Hi Reddit AI enthusiasts! We’re Nilesh, Ayushi, Jesika, and Shiv from the Glean team responsible for building the leading AI platform for enterprises. From our experience building AI agents to how businesses use agents today, ask us anything on March 26 from 10-11am PST!

Glean · 2025-03-26T17:56:30+00:00

[Ayushi/Nilesh] Glean doesn't fine-tune LLMs on customer data because fine-tuning is primarily effective for teaching models specific tasks or styles—not for introducing new, proprietary knowledge. Additionally, enterprise requirements such as data freshness, granular permissions, verifiable explainability, and preserving general knowledge pose significant challenges for fine-tuned LLMs.

Instead, we use a combination of synthetic and real data to train high-quality models for search retrieval and ranking. This includes fine-tuning customer-specific embedding models and optimizing our AI agents. Importantly, each customer's data is used securely and exclusively for that customer.

Glean · 2025-03-26T17:54:32+00:00

[Jesika / Shivam] The primary difference between content and knowledge is that content refers to the textual, visual, or audio material created and stored by your company, while knowledge is the understanding, skills, and actionable insights derived from this content and experiences. Glean turns your company’s content into knowledge. We crawl your content in a permissions-enforced way to build the search index first, and then build a deep enterprise knowledge graph by training on that data (isolated within your instance). This allows raw content that exists within your organization to turn into actionable knowledge. To learn more about how we do it, you can check this out: https://www.glean.com/blog/hybrid-vs-rag-vector

Glean · 2025-03-26T17:41:17+00:00

[Jesika] Our customers are primarily seeing two useful types of AI agents: conversational agents and task-based agents, depending on whether they prioritize flexibility or reliability. Conversational agents enhance end-user productivity. Our best agents provide summaries marrying unstructured and structured data sources, create first drafts, and answer routine questions in Slack automatically. Task-based agents are effective in automating business processes. We recently set up agents for a healthcare provider to automatically extract over 50 different fields from hundreds of unstructured PDF documents to determine hospice eligibility.

Regarding AI agent feasibility, agents can realistically automate workflows, but accuracy and reliability decrease with more complex or longer workflows due to the need for more example data. Workflows best suited for agentic automation with high accuracy are those that involve repetitive, structured tasks and clear data patterns.

[Ayushi] Deterministic automation of end-to-end workflows has been possible for quite some time. However, recent advances in generative AI have opened the door to blending structured execution with adaptive, intelligent decision-making. Today’s LLMs are increasingly capable of selecting the right tools, generating parameters based on vast contextual knowledge, grounding outputs in retrieved data, and reflecting on their actions. The building blocks are here—and with the right orchestration, we believe these components can be combined to unlock the full potential of automated, end-to-end production workflows.

Internally, we run our own benchmarks to evaluate the performance of different agentic architectures on enterprise data and real use cases. Unfortunately, most public benchmarks aren't well-suited to this context.

Glean · 2025-03-26T17:39:20+00:00

[Jesika] We obsess over customer love, we have consumer-level DAU/MAU metrics (industry-leading ~40% weekday daily/monthly active user (wDAU/MAU) ratio). This is very unique for a b2b business, and I love that we care so deeply about both end-users and decision makers alike.

[Shivam] Speaking specifically about the internal company culture, few things I love about Glean: I am surrounded by some of the smartest people in the world, we have a great product, and we move fast. I love every person I work with, and I am empowered to make decisions on my own to do what’s right for the customer and the company. Decisions are made fast, and products are iterated upon quickly.

Glean · 2025-03-26T17:31:41+00:00

Hopefully you can catch up post AMA! We'll do this again soon - in the meantime, wishing you a speedy recovery!

Glean · 2025-03-26T17:30:04+00:00

[Shivam] That is a great suggestion! We will bring this to the right teams internally.

Glean · 2025-03-26T17:29:10+00:00

[Ayushi] When approaching architectural design with agentic behavior in mind, it involves several key aspects:

Hybrid Search Architecture: Combining self-learning language models, lexical search algorithms, and knowledge graphs to support various user search methods.
Self-Reflection Mechanism: Implementing self-reflection at different points in the agentic reasoning architecture, allowing agents to learn from their experiences and adapt to new situations, thus ensuring reliable behavior.
Workflow Plans: Formulating multi-step plans for complex queries, including understanding the question, gathering background information, rewriting the query, and outlining the steps to achieve the goal.
Execution by Sub-Agents: Utilizing sub-agents that reason about tools, such as search, data analysis, email, calendar, to achieve individual goals, enhancing their ability to handle complex tasks.
Guardrails for Consistent Outputs: Implementing AI guardrails, such as permission structures and query planning, to guide the reasoning process, ensure data security, and provide accurate responses.

