Enterprise AI Search & Knowledge Platforms 2026 — Glean, Guru, Coveo, Atlassian Rovo, Notion Atlas, Microsoft 365 Copilot, Slack AI Deep Dive

Prologue — "Where is that document?" is still unsolved in 2026

One of the most common things you hear at the office every day.

PM: "Where was our Q1 OKR retro?" Engineer: "Somewhere in Confluence... or Notion?" PM: "I think someone shared it on Slack." Engineer: "Searched it, can't find it."

In 2026, AI models do PhD-level reasoning and write code, but finding the document I looked at yesterday in my own company is still unsolved. Corporate knowledge is scattered across Slack, Confluence, Drive, Notion, Jira, SharePoint, Salesforce, Zendesk, and GitHub, and each tool's search only looks inside itself. IDC estimates the average employee spends 9.3 hours per week searching — a quarter of the workweek.

This article maps the enterprise AI search and knowledge platform landscape as of 2026. How RAG over corporate data is changing employee productivity, where the major players (Glean, Microsoft 365 Copilot, Atlassian Rovo, Notion Atlas, Slack AI) sit, how far open source RAG has come, how to build a RAG architecture stack, and how to decide whether your team should build or buy.

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1. Why enterprise AI search is the hottest market of 2026

The reason is simple. Internal knowledge silos are the most expensive problem, and RAG is the first thing that can actually solve them.

Three trends converged.

First, knowledge fragmentation accelerated. An average mid-size company uses 200 to 500 SaaS tools. Okta's Businesses at Work 2025 report shows the average employee uses 22.6 apps. Each tool has its own search and they do not integrate.

Second, the cost curve of embeddings and LLMs. OpenAI text-embedding-3-small costs $0.02 per 1M tokens. Cohere Embed v3 is similar. Llama 4 Maverick is self-hostable. In 2022 building a RAG index was prohibitive. In 2026 embedding a million documents costs tens of dollars.

Third, the maturation of permission preservation. Enterprises demand "AI must not mix documents I cannot see into the answer." Glean, Microsoft, and Atlassian standardized the pattern of preserving ACLs at index time and checking at query time, in 2024 and 2025.

These three together caused the market to explode. Glean raised $260M at a$4.6B valuation in 2024, and Microsoft announced that 70%+ of the Fortune 500 had adopted Copilot by late 2024.

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2. Glean — leader, becoming the enterprise standard

Glean was founded in 2019 by ex-Google search engineers. It started purely as enterprise search, and when the LLM era came in 2023, it naturally added a RAG layer.

Three core value props.

1. 100+ connectors — Google Drive, OneDrive, SharePoint, Box, Dropbox, Slack, Teams, Jira, Confluence, Linear, GitHub, Salesforce, HubSpot, Zendesk, Notion, Asana, Monday, Gmail, Outlook. Covers nearly every enterprise SaaS.
2. ACL preservation — Pulls each source's permissions at index time and checks them at query time. The standard implementation of "documents I cannot see do not enter the answer."
3. Glean Apps — A platform to put workflow apps on top of search. Sales asks "last contact with this account?", engineering asks "who owns this service?" — these become bundled workflows.

Pricing. No public price list. Usually $40 to$80 per seat per month. 500+ seats means negotiation. SOC 2 Type II, ISO 27001, GDPR compliant.

Weaknesses. Expensive. Not a fit for small companies. Connectors for Korean and Japanese enterprise tools are weak (Channel Talk, Cybozu Garoon, etc.).

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3. Microsoft 365 Copilot — the biggest distribution channel

Microsoft 365 Copilot launched in 2023 and went GA in 2024. $30 per seat per month, annual commitment. Add-on on top of an M365 license.

Core architecture — Microsoft Graph rules everything. All Outlook, Teams, SharePoint, OneDrive, Loop, and Planner data already lives in Graph. Copilot is RAG layered on top of Graph.

Copilot Studio + Copilot Connectors. Connectors that bring external data (Salesforce, ServiceNow, Workday, Jira) into Graph. 100+ official connectors were launched by late 2024. Microsoft's strategy is effectively to be "the OS of enterprise search."

Strengths. The most natural fit for companies already on M365. SSO, DLP, Purview, and Sensitivity Labels are all integrated. The EU Data Boundary is guaranteed (data does not leave the EU).

