Engineering

Insights and analysis on AI and machine learning from our engineering team.

Claims Bring Stress and Urgency: Why Your Unified System Matters

John McMullan

Director of AI Agent Marketing

AI agents fail when knowledge isn’t structured. Learn how to refactor existing content into execution-ready guidance for humans and AI.

A Practical Guide to Structuring Knowledge for AI Agents

Ana Dippell

AI Agent Experience Designer

Customers or agents always make calls or initiate an interaction for a specific reason or objective – which we define as an interaction driver.

How Observe.AI Decodes Conversations to Identify Multi-Level Contact Reasons

Anup Pattnaik

Staff Machine Learning Engineer

Modern ASR struggles with rare/OOV words in contact centers. Spot and Merge (SAM) uses LoRA adapters, phrase spotting, and dynamic merging to boost accuracy. It scales to large bias lists, improves rare word detection, and reduces WER without retraining.

How Observe.AI improves transcription accuracy for rare, domain-specific and out-of-vocabulary terms

Bramhendra K

Sr. Machine learning Scientist

AI call summaries can be factually correct yet still distort sentiment, tone, or key details. BlindSpot, Observe.AI’s new framework, uncovers and mitigates these hidden biases across five dimensions—helping contact centers ensure summaries are accurate, fair, and business-ready.

Mitigating Bias in AI Call Summaries: Introducing BlindSpot for Contact Centers

Kawin M

The launch of GPT-5 brings impressive reasoning and benchmark wins - but deploying it in live contact-center workflows isn’t a plug-and-play upgrade. Our study benchmarked 17 LLMs across six core NLU task categories, analyzing accuracy, reasoning, and latency.

What Does GPT-5 Mean for Contact-Center Tasks?

Ayush Kumar

Manager, Machine Learning

A deep dive into the architecture, strategy, and execution behind one of our largest and most complex data migrations to date.

How Observe.ai Migrated 1.8 Billion Documents with High Throughput and Zero Downtime

Rehmanali Momin

Rehmanali Momin

Software Development Engineer III

Let's start with the obvious: LLMs are revolutionary. They’re no longer just tools for information retrieval—they’re powering smarter, faster, and more human-like experiences across our daily lives. From the apps on your phone and computer to the AI assistants in your contact center, AI is no longer a novelty. It’s a necessity. But here’s what is less obvious: Both the way we prompt and the LLM we are prompting have more sway in the output than everything else.

Why Prompt Optimization Is The Missing Link in Contact Center AI

Kawin M

In Asynchronous systems, logs are vital for debugging, yet traditional approaches falter as systems scale. This is the journey of transforming them into a developer's most powerful ally.

Logs Reimagined: Elevating Observability & Debugging in Asynchronous Systems

Tejas Kothari

Software Development Engineer

Observe.AI Resource: One Sheet

2021 Post-Pandemic Contact Center Report

The Agent Performance Handbook

The Agent Performance Handbook

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