Use Case:
Consider Management Consulting companies like McKinsey, PwC or BCG.
They consult with large scale enterprises in driving growth.
For example, Dabur India has hired PwC to consult, the goal is to grow their Revenue from 12,000 crores (In FY 24)
to 20,000 crores (In FY 28) in 4 years. 66% CAGR
To achieve this they have to transform various functions of their business, like Enhanced Sales Operations, Efficient
Supply Chain, Optimize Manufacturing, Cost Reduction in Procurement etc.
These strategies shall be driven by Individual Resources who head this function.
In the current scenario, Expert SME's conduct detailed assessments through interviews of key personnel from Top
like CEO, CFO, COO about the skills required by the above mentioned Individual Resources to achieve this.
For example, optimizing operations is the responsibility of the COO. They interview the CFO and ask him about how
the current skills (based on a baseline logic or SOP) is aligned with the strategy defined. This is to evaluate the COO
on the skills required by him for the job to be done (which is optimizing operations).
We need an AI Agent to replace the interview done by the SME's, instead of SME's COO will talk to agent and
assessment would be done.
We have a standard dataset of relevant questions, the context of these questions, scoring logic based on the responses.
We need an AI Agent to dynamically ask questions during the assessment to find the logical answers which will be then
converted to a score.
Creating an end-to-end conversational AI agent for this use case involves several components:
1. Natural Language Processing (NLP) Model: To understand and generate human-like responses.
2. Knowledge Base: Contains standard datasets of relevant questions, context, and scoring logic.
3. Dialogue Manager: To manage the flow of conversation and dynamically ask relevant questions.
4. Scoring Engine: To evaluate the responses and generate a score based on predefined logic.
5. Interface: A user-friendly interface for the COO to interact with the AI agent.
Here is a high-level overview of the steps involved:
1. Data Preparation
- Collect Standard Questions: Gather all the questions used by SMEs during the interviews.
- Define Context and Scoring Logic: Clearly define the context for each question and the scoring mechanism.
2. NLP Model
- Select NLP Framework: Use a pre-trained model like GPT-4, BERT, or similar.
- Fine-Tune the Model: Fine-tune the model with your dataset of questions and expected responses to improve its
understanding and generation capabilities.
3. Knowledge Base
- Create a Knowledge Base: Store all the questions, context, and scoring logic in a structured format (e.g., a database).
4. Dialogue Manager
- Develop Dialogue Manager: Create a module to handle the flow of the conversation. This involves selecting the next
question based on previous responses and the context.
5. Scoring Engine
- Implement Scoring Engine: Develop a system to evaluate responses and generate a score. This can be based on
keyword matching, semantic similarity, or other NLP techniques.
6. Interface
- Build User Interface: Create a user-friendly interface for the COO to interact with the AI agent. This could be a web
or mobile application.
Detailed Steps
```python
# Import necessary libraries
import openai
import json
# Initialize OpenAI API (assuming you are using GPT-4)
openai.api_key = 'your-openai-api-key'
# Sample knowledge base (a simplified example)
knowledge_base = {
"questions": [
{
"id": 1,
"question": "How would you describe the current efficiency of the supply chain?",
"context": "Supply Chain Efficiency",
"scoring_logic": "response should mention specific metrics like lead time, cost, and reliability."
},
# Add more questions here
],
"scoring": {
"lead time": 10,
"cost": 10,
"reliability": 10
# Add more scoring criteria here
}
}
# Dialogue Manager
class DialogueManager:
def __init__(self, knowledge_base):
self.knowledge_base = knowledge_base
self.current_question = 0
def get_next_question(self):
if self.current_question < len(self.knowledge_base["questions"]):
question = self.knowledge_base["questions"][self.current_question]
self.current_question += 1
return question["question"]
else:
return None
def score_response(self, response, context):
score = 0
for criterion, points in self.knowledge_base["scoring"].items():
if criterion in response:
score += points
return score
# Instantiate dialogue manager
dialogue_manager = DialogueManager(knowledge_base)
# Function to handle conversation
def handle_conversation(response):
current_question = dialogue_manager.get_next_question()
if current_question:
print(f"AI Agent: {current_question}")
# Assuming `response` is the user's response to the question
score = dialogue_manager.score_response(response, current_question)
print(f"Score: {score}")
else:
print("AI Agent: Thank you for your responses. The assessment is complete.")