Weaknesses. It "does what is in Graph well." External data has to be brought in via Copilot Connectors, and the catalog is smaller than Glean's. And hallucination cases get talked about the most — partly because Microsoft has the biggest deployment, but Glean's reliability gets higher ratings.

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4. Atlassian Rovo — uniting Jira, Confluence, and Bitbucket

Atlassian Rovo was announced in 2024 and went GA at the end of 2024. Atlassian's enterprise search plus AI agent layer.

Core value. Search Jira, Confluence, Bitbucket, and Trello data from one place. And expand with 30+ external connectors (Google Drive, Microsoft 365, GitHub, Slack, Figma, Notion).

Rovo Agents. Not just search but "agents" — code review agents, release note writing agents, meeting summary agents. A platform where Atlassian builds its own agents and users build custom ones.

Pricing. Moving toward being bundled with Atlassian Cloud Premium and Enterprise. Standalone SKU is around $20 per seat per month (estimated).

Strengths. Natural for companies on Atlassian (many engineering orgs). Data governance follows Atlassian Cloud standards.

Weaknesses. Depth on external SaaS that is not Atlassian data is shallower than Glean's.

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5. Notion AI + Notion Atlas — the search evolution of collaborative docs

Notion AI launched in 2023, initially as a writing assistant. Starting in 2024 it expanded under the name Notion Atlas to search other sources (Slack, Google Drive, Linear, Jira, GitHub, Figma).

Meaning of Atlas. "Documents inside Notion plus external data in one search box." A mini-version of Glean's positioning. Natural for places where Notion is already the wiki, planning, and OKR hub.

Pricing. Business is $15 per seat per month, Enterprise is negotiated. Notion AI is roughly$10 per seat on top.

Strengths. Most natural for startups and mid-sized companies using Notion as their wiki. The UX is clean.

Weaknesses. Large enterprise SaaS connectors (Salesforce, Workday, ServiceNow) are shallower than Glean's.

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6. Slack AI — the assumption that channels are the knowledge base

Slack AI went GA in 2024. $10 per seat per month add-on, on top of Slack Business or Enterprise plans.

Four features.

1. Channel summary — summarize a week of missed messages.
2. Thread summary — boil a long thread down to a paragraph.
3. AI search — natural language queries like "what was decided about project A last week?"
4. Meeting notes — Huddle transcription and summary.

Core assumption. "The real-time knowledge of the company lives in Slack." Decisions, discussions, and handoffs happen in channels.

Strengths. Most natural for Slack users. You just flip it on, no separate tool to deploy. Data does not leave Slack.

Weaknesses. Trapped inside Slack. Cannot see documents in other tools. Many companies run Slack AI plus Glean — Slack AI for channel summaries, Glean for company-wide search.

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7. Google Workspace + Gemini for Workspace — Google's answer

Gemini for Workspace went GA in 2024. $20 per seat per month (Business Standard add-on; Enterprise costs more).

Features. Gemini inside Gmail, Docs, Slides, and Sheets. Plus Workspace-wide search — Drive, Gmail, and Calendar from one place.

Connectors. Weaker external connectors than Microsoft. Salesforce, Jira, and HubSpot, roughly. Google is investing here but the depth is behind Microsoft and Glean as of May 2026.

Strengths. Natural for Workspace shops. Cheaper than M365 Copilot. Leverages Gemini 1.5 Pro and 2.5 Pro's large context window (1M to 2M tokens) for long-document tasks.

Weaknesses. Depth of external connectors. And not an option for non-Workspace companies.

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8. Guru — wiki plus AI suggestions

Guru is a company built around wiki. It strengthened AI search and auto-suggestion in 2024 and 2025.

Core value. Build short, verified answers as "Knowledge Cards", and have AI suggest cards based on context inside Slack, the browser, or your CRM.

When it fits. Teams like sales and customer support where "short, verified answers" matter. Lighter than Glean.

Pricing. Builder is $15 per seat per month, Enterprise is negotiated.

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9. Coveo — straddling enterprise search and commerce search

Coveo is a Canadian company founded in 1996. It does both enterprise search (for employees) and commerce search (for customers) at the same time.

