, the output, and the person responsible. Record the time spent and the common mistakes. Decide which part is suitable for assistance and where a person must still review or authorize the result.
Build a small evaluation set from representative examples you are allowed to use. Include ordinary cases, missing information, ambiguous requests, and situations where the workflow should stop. Define the expected outcome before trying different prompts or models, and keep some cases aside for a later check.
Create the simplest working flow. Give it only the data and operations needed for the task. Start with drafted output or a test system, and make external actions explicit. Define what happens when a tool fails or the input cannot support a reliable answer.
Run the examples and inspect the whole result. Measure quality, time, cost per completed case, and the amount of human correction. Record where the process fails, including cases that sound convincing but are wrong. Compare it with the original manual process on the same task.
Review the findings with the process owner. Decide whether to improve the pilot, stop it, or plan a controlled production rollout. Keep the evaluation examples and operating notes so later changes can be checked against the same expectations.
How we approach the work
Our plan builds on practical AI integration and our experience modeling business processes in .NET and connected systems. We separate the domain rules, the model’s role, and the operations that change real data. Clear boundaries make the workflow easier to evaluate and maintain.
We define the pilot’s acceptance criteria with the business owner, implement one complete path, and review the failures as carefully as the successful examples. Human review and deployment decisions stay visible. The outcome is a working pilot with measured findings and a reasoned next step.
The tools behind the work
The selected model API provides the language capability; .NET services and existing APIs handle integration and deterministic business rules where appropriate. MCP, the Model Context Protocol, can expose bounded business operations to an assistant. An evaluation harness records the input, output, tool calls, and review result.
We use SDD, Specs Driven Development, to connect the business objective, workflow specification, evaluation cases, and discovered issues. This gives each prompt or integration change an acceptance target and preserves the evidence behind the go or no-go decision.
Explore the tools
Ready to begin
What we need
from your team.
- A process owner and representative examples
- Authorized access to the agreed data and systems
- A decision-maker to define acceptable quality and review points
A few useful answers
Before we start.
Can the pilot use our existing business tools?
Yes. We scope the connections to your APIs, documents, or internal systems. MCP can provide a reusable tool interface when appropriate; the business workflow determines the design.
How is the engagement priced?
The indicative estimate starts at €10,000, excluding VAT, for the stated package boundary. After a fit conversation, you receive a fixed quote with the exact deliverables, fee, and schedule. Work starts after acceptance; changes are quoted separately.
What happens after delivery?
You receive the working files, findings, and a handover session. Your team can take the next steps, or we can scope a follow-on engagement together.
Let’s talk about AI Workflow Pilot
Your next step
starts here.
Tell us what needs to change.
We’ll review your request and come back with a practical next step. No commitment required.
Prefer email? dev@roggemans.com ↗Discuss this package
A little context helps us make the conversation useful.
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