# Example interaction
response = "Our supply chain has an average lead time of 5 days, costs are within budget, and reliability is above
95%."
handle_conversation(response)
```
Integration
1. Backend: Set up a backend server to handle API requests and manage state.
2. Frontend: Create a web or mobile interface for user interaction.
3. Deploy Model: Deploy the NLP model and integrate it with the dialogue manager and scoring engine.
4. Testing and Refinement: Test the system with real users and refine the model and logic based on feedback.
By following these steps, you can develop a conversational AI agent that effectively replaces SME interviews and
dynamically assesses the skills of individual resources in a consulting scenario.
To develop a conversational AI agent with a knowledge graph for your use case, you need to gather specific information
and API access from the client. Here is a comprehensive list of requirements:
Information Needed from the Client
1. Business Context and Goals:
- Detailed description of the business goals (e.g., growing revenue from 12,000 crores to 20,000 crores in 4 years).
- Specific functions and departments involved (e.g., Sales Operations, Supply Chain, Manufacturing, Procurement).
2. Subject Matter Expertise (SME) Input:
- Standard dataset of relevant questions used by SMEs.
- Context for each question and expected answers.
- Scoring logic and criteria for evaluating responses.
3. Current Processes and Workflows:
- Detailed documentation of the current assessment processes.
- Any existing SOPs (Standard Operating Procedures) or guidelines.
4. Data and Knowledge Base:
- Access to any internal data that can be used to train and fine-tune the NLP model (e.g., past interview transcripts,
assessment reports).
- Information about key metrics and KPIs relevant to each function.
5. User Information:
- Profiles of the individuals who will be interacting with the AI agent (e.g., COO, CFO).
- Specific skills and competencies required for each role.
API and Technical Requirements
1. Access to Internal Systems:
- APIs to access internal databases and systems relevant to the assessment (e.g., HR systems, performance management
systems).
2. NLP Model API:
- OpenAI GPT-4 or similar NLP model API for language understanding and generation.
3. Knowledge Graph API:
- Access to a knowledge graph API (e.g., Neo4j, Amazon Neptune) to store and query the relationships between
different entities (questions, contexts, responses).
4. Scoring Engine API:
- APIs or libraries for implementing the scoring logic (e.g., text analysis, semantic similarity).
Example API and Integration Points
1. NLP Model (e.g., OpenAI GPT-4):
- API Key: `your-openai-api-key`
- Endpoint: `https://api.openai.com/v1/engines/gpt-4/completions`
2. Knowledge Graph (e.g., Neo4j):
- API Endpoint: `http://localhost:7474/db/data/`
- Authentication: Username/Password or OAuth token
3. Internal Data Access (e.g., HR System):
- API Endpoint: `https://internal-api.company.com/hr-data`
- Authentication: OAuth 2.0 token
4. Scoring Engine (e.g., Custom Scoring Service):
- API Endpoint: `https://internal-api.company.com/scoring`
- Authentication: API Key or OAuth token
Questions to Ask the Client for Production
1. Business and Functional Requirements:
- What are the specific goals and objectives for the AI agent?
- Which functions and departments will be assessed by the AI agent?
- Can you provide detailed documentation of the current assessment processes?
2. Data and Knowledge Base:
- Can you provide access to historical data (e.g., past assessments, interview transcripts)?
- What are the key metrics and KPIs for each function?
- Can you share the standard dataset of questions, context, and scoring criteria?
3. Technical Requirements:
- What internal systems and databases need to be integrated?
- Can you provide API documentation and access credentials for internal systems?
- Are there any specific security and compliance requirements?
4. User Interaction:
- Who are the primary users of the AI agent?
- What are the specific skills and competencies required for each role?
- What kind of user interface is preferred (e.g., web, mobile)?
By gathering this information and accessing the necessary APIs, you can develop a robust conversational AI agent
with a knowledge graph tailored to the client's specific needs.
POC
Questions for the Client
1. Business and Functional Requirements:
- What are the specific goals and objectives for the AI agent?
- Which functions and departments will be assessed by the AI agent?
2. Data and Knowledge Base:
- Can you provide a standard dataset of questions, context, and scoring criteria?
- Can you provide a sample of historical data (e.g., past assessments, interview transcripts)?
3. Technical Requirements:
- What internal systems need to be integrated?
- Can you provide API documentation and access credentials for these systems?
4. User Interaction:
- Who are the primary users of the AI agent?
- What kind of user interface is preferred (e.g., web, mobile)?
Example Project documents
https://github.com/tomasonjo/NeoGPT-Recommender
https://microsoft.github.io/graphrag/