Commerce side. Search and recommendations on top of Shopify Plus, Salesforce Commerce Cloud, and Adobe Commerce. An Algolia competitor.

Enterprise side. Customer support search inside Salesforce Service Cloud, employee intranet search.

AI. Coveo Relevance Cloud added RAG and generative answers (Coveo Relevance Generative Answering, RGA). GA since 2024.

Pricing. No public price list, typically enterprise-negotiated (tens to hundreds of thousands per year).

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10. Lucidworks Fusion and Elastic — the AI evolution of classic search

Lucidworks Fusion. An Apache Solr based enterprise search platform. Strengthened the ML and RAG layer in 2024 and 2025. Strong on large-scale e-commerce and customer support. "A more buildable option than Glean."

Elastic. The company behind Elasticsearch. In 2023 it sunset its separate "Enterprise Search" product, and instead reorganized toward building vector search and RAG capabilities into the Elastic Stack itself. Supports both ELSER (Elastic Learned Sparse Encoder) and dense vectors. A great base for teams building RAG directly.

These two are a different category from Glean and Microsoft — they are build platforms, not finished SaaS search apps.

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11. New entrants — Hebbia, Perplexity Enterprise, You.com, Claude Enterprise, ChatGPT Enterprise

Hebbia. Specialized for research workflows — deep document analysis for investment banks, law firms, and consulting. Strong on PDFs, earnings call transcripts, deal docs. Raised $130M in 2024.

Perplexity Enterprise. The enterprise version of regular Perplexity. Combines internal data connections with web search. Around $40 per seat per month.

You.com Pro / Enterprise. Multi-model (GPT, Claude, Gemini) integration plus enterprise connectors.

Anthropic Claude Enterprise. Claude's enterprise plan. Large context window (1M tokens), Projects (documents as context), Tool use API. The pattern is "feed internal docs into context" rather than searching internal data directly.

ChatGPT Enterprise / Team. OpenAI's enterprise offering. ChatGPT Team ($25 per seat per month, 2+ seats), Enterprise (negotiated, often$60+ per seat per month). Custom GPTs, internal connectors (Drive, SharePoint, Box, etc.).

These complement rather than completely replace Glean and Microsoft — generic LLM interface plus light internal connectors.

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12. Open source RAG — Onyx, Quivr, Khoj, LibreChat, AnythingLLM

If enterprise is too expensive, there is a self-hosted option. Meaningful OSS RAG platforms as of 2026.

Onyx (formerly Danswer). Y Combinator 2023, Apache 2.0. Connectors for Slack, Confluence, Drive, Notion, Jira, GitHub. ACL preservation (partial). Self-host or Onyx Cloud. Becoming the standard for OSS enterprise search. Most often cited as the OSS alternative to Glean.

Quivr. OSS in the "second brain" mold. Personal and small-team RAG. Apache 2.0.

Khoj. Personal RAG assistant. Indexes notes, email, and calendar. Obsidian and Emacs integration. Apache 2.0.

Continue. Code-specialized OSS — RAG inside the IDE. JetBrains and VS Code plugins.

LibreChat. Multi-provider chat UI (GPT, Claude, Gemini, Ollama). Includes RAG plugin. MIT.

AnythingLLM. Local RAG desktop app. RAG against your own docs on a laptop. MIT.

Ragna, R2R, Verba — more library and SDK in nature. Tools teams building RAG use as a base.

When OSS fits. (a) When data sovereignty is absolute (legal, healthcare, defense), (b) when Glean cost does not fit, (c) when an engineering team can operate it.

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13. RAG architecture stack — 6 layers

The internals of enterprise AI search are ultimately a RAG pipeline. It helps to look at it as 6 layers.

Choices at each layer are independent. So RAG is ultimately the product of 6 decisions.

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14. Ingest — the most underrated layer

"How you parse a PDF" determines 50% of RAG quality.

Unstructured.io. OSS plus commercial. Turns PDF, docx, HTML, email, and images into structured element lists. partition_pdf's hi_res mode is layout-aware. Effectively the standard in 2024 and 2025.

LlamaParse. LlamaIndex's managed service. Strong on complex tables and multi-column PDFs. Per-page pricing ($0.003 to$0.03).

