Contextual AI
@ContextualAI
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The unified context layer for Enterprise AI. Bridging the gap between LLMs and enterprise data.
Bay Area
Joined March 2023
Contextual AI now offers custom role-based access control—so you no longer need to trade security for access when building and managing agents. Admins can define exactly who can access what resources across the platform. Here's how it works 🧵
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This year's @NeurIPSConf marks 5 years since the original RAG paper was presented at NeurIPS 2020. Our CEO and co-founder, @douwekiela, was a co-author on that work. While RAG was an important step forward, retrieval is just one piece of a much larger puzzle. At Contextual AI,
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Agents can do GraphRAG with the right set of tools.
An agentic alternative to GraphRAG. We built a Metadata Search Tool to solve reference traversal without the rigid complexity of static graphs. The result? Agents resolve complex queries in fewer steps with higher accuracy. 🧵 1/4
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An agentic alternative to GraphRAG. We built a Metadata Search Tool to solve reference traversal without the rigid complexity of static graphs. The result? Agents resolve complex queries in fewer steps with higher accuracy. 🧵 1/4
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An ideal evaluation system should surface failure modes and usage patterns, provide actionable insights, adapt to your product -- and continuously improve over time. Nothing out there met our eval needs. So we built AgentLens: a multi-agent system which surfaces deeper
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Custom RBAC is available now to all enterprise customers. Read the full announcement and get started:
contextual.ai
Contextual AI now offers custom RBAC. Gain granular security control and operational efficiency with flexible, reusable roles for users and groups.
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📊 Centralized governance Track all roles, permissions, and user access from a single dashboard. Simplified audits and compliance across your entire organization. 🧵
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👥 Manage at scale Assign roles to entire groups instead of configuring individual users one by one. Users can hold multiple roles, and permissions stack automatically. 🧵
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🔐 Principle of least privilege Create unlimited custom roles with precise permissions across agents, datastores, and admin tools. Give each user access to exactly what they need—nothing more, nothing less. 🧵
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LLM-as-a-Judge doesn't scale. After 100+ test cases, you're stuck with: → Binary scoring that treats 95% correct the same as 5%. → Judge prompts that drift every update. → Zero insight into WHY something failed. So we built something better. 🧵
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I just came back from an amazing trip to Europe, where I had the privilege of attending and speaking at @WebSummit in Lisbon. The event was great as always, and this year a big focus for speakers and attendees was about making AI real for enterprises. Here are a few of my
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Stop model shopping. Start learning from users. Feedback & Annotation now in @ContextualAI • Real-time capture • Intelligent categorization • Dashboards for faster fixes Blog post for details: https://t.co/lHueBcRZo2 Try it live: https://t.co/Dc73itsVuo
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Opening private preview today. If you're extracting data from complex documents and need both speed AND verifiability, request access: https://t.co/LxBFSciVVQ Built for teams where accuracy isn't optional.
go.contextual.ai
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Built to handle what other tools can't: 📄 400+ page documents 📊 Nested tables across multiple pages 📈 Complex charts and financial statements 🔗 Multi-level JSON schemas 🤖 Simple API 🧵
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Contextual AI's Extraction Agent treats verification as a first-class feature, not an afterthought. Every extracted field includes: - Source page citation - Confidence score - Full reasoning trace Your team reviews exceptions, not everything. 🧵
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The real problem with PDF extraction isn't speed—it's trust. Most tools give you structured output with no way to verify it. No citations. No confidence scores. No audit trail. So you extract 1000 fields and have to manually check all 1000. Where's the productivity gain? 🧵
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Ever use AI to extract structured data from a 400-page financial document, only to spend more time verifying the output than you saved? Template-based tools break on complex docs. LLMs hallucinate. You're stuck manually checking everything anyway. We built something better. 🧵
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AI that understands your business starts with context. Meet us at #WebSummit Lisbon, Nov 11–12, to see how enterprises are deploying trusted AI agents. #ContextualAI #AIagents #EnterpriseAI
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What happens when you build AI for one of the most demanding users in enterprise software? Claimwise is a legal tech company that serves patent attorneys—professionals with advanced degrees in science & law—who need to verify every single AI-generated result before using it in
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This is great validation of CodeGen-specific context engineering insights we shared in August! Like Cursor's combination of semantic + grep search, we found hybrid search (semantic + lexical) essential for large codebases. But for enterprise technical documentation, code alone
Semantic search improves our agent's accuracy across all frontier models, especially in large codebases where grep alone falls short. Learn more about our results and how we trained an embedding model for retrieving code.
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