Who does Omnihash work with?

We work with startups, SMBs, and enterprises investing seriously in a product. We're a senior, founder-led boutique software firm, so you work directly with the engineers building it — no account managers or hand-offs.

How much does an AI, MVP, or cloud project cost?

Engagements are project-based or a monthly retainer, scoped to your goals. We focus on production-grade work rather than the lowest bid, and most clients are funding a real product. We give a clear estimate after a short scoping conversation.

How quickly can you ship?

It depends on the project and scope — from a few days for a focused build, to weeks, to over a month for a larger platform. As one data point, we took a production-grade AI agent platform from zero to live in a month.

Can you add AI to our existing product?

Yes. We wire LLM features, RAG, and agent systems into existing products and stacks — not just greenfield builds.

Do you work with companies outside Chicago?

Yes. We're based in Chicago, IL and work with clients remotely across the US and worldwide.

Health-Tech · AI

Kayak-style clinical search for prescription options

A clinical search platform that uses AI agents and RAG to recommend and compare prescription options against ICD-10 code indications.

AI AgentsRAGLLMsICD-10

The problem

Clinicians needed a fast way to find and compare prescription options against the right ICD-10 indications — work that otherwise meant slow, manual cross-referencing across multiple sources, with little support for comparison.

Our approach

Built a clinical search experience — closer to a travel meta-search than a static formulary — that lets clinicians search, compare, and rank options.
Used AI agents and retrieval-augmented generation (RAG) over clinical data so results are grounded in source material rather than model guesswork.
Matched and validated options against ICD-10 code indications to keep recommendations clinically relevant.

Outcome

A working clinical search and comparison platform built on AI agents, RAG, and ICD-10 matching.
Recommendations grounded in source data and tied to the correct indications.
A foundation the team can extend to new data sources and clinical workflows.

Have a similar problem?

Tell us what you're building. We'll reply with how we'd approach it.

Start a Project

Anonymized at the client's request — sector and outcomes are real, identifying details removed.