Docling. IBM open-sourced this in 2024. Converts PDF, docx, xlsx, PPTX, HTML, and images to JSON or Markdown. Layout-aware. Apache 2.0. Good for table-heavy documents.

Apache Tika. A classic. Handles every format but is not layout-aware. OK for plain text extraction.

Marker. OCR plus LLM post-processing. Strong on academic papers and complex PDFs.

Selection guide. Lots of tables and charts: Docling, LlamaParse. Plain text: Unstructured, Tika. Scanned: Marker.

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15. Embed — embedding model comparison matrix

Selection guide. Mixed Korean and Japanese: Voyage or Cohere multilingual. English only: OpenAI text-embedding-3 or Cohere v3. Self-hostable required: BGE-M3 or Nomic.

The MTEB leaderboard (Massive Text Embedding Benchmark) is the standard reference. As of May 2026 you often see NVIDIA NV-Embed-v2, BGE-multilingual-gemma2, voyage-3-large, and OpenAI text-embedding-3-large near the top (varies by task and language).

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16. Rerank — the final 30% of RAG quality

Vector search pulls a top-k (say 50). Rerank picks the actually relevant 5 from that batch.

Cohere Rerank 3. The most commonly used managed option. English and multilingual. $2 per 1K queries.

Voyage rerank-2. Voyage's reranker. Pricing similar to Cohere.

ColBERT / ColBERT v2. OSS rerank model. Late-interaction style. Self-hostable. Easy to use via RAGatouille.

Why it matters. Vector search looks at "semantic similarity", but what really matters is "the chunk that contains the answer for this query." Rerank closes that gap. Empirically it lifts nDCG@10 by 5 to 15%.

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17. Eval — RAG evaluation tools

If you do not measure how well RAG runs, you cannot improve it. Four eval tools.

Ragas. OSS RAG evaluation framework. Metrics like faithfulness, answer relevancy, context precision, context recall. The most standard option.

TruLens. OSS plus hosted. Uses "feedback functions" to evaluate RAG and LLM apps.

DeepEval. OSS, pytest-style. Supports 14+ metrics.

Arize Phoenix. OSS observability plus eval. Strong on RAG trace visualization.

Four evaluation metrics. (1) Faithfulness: is the answer grounded in the context, (2) Answer Relevancy: does the answer fit the question, (3) Context Precision: what fraction of retrieved context is actually relevant, (4) Context Recall: does the context contain everything needed for the answer. These four are typically the first eval set.

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18. The connector universe — which SaaS systems do you link

The value of enterprise search is ultimately how many systems you tie together. Standard connector groups in 2026.

Cloud Drive. Google Drive, OneDrive, SharePoint, Box, Dropbox.

Messaging. Slack, Microsoft Teams, Discord.

Dev and issues. Jira, Confluence, Linear, GitHub, GitLab, Bitbucket, Asana, Monday.

CRM and support. Salesforce, HubSpot, Zendesk, Intercom, Freshdesk.

Docs and notes. Notion, Coda, Quip, Evernote.

Email. Gmail, Outlook.

Glean has 100+ connectors, Onyx has 40+, Notion Atlas has 20+, M365 Copilot Connectors has 100+ (partners included). Slack AI only sees its own data.

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19. Permission preservation — the enterprise's absolute requirement

Core principle. "Only documents the user can see in the source system may enter the answer." If this breaks, compliance breaks.

Two patterns.

1. Store ACLs at index time. At query time, filter on permissions matching the user_id. Glean and Onyx work this way.
2. Check permissions on the source system at query time. More accurate but slower. Robust to live permission changes. The Microsoft Copilot pattern leveraging the Graph permission model.

Challenges. Slack channel permissions, SharePoint site permissions, Jira project permissions — each system's permission model is different. Half the reason Glean took years to build 100+ connectors is ACL modeling.

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20. Data privacy — "your data is not used to train the model"

The second absolute requirement for enterprises is "is our data used to train external models?"

OpenAI Enterprise. API, Enterprise, and Team data are not used for training (official clause). Anthropic Claude Enterprise. API and Enterprise data are not used for training. Google Gemini for Workspace. Workspace data is not used for training. Microsoft Copilot. Tenant data is not used for training.

