AI budgets fail in one of two ways: paying six figures to discover the model cannot do the job, or under-funding the unglamorous engineering that separates a demo from a system. Our pricing is built to prevent both. A scoped proof-of-concept against your real data runs $10,000–$25,000 over two to four weeks. A production system — agent or automation with evaluation suites, fallbacks, and observability — runs $25,000–$150,000+ depending on integrations and stakes.
The structural point matters more than the ranges: every AI engagement we take is gated. You spend the small number to earn confidence before anyone commits the big one.
Why the PoC is priced separately — and fixed
The PoC answers one question: can this work on your data, to a bar we agree in writing before we start? Accuracy targets, evaluation cases, and the definition of "good enough" are fixed up front, so week two delivers a verdict rather than a vibe. If the answer is no, you have spent $15,000 to avoid spending $150,000 — the best money in AI. Around a fifth of our PoCs end with us recommending against the build. We consider those successes.
“The cheapest AI project is the one that fails in week two for $15,000 instead of month eight for $200,000.”
What moves a production build across the range
Integration surface is the biggest driver — an agent that reads one knowledge base is a different animal from one that acts across your CRM, billing, and support desk. Stakes come second: a system that drafts for human review needs lighter guardrails than one that acts autonomously. Then volume and latency, which set the infrastructure bill. Model API costs, for perspective, are usually the smallest line — typically hundreds to a few thousand dollars monthly, dwarfed by the engineering around them.
The ongoing line nobody budgets
Models drift, providers deprecate, prompts rot against new model versions. Production AI needs an owner: expect 10–20% of build cost annually for evaluation reruns, model migrations, and improvements. We run our own AEO platform, Sourceable, against four LLM engines around the clock — that operational reality is baked into every estimate we give, because we pay the same bill ourselves.
If you are budgeting an AI initiative, start with the question the PoC answers, not the system you imagine at the end. We will help you write the evaluation criteria before you spend real money — whether or not you build with us.
Raju Khunt
Emperor Brains LLP
