AI & Machine Learning
Most companies adding AI right now will quietly regret it within 18 months. Bolted-on models, hallucinated outputs, vanity features that look impressive in demos and bleed budget in production. Critonyx is the AI partnership for founders and operators who want to know — before the build — whether AI is actually the right tool. And if it is, we ship it like engineers, not evangelists.
Right now, somewhere in your industry, a team is paying $80K to bolt an LLM onto a process that worked fine without it. A founder is fundraising on an "AI-powered" pitch deck for a product that's 90% rule engine and 10% theatre. A board is approving an AI initiative because every other board did — not because the math says it'll return.
We've seen the receipts. The hallucinated customer support replies. The recommendation engines that quietly degraded conversion. The auto-generated content that tanked SEO. The compliance nightmare of a model trained on the wrong data.
The pattern is always the same: the wrong question got asked first. "How do we add AI?" instead of "Should we?"
We exist to ask the second question — and then either talk you out of the project, or build it correctly.
Our commitments
Roughly 40% of the AI projects we're approached with shouldn't exist. They're problems better solved by a database query, a process redesign, or a $20/month SaaS tool. We'll tell you that — for free — in the first conversation. We'd rather lose the build and earn the trust than take your money and waste your year.
Most AI agencies are wrappers around APIs. We're a software partnership that also does AI — which means the systems we build around the model are as rigorous as the model itself. Data pipelines, evaluation harnesses, fallback logic, observability, compliance. The unsexy 80% that determines whether your AI feature survives contact with real users.
Milestone-based pricing, signed before kickoff. When the model doesn't work in production, we don't get paid for the meeting that explained why.
See How we price →
When AI is the product. Custom LLM applications, RAG systems with real retrieval pipelines, agentic workflows that actually agent. Built with the same engineering discipline as any other software — meaning they work the second time and the hundredth time, not just in the demo.
Document processing, data extraction, classification, summarisation — applied to specific workflows where the unit economics actually work. We measure cost-per-task before we build, not after.
Adding a model to a product that already works — without breaking the parts that work. Search improvements, smart suggestions, customer-facing copilots. Built with proper evaluation, fallbacks, and the ability to turn the model off when it fails.
The unsexy, high-ROI category most agencies skip. Internal tools that 10× your team's leverage — built fast, scoped tight, often shipped in under 6 weeks.
When off-the-shelf models aren't enough. Fine-tuning, evaluation harnesses, prompt optimisation, model selection — with a clear answer to "is this actually better than the baseline?"
The killer offer
A one-week structured engagement. We look at the business case, the data, the workflow, and the ROI math — and we produce a written recommendation. Sometimes that recommendation is "yes, here's the architecture and the roadmap." Sometimes it's "no, here's the boring solution that'll save you $200K."
You walk out with:
Most founders save 6× the diagnostic cost in the first month — usually by not building something they were about to commit to.
The framework
Before any model gets called, we map the system around it. Data sources, evaluation criteria, fallback behaviour, cost ceiling, success metric. The boring decisions that determine whether the AI ships or stalls.
Specialist AI engineers, weekly cadence, milestone-priced. We build the pipelines, the prompts, the evaluation harnesses, and the production guardrails — not just the demo.
Long-term partnership for founders navigating AI's fastest-moving questions: when to switch models, how to handle costs at scale, where the regulatory floor is shifting.
Once the system works, we scale it — without the cost curve eating you alive. Caching strategies, model routing, evaluation in production, monitoring that tells you *before* the customer notices.
"We walked in convinced we needed a custom model. Critonyx walked us out of a $300K mistake and into a $40K solution that worked better."
What happens next
Free. You describe the problem. We ask the hard questions: workflow today, cost of getting it wrong, who's using the output, regulatory surface. By the end of the call, we'll tell you straight whether this is an AI problem or a different kind of problem.
One week. Written recommendation. You leave with clarity even if we never work together again.
If we're building, we map the system: data, models, evaluation, fallbacks, cost ceiling, milestones. Outcome-based pricing — agreed before kickoff.
Most AI engagements have a working pilot in 4–8 weeks. Production-grade systems in 12–16. Weekly demos, no surprises.
Models drift, costs change, regulations move. We stay on as your AI partner long after the launch — because the launch is the easy part.
Ready to talk
If you're being pushed toward an AI initiative and something in your gut says "wait" — listen to it. Then book a call. We'll spend 30 minutes telling you whether your instinct is right. No pitch, no pressure. Just the truth, from a team that's seen the wreckage and built the systems that work.