And data residency. EU, Japan, and Korea-based companies often have to keep data in-country or in-region. Microsoft EU Data Boundary, Anthropic's region options, and Google Cloud's regions are the answer.

When self-host is the answer. Some government, healthcare, and finance contexts disallow external APIs outright. The combination of OSS like Onyx plus self-hosted LLM (Llama 4, Qwen 3) is the only path.

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21. Korean and Japanese enterprise tools

Korea. Channel Talk (customer support and CRM), Jandi (Slack alternative), Naver Cloud Works (enterprise collaboration), Kakao Work AI, Goorm (collaborative coding). Few global tools have connectors for these. The reality is to add connectors to Onyx or build your own RAG.

Japan. Cybozu Garoon / Office (enterprise groupware), Kintone (low-code, by Cybozu), Sansan (business cards and CRM) plus Sansan ContractOne (contract search), Talknote (enterprise messaging). Japanese companies have higher dependence on local tools, so global SaaS search coverage is limited.

Realistic options. (a) Deploy Glean or Microsoft for global tools but keep local tools separate, (b) self-host Onyx and build local-tool connectors yourself, (c) wait for local SaaS vendors to embed AI search (Channel Talk and Jandi are partially in progress).

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22. Cost comparison — how much per seat

Rough per-seat per-month costs as of May 2026.

Total cost. For a 1,000-person company, Glean is $500K to$1M annually. M365 Copilot is $360K annually ($30 per seat times 12 months times 1,000). Self-hosted Onyx is $50K of infra plus LLM and embed usage (tens to hundreds of thousands, depending on usage).

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23. Build vs buy — decision tree

When to buy, when to build.

Buy (Glean, M365, Atlas) fits when.

• 100+ seats and IT does not want to run operations.
• You need 30+ connectors.
• You need security certifications (SOC 2, ISO, HIPAA).
• "ROI within 9 months" is the priority.

Build (Onyx self-hosted, or your own RAG) fits when.

• Data sovereignty is absolute (some defense, finance, healthcare).
• Korean or Japanese local-tool weight is high and global SaaS connectors do not fit.
• You need domain-specialized embeddings and reranking (legal, life sciences).
• The engineering team can carry RAG operations (2 to 5 full-time).

Hybrid is common. Glean for company-wide search, plus Onyx for security-sensitive domains, plus custom RAG embedded inside the product. This is the realistic pattern for large companies in 2026.

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24. First-deployment checklist — 12 items

1. Identify your top 5 data sources. Slack? Confluence? Drive? Notion? Jira? Where does your company's knowledge live?
2. Estimate user count. Units like pilot 100, company-wide 1,000.
3. Review the permission model. What do each source's ACLs look like, and can they be preserved?
4. Data residency requirements. Korea, EU, Japan obligations?
5. Current LLM contracts. Already in contract with OpenAI, Anthropic, or Google?
6. Budget range. $10 to$80 per seat times 100 to 10K seats — annual range of tens of thousands to millions of dollars.
7. Pilot PoC with three vendors. Throw the same 100 queries at three of Glean, Microsoft, Atlas, or Onyx and compare answers.
8. Eval metric. Measure baseline "time to find an answer" for employees.
9. Security review. SOC 2 Type II, ISO 27001, DPA, SCC.
10. Operations owner. Who handles index health, connector outages, and permission changes?
11. Rollout plan. 100-person pilot then by department then company-wide. 3 to 6 months.
12. Success metric. Search-time reduction (target 30%+), answer satisfaction, adoption (MAU per seat).

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25. 10 anti-patterns

1. "Company-wide bang launch." Permission and document-quality problems all surface at once. Run a 100-person pilot first.
2. "Evaluate only by search results." You have to look at user behavior (click-through, re-search).
3. "Turn on every connector from the start." Noisy sources (personal notes, DMs) come later. Start with the core wiki.
4. "ACLs as a later concern." One leak kills trust. Week-1 priority.
5. "One embedding model for every language." Mixed Korean and Japanese demands a multilingual embedding model.
6. "Skip rerank." Without narrowing top-50 down to top-5, answer quality is low.
7. "No hallucination eval." Run the faithfulness metric weekly.
8. "Index once and forget." Documents keep changing. Build change detection and re-indexing pipelines.
9. "No feedback loop." Users need somewhere to click "not helpful."
10. "Chatbot is not search." Giving only a chatbot to users who do not know what is the right answer wastes money. Citation links are 50% of the answer.

