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.

Blog

Notes from the build

Practical, experience-based writing on AI, RAG, agent-driven development, and shipping software to production.

AIJun 24, 2026 · 8 min
How much does it cost to build an AI MVP?
A scope-first framework for budgeting an AI product: what actually drives cost and timeline, what a realistic range looks like, and how to ship the first version in weeks instead of quarters.
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RAGJun 20, 2026 · 9 min
RAG vs fine-tuning: which one do you actually need?
A decision guide based on production experience. What retrieval-augmented generation and fine-tuning each actually solve, when to use which, and why most teams reach for fine-tuning when they should not.
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AIJun 16, 2026 · 8 min
How to add AI to an existing product without a rewrite
Most AI work is not greenfield. A practical guide to bolting AI onto a product you already have: where to start, the integration patterns that are safe, and the failure modes to avoid.
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AIJun 12, 2026 · 8 min
Build vs buy: custom AI or off-the-shelf?
An honest decision framework for choosing between an off-the-shelf AI tool and a custom build. When buying wins, when building wins, and the total-cost questions teams forget to ask.
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RAGJun 9, 2026 · 9 min
Shipping RAG to production: what actually matters
A RAG demo takes an afternoon. A RAG system real users trust is a different animal. The things that decide whether retrieval-augmented generation works in production: retrieval quality, evals, grounding, and guardrails.
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CryptoJun 5, 2026 · 7 min
The hidden five-figure bug: lessons from a crypto infrastructure audit
An advisory engagement uncovered a bug driving tens of thousands of dollars per month in unnecessary blockchain RPC costs. How that kind of waste hides, how we found it, and what you can check yourself.
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Agent-Driven DevelopmentJun 2, 2026 · 7 min
Agent-driven development: how we actually use AI coding tools
Agent-driven development multiplies what a senior engineer can ship without giving up code quality. How we use Claude Code, Codex, and Cursor in practice, and how to adopt it on a team without creating technical debt.
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Notes from the build

How much does it cost to build an AI MVP?

RAG vs fine-tuning: which one do you actually need?

How to add AI to an existing product without a rewrite

Build vs buy: custom AI or off-the-shelf?

Shipping RAG to production: what actually matters

The hidden five-figure bug: lessons from a crypto infrastructure audit

Agent-driven development: how we actually use AI coding tools