Chetan ParikhCEO & Founder

It was by design. Engineering gave me a great base, but I wanted work that felt deeply meaningful. Education and healthcare stood out. In 2002 I entered medical transcription, saw the impact of getting the right information to the right person at the right time, and never looked back.

I wanted the deeper meaning inside notes. Around 2008-2009 we set out to understand records automatically and put synoptic, accurate information in front of providers. I did not find tech good enough, so we built our own NLP, formed a team, and launched EZDI.

AI looks at lots of data and predicts next steps. NLP teaches a system to understand human language and structure. LLMs add huge compute and context, so the model can generate the next word and connect many dots at once.

One small wrong assumption can multiply as the model keeps generating. It looks convincing but goes off-path. In healthcare this is unacceptable, so we push explainability and clear evidence.

The neuro is the LLM. The symbolic is a knowledge graph of medical concepts and connections. We ground the LLM in that graph so decisions use real clinical relationships, not just statistics.

Classic NLP gave about 65-70% out of the box coding accuracy. With neuro-symbolic we see about 92% out of the box on risk adjustment tasks. That is a step change.

Ask if they can implement now at your volume, how pricing and support work, how they prove trustworthiness on your data, and how they defend against RADV risk with evidence and explainability.

Do a small POC with real charts. We can provision access, receive charts, and show results on the same call. Following CMS guidelines, high accuracy should show up without training on client data.

NLP was assistive. Coders checked everything. Neuro-symbolic becomes augmentative. The system auto adjudicates high confidence codes, shows probability, and coders focus on true judgment calls. Trust builds over time.

No. AI is an enabler now. Advances arrive in weeks, not years. That is good for buyers. We stay ahead with applied research, domain focus, and fast productization.

We fund university research and turn it into applied algorithms for risk adjustment, coding, and CDI. Topics include trustworthy AI, neuro-symbolic methods, and both large and small language models.

A RADV audit tool built by auditors for auditors, and our neuro-symbolic coding platform that outperforms legacy NLP. The goal is trustworthy automation and fewer manual passes.

We do not need client data to train models. For large enterprises on Azure, we can deploy inside their own instance behind their firewall, so data never leaves and we do not access it.

We create multiple AI agents with clear personas: top coder on one model, another on a second, an evidence finder, judges, and a compliance auditor. They cross check each other and escalate only the hard cases to a human.

Tech cost rises but labor drops. Think excavator vs many shovels. Total cost goes down while accuracy, compliance, and only pass outcomes improve.

With old NLP, yes. With agentic, neuro-symbolic AI, we aim for one decisive pass plus targeted human augmentation where agents disagree. It is faster, cheaper, and more defensible.

Agents analyze every date of service, but we present the signal once with linked evidence. CMS wants accurate capture and management of true conditions. Looking across the year avoids noise, reduces burnout, and still defends every code.