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Epilogue — search is once again a differentiation surface

In the late 2010s, enterprise search was considered "a closed market." SharePoint Search and Confluence Search were assumed to be good enough. In practice everyone was unhappy, but there were no alternatives.

In 2026, that assumption broke. LLMs and embeddings combined as RAG, and for the first time it became possible to "ask anything across all of my company's documents in natural language." And search is once again a differentiation surface. That is why Glean gets a $4.6B valuation and why Microsoft is betting everything on Copilot.

The key insight. Search is the interface closest to the end user. Users do every job on top of it. So a company that does search well has effectively built "the OS of employee work." That is what Glean, Microsoft, and Atlassian are after.

What your team should do. (1) Start small with a 100-person pilot. (2) Get foundations like ACLs, eval, and reranking in early. (3) Build vs buy is not a single decision — it differs by domain. (4) Measure user satisfaction weekly.

This market is forecast to grow 30%+ annually through 2030. A company that does search well does work well. Already in 2026, that is true.

12-item checklist

• Identify top 5 data sources
• Estimate user count
• Review permission model
• Data residency requirements
• Confirm current LLM contracts
• Budget range
• Pilot PoC with three vendors
• Eval-metric baseline
• Security review (SOC 2, ISO, DPA)
• Operations owner
• Rollout plan over 3 to 6 months
• Success metric (search time, satisfaction, adoption)

Next post preview

Next time I plan to cover "RAG evaluation pipelines — measuring RAG quality daily with Ragas, TruLens, and Phoenix."

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References

• Glean — https://www.glean.com
• Glean Series E funding 2024 — https://www.glean.com/blog/series-e
• Microsoft 365 Copilot — https://www.microsoft.com/en-us/microsoft-365/copilot
• Microsoft Copilot Studio — https://www.microsoft.com/en-us/microsoft-copilot/microsoft-copilot-studio
• Atlassian Rovo — https://www.atlassian.com/software/rovo
• Notion AI — https://www.notion.so/product/ai
• Slack AI — https://slack.com/features/ai
• Google Gemini for Workspace — https://workspace.google.com/solutions/ai/
• Guru — https://www.getguru.com
• Coveo — https://www.coveo.com
• Lucidworks Fusion — https://lucidworks.com
• Elastic vector search — https://www.elastic.co/elasticsearch/vector-search
• Hebbia — https://www.hebbia.com
• Perplexity Enterprise — https://www.perplexity.ai/enterprise
• You.com — https://you.com
• Anthropic Claude Enterprise — https://www.anthropic.com/enterprise
• ChatGPT Enterprise — https://openai.com/enterprise
• Onyx (Danswer) — https://github.com/onyx-dot-app/onyx
• Quivr — https://github.com/QuivrHQ/quivr
• Khoj — https://github.com/khoj-ai/khoj
• LibreChat — https://github.com/danny-avila/LibreChat
• AnythingLLM — https://github.com/Mintplex-Labs/anything-llm
• Unstructured.io — https://unstructured.io
• LlamaParse — https://www.llamaindex.ai/llamaparse
• Docling (IBM) — https://github.com/DS4SD/docling
• OpenAI text-embedding-3 — https://openai.com/index/new-embedding-models-and-api-updates/
• Cohere Embed v3 — https://cohere.com/embed
• Voyage AI — https://www.voyageai.com
• Jina Embeddings v3 — https://jina.ai/embeddings/
• BGE-M3 — https://huggingface.co/BAAI/bge-m3
• Pinecone — https://www.pinecone.io
• Weaviate — https://weaviate.io
• Qdrant — https://qdrant.tech
• pgvector — https://github.com/pgvector/pgvector
• Cohere Rerank — https://cohere.com/rerank
• ColBERT v2 — https://github.com/stanford-futuredata/ColBERT
• Ragas — https://github.com/explodinggradients/ragas
• TruLens — https://www.trulens.org
• DeepEval — https://github.com/confident-ai/deepeval
• Arize Phoenix — https://github.com/Arize-ai/phoenix
• MTEB Leaderboard — https://huggingface.co/spaces/mteb/leaderboard