"Guru has made it really easy for us to implement AI without involving our Product and Engineering teams. Someone like me, who's a senior associate, can make a knowledge agent AI chatbot that our internal teams use for actual, true efficiency impacts."
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— A Senior Operations Associate

The Challenge: Demanding High Quality from our AI

This healthcare company is a large global telehealth and benefits company. They provide telehealth appointments and reimbursement services for fertility and family-building benefits across thousands of employer clients. Their customer-facing operations teams field complex, sensitive questions where inaccurate answers carry serious medical, financial, and legal consequences.

Before the Benefits AI Agent, the company's support reps faced a daunting workflow. Answering a single member question often required synthesizing information across fine-print documents, SOPs, Google Drive folders, and prior Slack threads — a time-consuming process that became unsustainable at scale.

Problem 1: Scattered Documentation, No Single Source of Truth

The company's information was spread across multiple collections, drive folders, and Slack threads. The content team was small, and much of the documentation hadn't been written with AI in mind — full of industry jargon, implicit assumptions, and language that was clear to a tenured human but opaque to an AI agent.

"Our support team had to go through various fine print documents — all to synthesize an answer. That takes a lot of time. And when you're a customer support agent, that time isn't necessarily something you're able to take."
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— A Senior Operations Associate

Problem 2: High Bar for Accuracy

One of the company's core tenets is "keep healthcare human," and any AI implementation had to enhance human performance and maintain a high bar for quality and accuracy, not replace critical thinking.

"We are really using Guru to enhance our human performance and not replace it. We know that the individual reasoning and critical thinking a human has is still unique compared to the majority of AI Agents."
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— A Senior Operations Associate

Problem 3: Trust Had to Be Built From Day One

They knew from experience that tool adoption at their company — and most companies — lives or dies in the first week. If the Benefits Agent surfaced inaccurate answers early, the team would write it off permanently, regardless of later improvements. This meant governance couldn't be an afterthought; it had to be foundational.

"If your team doesn't trust the tool from day one, they're not going to adopt it later when you fix it."
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— A Senior Operations Associate

The Approach: Citation-First AI With Human Oversight Built In

Rather than deploying a general-purpose agent and hoping for the best, they took a methodical, multi-phase approach: clean the content, constrain the prompt, restrict access during testing, and build governance into the workflow from day one.

1. Scoping Content Tightly

The first lesson they learned was perhaps counterintuitive to some: less is more when it comes to Knowledge Agent sources. Rather than connecting every collection "just in case," they identified exactly what the Benefits Agent needed — and nothing more. Broader scope introduced the risk of the agent pulling from irrelevant or outdated content and producing confusing answers.

"More isn't merrier when it comes to knowledge agent resources. We found that if we included broad information just in case we needed it, it just introduced the risk of misunderstanding."
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— A Senior Operations Associate

2. Cleaning Up the Knowledge Foundation

A small team of SMEs spent weeks reviewing the highest-use materials — verifying accuracy, removing jargon, and using Guru's AI editing tools to make content more AI-readable. The principle was simple but non-negotiable: if the content isn't clear enough for an AI agent, it's not clear enough for a new human agent either.

"Garbage in, garbage out. If your resources are not clear and correct, your knowledge agent's response won't be either. And it's not the knowledge agent's fault. It's the resources that it was pulling from."
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— A Senior Operations Associate

3. Citation-First Prompt Engineering

The prompt was designed to prioritize direct citations over synthesized summaries — making every response auditable and reducing hallucination risk. They also built a 9-step sequential evaluation into the prompt, and required an organization name in every question before the agent would proceed. This single constraint dramatically improved accuracy by forcing the agent to anchor every answer to a specific client's program documents.

"We really focused on citations over summarization. We customized our agent prompt to cite where it found information to answer the question, not just create a general summary of an answer based on multiple sources."
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— A Senior Operations Associate

4. Tiered Rollout and Daily QA

Rather than opening the agent to the full team immediately, they ran a restricted testing process: first themselves, then a handful of admins, then senior team members with the background knowledge to challenge incorrect answers. Only after this tiered validation did the agent go live to the broader team.

Even post-launch, two SMEs conduct daily QA. When an inaccurate answer is flagged, the team corrects the information.

"We didn't want them to just blind faith trust our agent. We wanted them to be able to critically think like humans do very well — to review the output and then decide if they need to look deeper."
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— A Senior Operations Associate

Results: Trust Built Through Governance

• 🚀 16% reduction in peer-to-peer Slack questions — across the company's operations teams, before the specialized Benefits AI Agent was even deployed
• 📈 700+ questions per month to the Benefits AI Agent
• 🔥 17% higher productivity for Guru AI super-users — new hires who actively used the Guru AI Agent ramped faster than their peers

Within months of launch, Guru's AI chat feature drove a 16% reduction in peer-to-peer Slack questions across the company's operations teams — before the specialized Benefits AI Agent was even deployed. By making it easy for their team to self-serve, early data from the newest training cohorts shows that new hires who actively used the Guru AI Agent had higher productivity rates and ramped faster than their peers. That productivity boost applies to users of all tenures — Guru AI super-users are generally more productive than their peers. The company's governance investment didn't slow adoption — it accelerated it by building the trust needed for teams to rely on the tool.

"Guru has been my savior since I started working on my own out of training!"
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— New hire, Benefits Operations

What's Next: From One Agent to a Platform

With the Benefits AI Agent a proven success, the company is exploring how to extend the citation-first, governance-heavy approach to other teams and use cases. The daily QA process has surfaced knowledge gaps that are improving training programs — turning the agent into a diagnostic tool for organizational learning, not just a support accelerator.

The AI agent and a brand new human agent go through the same processes. If your resources aren't clear to AI, they won't be clear to a new hire either.

"The investment in content quality and governance isn't just powering one bot — it's building the foundation for every AI tool the company will deploy going forward."
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— A Senior Operations Associate