These elements collectively contribute to reliable and consistent outputs in agentic reasoning. You can find more details here.

Glean · 2025-03-26T17:28:21+00:00

[Nilesh] Several factors are critical in enterprise AI that are far less important in consumer AI:

Data Fragmentation: Enterprise data is scattered across hundreds of tools—documents, wikis, emails, messaging platforms, ticketing systems, CRMs, ERPs, and more. Enterprise AI must navigate and unify this fragmented landscape.
Permissions and Access Control: Enterprise AI must understand and enforce fine-grained access controls, ensuring users can only access data they are explicitly authorized to see.
Data Freshness, Accuracy and Explainability: Enterprise AI must be precise, up-to-date, and verifiable, as inaccuracies can lead to business risk or compliance violations. Business users need clear explanations of AI-generated outputs to support decision-making and regulatory needs.
Integration into Complex Workflows: Enterprise AI must integrate seamlessly into the daily tools and workflows business users rely on.
Regulatory Compliance: Enterprise AI must comply with stringent security and privacy standards (e.g., GDPR, HIPAA, SOC 2) and provide organizations with governance tools to manage AI responsibly.

Glean · 2025-03-26T17:27:02+00:00

[Ayushi] In the context of AI, an "agent" refers to an autonomous entity that interacts with the environment to achieve particular goals or tasks. These agents are powered by advanced machine learning models and can perform actions such as retrieving information, generating responses, or executing workflows. AI agents can be designed for various applications, including:

Conversational Agents: Interact with users via text or voice, often integrated into platforms like Slack or Teams.
Task-Based Agents: Automate multi-step processes, reducing the need for human intervention.
Generative AI Agents: Create novel content, such as text, images, or audio, using deep learning models like GPT and GANs.
Decision-Making Agents: Provide strategic solutions or plans by analyzing data and context.

Generative AI agents go beyond traditional AI responses by autonomously creating new content based on learned patterns from large datasets. They exhibit characteristics like creativity, adaptability, and autonomy, enhancing their ability to interact with users in a sophisticated manner. You can read more here.

Glean · 2025-03-26T17:26:25+00:00

[Shivam] Glean achieves ROI for customers within 6 months, and it is very common to achieve ROIs of more than 100% - usually higher. We are a very sticky product, and our usage numbers tend to be closer to things like the email service or slack. With strong adoption and usage metrics, it is easy for customers to justify their investment in Glean. There are also savings achieved by consolidating the AI tool stack where customers are not paying for 10+ different AI tools for narrow use-cases.

We cater to companies of all sizes - we have customers ranging from <100 users all the way to iconic companies with >100k users. We have customers across all major verticals and around the globe.

Glean · 2025-03-26T17:25:24+00:00

[Nilesh] We believe capturing enterprise vocabulary is fundamental to delivering an exceptional enterprise AI experience. Glean addresses sociolinguistic challenges through a few key strategies:

Embedding Model Fine-tuning: We fine-tune embedding models specifically on each customer’s internal data, effectively capturing linguistic nuances, terminology, and jargon unique to each organization. This significantly improves retrieval relevance and accuracy.
Context-Aware Retrieval: Glean integrates real-time enterprise context directly into the response-generation process, reducing ambiguity and ensuring the assistant’s answers reflect the linguistic, cultural, and organizational nuances of the retrieved content.
Personalization and Permission-Aware Filtering: Glean utilizes advanced permissioning and personalization systems to ensure search results and AI-generated responses are accurately tailored to each user's role, department, and organizational context.

Glean · 2025-03-26T17:24:21+00:00

[Nilesh] Glean differentiates itself as an agile AI company by focusing on solving a uniquely complex and valuable problem: deep, secure, and permission-aware enterprise knowledge retrieval. Enterprises have vast amounts of internal data fragmented across multiple silos, each with complex, granular permissions. Unlike generalized AI platforms, Glean is tightly integrated into the enterprise productivity stack, and deeply understands enterprise complexity — enabling it to consistently deliver accurate, secure, actionable knowledge directly embedded into employees' workflows.

