everlign

Enterprise AI, Engineered for Security and Scale.

[everlign @everlign]

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

[everlign @everlign]

Our team submitted the Finals entry for the @DARPA Triage Challenge Data Competition.

The challenge addresses this problem: in a mass-casualty event, which casualty needs a lifesaving intervention next, and how should limited resources change that call?

Building for it meant solving three problems at once:
• First Look: Predict the next four hours from a single snapshot of a patient.
• Continuous Alert: Update predictions every 30 seconds as vitals stream in.
• Resource Allocation: Decide who gets limited resources across many patients.

We built an ensemble across model families, suited to messy, sparse trauma data. And it had to run inference fully air-gapped, in locked-down containers with no network access. Defense lives with that constraint, and so do healthcare customers, where data privacy is critical.

[Sushil Muzumdar @muzumdar_s]

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

[everlign @everlign]

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

[Sushil Muzumdar @muzumdar_s]

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.

[everlign @everlign]

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

[everlign @everlign]

Meet ClassifyIQ - AI-powered Secure Document Intelligence.

From provider contracts to compliance workflows, it automates classification & flags non-standard clauses with precision.
▶️ Watch the demo video: https://t.co/mvrLKRzfui

[everlign @everlign]

Secure. Compliant. Mission-ready.
Everlign’s AI platform is built to meet the unique needs of Defense. Learn more: 🌐 https://t.co/oAT93Hyf7F

[everlign @everlign]

How we optimize for recall (not just precision) when building production-grade GenAI RAG applications.

Read more 👉 https://t.co/JSP8uUn9sX

[everlign @everlign]

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

[everlign @everlign]

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.

[everlign @everlign]

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

[everlign @everlign]

At Everlign, we don’t skip leg day.
We download all the model weights. Locally.
Because security is how we roll. 🏋️‍♂️💪🔐 #EnterpriseAI #NoWeightsLeftBehind

[everlign @everlign]

When you realize you don’t need a wrapper—you need a real platform 🤓

[everlign @everlign]

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. 🌍🚀😜

[everlign @everlign]

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.

[everlign @everlign]

🚀 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