Infrastructure and AI expertise, applied to healthcare.
We bring production-AI and security experience to help clinics adopt assistants safely, with governance that keeps people in charge.
Building AI agents since before they were a category.
MediReady comes out of a decade of production infrastructure and early agent-tooling work, pointed at one problem: making clinics AI-ready without handing them risk. We build and run the systems ourselves rather than wrapping someone else's API.
The foundation
A decade of systems that aren't allowed to fail
10+ years designing, shipping, and hardening production infrastructure and security: the discipline regulated clinics need from day one, not bolted on later.
Production AI, early
RAG, LLM systems, and agents in real organizations
We built and operated retrieval, LLM, and agent systems under real constraints (access controls, audits, uptime) before 'AI agent' was a pitch-deck word.
MCP, from the first releases
Agent tooling on the Model Context Protocol
We were building and operating MCP servers and agent infrastructure while most of the market was still discovering what the protocol was. That early, hands-on depth is why our agents are governed systems, not demos.
MediReady, today
All of it, applied to clinics
Multi-channel agents on one governed knowledge layer, with human oversight, monitoring, and honest claims: the way production AI should be run.
Prefer email?
Write to us directly. The founder reads it.
The people behind MediReady.
Infrastructure, security, and production-AI experience, applied to healthcare with people in control at every step.
Harshit Luthra
Founder
Infrastructure & AI engineering
Senior infrastructure and AI engineer with 10+ years shipping and hardening production systems (Toptal Top 3%). Through his consultancy k8s.org.in he builds secure, reliable production AI: RAG and LLM systems, agents, and the platform and security hardening that regulated workflows demand.
Sai Krishna
Advisor
Senior Software Engineer, TrueFoundry
Builds and validates production AI systems for large organizations, with a background in software engineering and generative AI.
How a readiness engagement comes together.
An illustrative, synthetic example shown until a client story is approved for publication.
A hypothetical multi-location primary care group with a stretched front desk, long after-hours voicemail queues, and no shared view of what its early AI experiments were doing.
- 01
Audit
Mapped workflows, data sources, risks, and where a human must stay in the loop.
- 02
Blueprint
Defined channels, escalation rules, ownership, and governance before any launch.
- 03
Deploy
Rolled out web, phone, and internal assistants on one approved knowledge layer, in stages.
- 04
Operate
Monitored outcomes, reviewed escalations, and versioned prompts and knowledge over time.
What the engagement produced
- After-hours calls answered and triaged instead of lost to voicemail
- Routine intake and FAQs handled, freeing staff for in-person care
- A single dashboard showing volume, escalations, and knowledge gaps
Q3 2026 intake is open
Start with an AI readiness audit.
Map the workflows, data, risks, and opportunities that will determine where AI can create value safely.
Deliberately few clients at a time. We onboard a few clinics per quarter. Once this quarter's roster fills, new engagements start the following quarter. Every engagement gets senior, hands-on attention because we never run more than a handful at once. If we're at capacity, we'll say so and give you a real start date.
This is a marketing form, not a patient channel. Please don't share patient information. We'll scope security and data handling during the audit.