Scaling Enterprise Productivity with LLM Workflows: A Case Study in Recruitment

Beyond ChatGPT: Why Enterprises Need LLM Workflows

In the rush to integrate AI into business processes, many enterprises experiment with ChatGPT for automation. But here’s the reality: integrating a chatbot is not the same as designing a structured, scalable workflow that delivers consistent and reliable outcomes.

At DeepDive Labs, we specialize in LLM workflows—breaking down enterprise processes into structured, language vs non-language and reproducible tasks that can be orchestrated into a seamless workflow. Unlike standalone AI responses, an LLM workflow ensures accuracy, consistency, and business-ready automation.

Across APAC enterprises, we’ve applied these workflows to recruitment, compliance, edtech, and process automation, helping companies unlock efficiency while maintaining human oversight. One such case involved optimizing interview evaluations for a recruitment agency, reducing their report generation time and benchmarking human vs. LLM-based assessments. If you’re exploring LLM automation beyond chatbot & basic chatGPT prompts, read on for insights into how enterprises can scale productivity with structured LLM workflows.

The Challenge: Enhancing Productivity & Human Review

A leading recruitment agency in APAC had two key objectives:

1. Reduce the time required to generate interview reports by at least an hour (from an average of 10 hours).

2. Compare human evaluations with an LLM-based assessment to analyze correlations and identify gaps where automation could enhance decision-making.

This challenge is not unique to recruitment. The key question was: How do we structure the current process so LLMs can augment human expertise, rather than replace it? So it was crucial for us to understand the following questions:

• Should this workflow apply across multiple job roles?

• What structured abstractions are available for each role?

• What are the key language tasks involved?

• What different data needed to be analyzed to generate an effective report?

• Are panelists asking consistent, comparable questions across candidates for the same role?

Many enterprise workflows—whether in recruitment, compliance, or finance—can be broken into modular, structured evaluations.

Structuring the LLM Workflow: A Multi-Step Approach

Interviews might seem like simple Q&A sessions, but evaluation is a multi-layered process. Similarly, enterprise workflows that involve human reviews—such as contract risk assessments or compliance audits—require structured evaluation criteria.

For recruitment, we requested:

1. A transcript with speaker diarization (separating interviewer and candidate responses).

2. A list of skills the candidate was evaluated on.

3. The job role being assessed.

Although, today we take transcript for a give, common issues faced in step 1 are:

1. Speech-to-text accuracy for Indian accents – Many existing models struggled with specific pronunciation variations.

2. Speaker diarization errors – The separation between interviewers and candidates wasn’t always accurate due to recording conditions.

Despite challenges, we optimized our LLM workflow to extract meaningful insights.

LLM Workflow

Orchestrating the LLM Workflow

Our workflow was broken down into reproducible, modular sub-tasks, ensuring structured evaluation rather than relying on generic AI responses.

Step 1: Extract & Tag Questions

• The LLM identified all questions from the transcript.

• Each question was tagged to a specific skill category (e.g., "AWS," "DevOps", “NodeJS”). Categorizing skills needs a deepdive of itself!

Step 2: Extract Question & Answers Pairs

• Candidate responses were identified, forming structured question-answer (Q&A) pairs.

• Responses were mapped to predefined skill sets for analysis.

Step 3: LLM-Based Scoring & Calibration

• Each Q&A pair was assessed with an LLM-based evaluation prompt, assigning a 0-5 score.

• Calibration examples ensured consistency across different job roles and industries.

• Each answer was analyzed for:

  • Relevance – Did the response truly address the question?

  • Sufficiency – Was the answer complete and detailed?

• Predefined scoring examples ensured reliable output.

Step 4: Aggregating Results & Generating Enterprise-Grade Reports

Once individual responses were evaluated, scores were aggregated at the skill level. However, a raw score isn’t enough for decision-making in recruitment—or any human-reviewed process.

• Qualitative feedback was consolidated at the skill level.

• Structured reasoning accompanied numerical scores, ensuring transparency in decision-making.

This is a crucial differentiator for enterprises using LLMs: The workflow doesn’t just generate a score—it provides explainability and structured insights for reports at the skill level. This workflow was tested across multiple tech recruitment roles:

• Salesforce Developer

• Data Analyst

• DevOps Engineer, etc.

The results? The workflow performed consistently across different tech roles, proving it to be scalable and adaptable across various hiring needs.

The Enterprise Impact:

• Faster Evaluations – The structured workflow reduced report generation time, improving operational efficiency.

• Augmenting Human Decision-Making – The LLM’s evaluation correlated well with human assessments, making it a valuable tool for scaling reviews.

• Scalability Across Industries – Whether in hiring, compliance, or contract analysis, structured LLM workflows can enhance productivity while keeping human oversight central.