Introduction
A leading two-wheeler automobile manufacturer deployed a generative AI application to accelerate sales-team productivity. The AI processed complex documents to answer real-time queries about competitor pricing, product specs, and customer enquiries. However, the lack of visibility into why the AI generated specific outputs led to:
- Silent errors (e.g., incorrect pricing comparisons).
- Compliance risks from unchecked outputs.
- Missed market shifts as competitor data evolved.
The brand partnered with Akaike to build an AI Observability Dashboard, a single pane of glass that monitors response quality and ensures ethical outputs.
The Challenge
The brand team faced critical observability blind spots in their LLMOps implementation:
- Response Quality & Reliability: Sales agents couldn't trust outputs without validation (e.g., "Is this competitor's pricing up-to-date?"). The system's black-box nature makes it difficult to trace why specific outputs were generated.
- Model Performance Degradation: As the AI system continued running, its performance would gradually degrade without clear indicators of when or why this was happening, creating an unpredictable user experience for the sales team.
- Hallucination Detection: The AI occasionally produced false information when confronted with queries outside its knowledge domain. Despite being erroneous, these responses were delivered with high confidence, a dangerous scenario when dealing with competitive pricing-related data.
- Chain Visibility & Debugging: The multi-step process of retrieving information from internal PDFs and generating appropriate responses remained opaque. This made it impossible to identify which component was causing errors in the final output.
- Cost & Performance Optimization: The team couldn't optimize for response quality and operational costs without proper token usage tracking and performance metrics. This led to inefficient resource allocation.
- Compliance & Safety Guardrails: The system lacked proper audit mechanisms for detecting biased comparisons, PII leaks, or potentially harmful outputs that could create regulatory and reputational risks.
These challenges revealed that traditional monitoring approaches were insufficient for generative AI applications, where probabilistic outputs require qualitative evaluation alongside quantitative metrics. Most importantly, the non-technical business team had no visibility into the AI system's operations, limiting their ability to make informed decisions.
Why Observability Was Non-Negotiable
The stakes were high for the brand:
- Reputational Risk: A single pricing error could cost a customer
- Operational Efficiency: Sales teams wasted hours verifying AI outputs manually
The Solution: Akaike’s Observability Dashboard
Akaike designed a comprehensive dashboard to monitor the brand's AI health across three pillars, providing a single pane of glass that enabled both technical and non-technical team members to review application performance:
Step 1: Real-Time Response Quality Monitoring
- Accuracy Validation:
- Used LLMs as judges to evaluate response quality through semantic similarity and factual correctness scoring.
- Implemented MLflow tracking to measure how grounded responses were in the source data.
- Identified hallucination instances where the system would confidently share incorrect information (e.g., when asked about Bike X's price, it would confidently share Bike Y's price as if it were for Bike X).
- Flagged low-confidence answers for manual review.
- Bias & Fairness:
- Tracked overrepresentation of Client X’s models vs. competitors (e.g., “Why is our scooter recommended 80% of the time?”).
Step 2: Proactive Model Performance Monitoring
- Performance Metrics:
- Monitored latency, GPU utilization, and token consumption to provide insights into the overall cost of running the application.
- Used MLflow to track accuracy drops in production vs. staging environments, automating retraining workflows.
- Set up alerts when model performance metrics fall below established thresholds.
- RAG System Monitoring:
- Tracked the quality and relevance of PDF information retrieval before it reached the LLM.
- Implemented visualization tools that showed the connection between retrieved context and generated answers.
Step 3: Closing the Loop with Human Feedback
- Sales Agent Ratings:
- Integrated a 1–5 star rating system; low scores correlated with data freshness or drift issues.
- Example: A 2-star rating spike led engineers to discover outdated competitor specs in training data.
- Compliance Safeguards:
- Scanned outputs for PII (e.g., leaked customer emails) and biased language (Azure AI redaction).
- Generated audit logs for regulatory reviews.
The dashboard's intuitive visual interface enabled non-technical business users to monitor key performance indicators, track response quality trends, and identify potential issues before they impacted customer interactions. All this, without requiring specialized AI expertise.
Impact & Outcomes
For the brand team:
- 15% Improvement in response accuracy within 6 weeks.
- 90% Faster Drift Detection: Model decay alerts reduced from 14 days to <24 hours.
- Zero Compliance Escalations post-deployment.
Business Impact:
- Sales teams redirected 200+ hours/month from manual verification to customer engagement.
- Leadership approved scaling the AI tool to 5 new markets, citing the dashboard’s transparency.
Why This Matters for Every Gen AI Application
The brand’s success highlights universal truths about generative AI:
- Trust is Built on Transparency: Observability turns “black-box” AI into an accountable system.
- Drift is Inevitable: Proactive detection prevents costly, silent failures.
- Human Feedback is Critical: End-user ratings align AI with real-world needs.
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