Contextual AI

258 posts

Contextual AI banner
Contextual AI

Contextual AI

@ContextualAI

Expert AI for advanced industries

Bay Area Katılım Mart 2023
22 Takip Edilen5.2K Takipçiler
Sabitlenmiş Tweet
Contextual AI
Contextual AI@ContextualAI·
Announcing Agent Composer: AI that works when it actually IS rocket science. Technical teams can now automate the routine (but complex) tasks that used to take hours every week—root cause analysis, production planning, test code generation— and reduce them down to minutes, so you can get your real work done. Here's how we solved the AI context problem for hard engineering tasks (like rocket science 🚀) 🧵
English
29
47
505
1.6M
Contextual AI retweetledi
Abdallah Bashir
Abdallah Bashir@abdallah197_·
Search agents, whether they're powering deep research, or multi-step QA over a private corpus, spend most of their time and compute in the research loop: query, search, reason, repeat. We wanted to make that loop faster and more accurate. So we optimized two things jointly: the retrieval stack itself, and the planner that decides when and how to search. A trained planner on our fastest retrieval config matches an untrained planner on the most expensive one, at half the latency. Every arrow in this plot points up and to the left. [1/n]
Abdallah Bashir tweet media
English
3
9
58
55.3K
Contextual AI
Contextual AI@ContextualAI·
Stop spending 8+ hours manually root-causing semiconductor device failures. Generic AI breaks here — logs are too large, error codes too device-specific. Our agentic workflow processes multiple logs simultaneously and delivers a complete root cause report in 20 min or less — no file size limits, any format, grounded in your device specs. 🚀 Try it → demo.contextual.ai
English
0
5
6
354
Teng Yan · Chain of Thought AI
Teng Yan · Chain of Thought AI@tengyanAI·
most developers are trying to give AI agents more freedom. giving an LLM a massive toolkit is usually a recipe for failure. @contextualAI just launched Agent Composer to prove that agents fail because they are overloaded, not because they are dumb. by strictly limiting context and tools, teams are cutting root cause analysis from 8 hours to 20 minutes. manual research that once took hours now finishes in seconds. i believe the obsession with autonomy is actually holding back production scale deployments. intelligence is useless when the context window is a mess of irrelevant data.
Teng Yan · Chain of Thought AI tweet media
Contextual AI@ContextualAI

Announcing Agent Composer: AI that works when it actually IS rocket science. Technical teams can now automate the routine (but complex) tasks that used to take hours every week—root cause analysis, production planning, test code generation— and reduce them down to minutes, so you can get your real work done. Here's how we solved the AI context problem for hard engineering tasks (like rocket science 🚀) 🧵

English
10
3
28
4.1K
Contextual AI
Contextual AI@ContextualAI·
Excited to share our latest breakthrough: ⚡BlitzRank! This innovative LLM reranker optimizes token usage with tournament graphs, delivering 25-40% efficiency gains while maintaining top-tier quality across benchmarks. Huge props to @sheshanshag and @Thienhn97 for this excellent work. Check it out: blitzrank.ai
Sheshansh Agrawal@sheshanshag

**New research: Introducing ⚡BlitzRank** Current LLM rerankers waste tokens on information they already have. If A > B and B > C, you already know A > C, existing methods don’t track this. BlitzRank fixes this. It uses tournament graphs to extract maximal information from each LLM call. 📊 Pareto-optimal across 14 benchmarks × 5 LLMs ⚡ 25–40% fewer tokens than comparable methods ⚡ 7× cheaper than pairwise at near-identical quality