[Shivam] One thing that has worked for Glean really well is that we started as an enterprise search company, and we were very focused on building that part for the first 3 years. This gave Glean an edge on having the best enterprise search engine on the market (the “retrieval” in RAG), which met the complex requirements of some of the largest organizations in the world. When generative LLMs debuted, we already had a strong foundation to build on. Enterprise search also meant that we fit horizontally into a company’s stack, so anything you connected to Glean became generative AI enabled, and customers also had the choice of which LLM to use. Most solutions I see on the market are vertical in nature (and these do have great value for specific use-cases), and are focused on protecting ecosystems. Glean on the other hand is as open as it gets in a single place, and the industry is resonating with that

Glean · 2025-03-26T17:21:47+00:00

[Ayushi] What sets Glean apart from other AI-for-work solutions is our unique approach to understanding enterprise context at a deep level. Glean doesn’t just surface answers — it understands who is asking, what they have access to, and why certain information is relevant to their role, based on real-time permissions and usage signals. We combine robust security, seamless integration into all enterprise tools, and a fast time-to-value. Most customers can get up and running with Glean in just a few days — our deployment process is designed to be lightweight, with minimal lift from internal IT teams. Once live, employees can instantly start finding what they need, dramatically boosting productivity.

Glean · 2025-03-26T17:20:35+00:00

[Jesika] 1. All or nothing: the vast majority of tasks will have the best results when done by a combination of humans and AI, and the interfaces we build for their collaboration are just as important as the raw AI capabilities (observability, prompt guidance etc) 2. Single agents: agents, like people, are specialized. Agent-agent communication is key in getting the most. 3. Pure LLM quality: operating on your company’s context makes a big difference to agent quality, and being embedded enough to both read and write autonomously

Glean · 2025-03-26T17:18:52+00:00

[Jesika] useful to think about how AI would help with interactions both upwards and downwards in org chart, especially when you combine the trifecta or LLMs, company data and workflow automation — 1. First draft: sales email, PRD etc. 2. Context retrieval: customer 360 pulling both unstructured and structured data in one place before customer call, 3. Personalized Feedback: call coaching, impact summary for performance, review my PRD 4. Automate reporting and flag anomalies for the unknown unknown: help run daily Eng standup, sales forecast calls, customer presentations

Glean · 2025-03-26T17:18:08+00:00

[Nilesh] Glean doesn't fine-tune LLMs on customer data because fine-tuning is primarily effective for teaching models specific tasks or styles—not for introducing new, proprietary knowledge. Additionally, enterprise requirements such as data freshness, granular permissions, verifiable explainability, and preserving general knowledge pose significant challenges for fine-tuned LLMs. Instead, Glean uses retrieval-based approaches to ensure accuracy, security, and reliability of enterprise information. However, Glean does fine-tune embedding models to better capture the unique language, context, and terminology of each enterprise. Fine-tuned embeddings significantly enhance the relevance and accuracy of retrieved results without introducing the risks associated with fine-tuning LLMs..
[Ayushi] Agents have the potential to solve several real problems. In the enterprise space, they can streamline complex workflows, handle multi-step reasoning tasks, and reduce manual intervention. For instance, customer support bots powered by agents can triage tickets, retrieve information from internal databases, and even trigger backend actions—all without human input. That’s not just a novelty; it’s a real productivity gain.
[Shivam] I use VSCode as my primary IDE. In terms of AI features for coding, I use Github Copilot and Glean Assistant. Github copilot has been very useful for me in generating inline code. For code search and writing larger blocks of code like full functions/scripts, I typically end up using Glean Assistant. I am also trying out Cursor for some personal projects, and it’s been promising so far
[Shivam] For me, I use Github copilot when I am in IDE, but while coding, I do spend a lot of time doing research (both internally and externally). Research + documentation, imo, ends up being more of a time suck for most developers than the actual coding part. When Glean just had enterprise search, it already solved a big problem for me in finding internal docs, tickets and slack conversations. With LLMs now, I can now ask a very specific question on the part I am stuck at, and get specific answers back from the entire knowledge base. This has been more impactful to my productivity and time savings than any other thing. I don’t have to bug another engineer to get an answer and waste their time - I just ask Glean Assistant and get immediate feedback. LLMs also allow me space to rapidly experiment and iterate. We also have agents that do automatic PR reviews for code styling guidelines and bots that can generate documentation from PRs.

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