AI Workflows & Agentic AI / Case study

micro1.

Evaluating AI where real work happens.

100+ real-world workflows designed, executed and evaluated across AI models, with structured human feedback grounded in business needs.

  • Workflow design
  • Multi-model evaluation
  • Human feedback
Explore the project
micro1 AI workflow design and evaluation illustration connecting business tasks, model comparison and human feedback.
The challenge
micro1 needed realistic business workflows to test models beyond isolated questions and answers. Each task had to reflect practical operations while remaining clear enough for consistent evaluation across models.
Our contribution
Engross worked as a dedicated workflow evaluation team: researching business scenarios, writing detailed instructions, executing tasks, comparing model outputs and providing structured human feedback against micro1’s evaluation guidelines.
The outcome
100+ business workflows created and evaluated, supported by a repeatable research, execution, review and refinement process. The engagement gave micro1 practical evaluation scenarios and human feedback to support model improvement.
Technology & tools
ChatGPT Pro · OpenAI Codex · Feather Evaluation Platform · Custom workflow templates

What the work involved

  • Research business scenarios and define complete, multi-step tasks.
  • Create workflow instructions and evaluation scenarios.
  • Execute tasks across models and compare their responses and actions.
  • Identify quality issues and provide structured human feedback.
  • Review and refine workflows against client-defined quality requirements.

From business requirement to evaluation

  1. Business scenario
  2. Workflow design
  3. Model execution
  4. Model comparison
  5. Human evaluation
  6. Structured feedback
  7. Refinement

Business workflows covered

  • Pre-sales
  • Post-sales
  • Bookkeeping & finance
  • HR & internal operations
  • SMB business operations

How quality was evaluated

Accuracy
Does the result correctly address the task?
Instruction following
Does execution follow the workflow requirements?
Completeness
Are all required parts of the task covered?
Business relevance
Is the result useful in a real business setting?
Reasoning & quality
Is the approach logical and reliable?
Consistency
Are results dependable across model runs?

Structured feedback supported model improvement and ongoing workflow refinement, with people evaluating the usefulness of the work.

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