English
1
2
10
1.2K
Contextual AI retweetledi
Sheshansh Agrawal
Sheshansh Agrawal@sheshanshag·
**New research: Introducing ⚡BlitzRank** Current LLM rerankers waste tokens on information they already have. If A > B and B > C, you already know A > C, existing methods don’t track this. BlitzRank fixes this. It uses tournament graphs to extract maximal information from each LLM call. 📊 Pareto-optimal across 14 benchmarks × 5 LLMs ⚡ 25–40% fewer tokens than comparable methods ⚡ 7× cheaper than pairwise at near-identical quality
Sheshansh Agrawal tweet media
English
4
21
72
17.5K
Contextual AI
Contextual AI@ContextualAI·
❌ Stop manually searching semiconductor datasheets, support logs, and device files to troubleshoot customer issues. Use context-aware AI that automates research in minutes, not hours—investigating vague questions, synthesizing answers, and verifying sources with bounding box attribution ⚡️ Try the Raspberry Pi docs agent or build your own in minutes: demo.contextual.ai/customer-engin…
English
1
3
3
886
Contextual AI retweetledi
anshuman
anshuman@athleticKoder·
Contextual AI just killed n8n.
anshuman tweet media
English
8
8
136
13.6K
Contextual AI
Contextual AI@ContextualAI·
Check out the link our bio to learn more about the launch 🚀
English
1
0
1
1.4K
Contextual AI
Contextual AI@ContextualAI·
Here are some examples of cool use cases we've built with our customers: - An advanced manufacturer reduced root-cause analysis from 8 hours to 20 minutes by automating sensor data parsing and log correlation. - A global strategy consulting firm reduced manual research from hours to seconds, giving consultants access to relevant case work, answers to complex questions, and prior examples. - A tech-enabled 3PL provider achieved 60x faster issue resolution by providing instant answers across their entire internal knowledge base. - A specialty chemicals manufacturer reduced product research from hours to minutes with agents that search patents and regulatory databases. - A test equipment maker generates test code in minutes instead of days by translating procedures into control logic.
English
1
0
3
1.6K
Contextual AI
Contextual AI@ContextualAI·
Announcing Agent Composer: AI that works when it actually IS rocket science. Technical teams can now automate the routine (but complex) tasks that used to take hours every week—root cause analysis, production planning, test code generation— and reduce them down to minutes, so you can get your real work done. Here's how we solved the AI context problem for hard engineering tasks (like rocket science 🚀) 🧵
English
29
47
505
1.6M
Contextual AI retweetledi
Nina Lopatina
Nina Lopatina@NinaLopatina·
Last month, I dove deep with @swyx on @latentspacepod about the state of context engineering, and scaling it as a full-stack discipline with benchmarks, tooling, and enterprise deployments. Hosted by @LaudeInstitute on the rooftop of the Hard Rock Cafe during @NeurIPSConf (my 5th!), and my first interview in sunglasses! Catch the full video from sunny San Diego here: youtube.com/watch?v=tSRqTe…. We unpacked the rapid evolution of context engineering, how agentic RAG became the baseline, why context rot is cited in every blog but industry benchmarks at real scale (100k+ documents, billions of tokens) are still rare, sub-agents with turn limits and other explicit constraints, instruction-following re-rankers for precision at scale, KV cache strategies for multi-turn agents, and why 2026 will shift to end-to-end system designs over component tweaks. For more details on the blogs, papers, and events that shaped Context Engineering in 2025 (as we referenced in our chat), join @ContextualAI's webinar next week on 1/13! Sign up here: linkedin.com/events/context…
YouTube video
YouTube
English
4
5
28
13.3K
Contextual AI retweetledi
Google Cloud Partners
Google Cloud Partners@gcloudpartners·
🛠️ @ContextualAI is proving what’s possible when you combine open models with highly scalable infrastructure. By fine-tuning Llama 3.1 70B on Google Cloud, they’ve built a "grounded" language model that reduces hallucinations and automates complex workflows. See how they did it → goo.gle/3Y0zZnN
Google Cloud Partners tweet media
English
0
3
12
1.4K
Contextual AI
Contextual AI@ContextualAI·
We are excited to share several new SOTA integrations with @trychroma: - Parser, to load your unstructured documents directly into Chroma - Reranker, to optimize your retrieved context within your context window - LMUnit, natural language unit tests to evaluate your Agent, LLM, or RAG system’s performance Contextual AI 🤝 Chroma: Working together to help you reduce context rot docs.trychroma.com/integrations/f…
Contextual AI tweet media
English
3
0
4
640
Contextual AI
Contextual AI@ContextualAI·
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, we're tackling the broader context problem. We're optimizing context engineering for dynamic agents with state-of-the-art accuracy, enterprise features, and production-ready scale. Looking forward to the next 5 years.
Contextual AI tweet media
English
1
1
3
633
Contextual AI
Contextual AI@ContextualAI·
@weaviate_io just released several cookbook recipes, including three new ones with Contextual AI: 1. Contextual AI Parse + Weaviate for RAG 2. Contextual AI’s Grounded Language Model (GLM) with Weaviate for generative search 3. Contextual AI's reranker V2 with Weaviate to improve search result quality Check them out here: 1. Parse github.com/weaviate/recip… 2. GLM github.com/weaviate/recip… 3. Reranker github.com/weaviate/recip…
Erika Shorten@eshorten300

December is the month for giving So here are 9 new Recipes demoing how to use @weaviate_io and @awscloud, @ContextualAI, @ChonkieAI, @p0, @Google, @modaicdev, @twelve_labs, and @llama_index 🧑‍🍳

English
0
3
9
781

Keşfet

@tengyanAI @0xtarzan1999 @sheshanshag @Thienhn97 @swyx @latentspacepod @LaudeInstitute @NeurIPSConf