Why are quality teams still manually digging through clinical faxes to find evidence? 📉
The data is there. The "intelligence" is missing.
Everlign moves HEDIS from a static dashboard to a System of Action. Our Autonomous Reasoning AI agents don’t just find gaps, they help close them.
Protect your Star trajectory and secure incentives before the year locks.
See the tech: 🔗 https://t.co/QMc28YAnaK
#VBC #ValueBasedCare #HEDIS #HealthTech
Fun experiment: deployed a production fix entirely from my phone.
Stack:
- Tailscale — tunnel to my Mac
- Termius — SSH client for iPhone
- Claude Code — AI coding agent in terminal
- GitHub + Vercel — push to deploy
We're heading to #HLTHUSA in Las Vegas! Excited to connect with healthcare leaders, and partners shaping the future of AI in healthcare.
If you’re attending, let’s connect and share experiences around deploying secure AI solutions that are making an impact.
See you there 👋
@HLTHEVENT
Couldn’t agree more. The AI race won’t necessarily be decided by model size; it’ll be by who builds useful, domain-specific applications that actually solve problems.
Scaling AI isn’t just a technical challenge, it’s an enterprise requirement. At Everlign, we’re supporting more users on the same infrastructure by optimizing search to run on CPU workloads and caching frequent queries, delivering faster results, lower costs, and greater scale without added compute spend.
#Scalability #EnterpriseAI
As context windows grow, there has been some discussion if RAG is even needed. However, for now, RAG remains highly relevant for enterprise use cases where traceability and governance are critical.
Our agentic framework rapidly generates the structured data GenAI needs to succeed.
Read more 👉 https://t.co/XFsdnS5XRo
#GenerativeAI #EnterpriseAI #RAG
Enterprise AI isn’t just about deploying the latest models; it’s about rethinking solutions from first principles. At Everlign, we build secure, scalable AI solutions by understanding the problem deeply and designing from the ground up.
LLaMA 4 Scout dropped with a 10M token context window. That’s entire databases of knowledge in a single prompt. This is orders of magnitude larger than previous models.
The model can now directly process massive documents without needing to retrieve or chunk information the way traditional RAG systems do.
RAG as we know it is evolving with less chunking, more deep reasoning. Think full context and fewer hallucinations. @AIatMeta
At Everlign, we don’t skip leg day.
We download all the model weights. Locally.
Because security is how we roll. 🏋️♂️💪🔐 #EnterpriseAI#NoWeightsLeftBehind
We got real-time multilingual support on our GenAI platform before #GTA6.
At this rate, we’ll have intergalactic translation ready before @RockstarGames drops the next trailer. 🌍🚀😜
We’re building containerized enterprise AI applications with native Kubernetes orchestration—fully self-managed and cloud-agnostic.
No managed abstractions. No lock-in.
Run it on any cloud, on-prem, or keep it air-gapped. Your infrastructure, your rules.
🚀 Update: In our latest blog, we discuss how Everlign’s ensemble RAG approach improves AI accuracy by combining multiple retrieval models. Each model is weighted based on domain expertise, ensuring unreliable extrapolations don’t skew results. Read more: https://t.co/YWWIBr98co
🚀 Some exciting news from Everlign! Our generative AI apps now support real-time multilingual conversations—detecting language on-the-fly and letting users switch languages seamlessly within the same conversation. Even if the source data is in English, responses come in your language. Breaking barriers, one convo at a time! 🌍 #MultilingualAI