AI & Machine Learning
AI is probably not your answer. If it is, we'll build it properly.
Most companies adding AI right now will quietly regret it within 18 months. 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.
The problem
The AI gold rush is producing more wreckage than wealth.
Right now, somewhere in your industry, a team is paying a serious sum 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 mostly a rule engine with a thin layer of theatre. A board is approving an AI initiative because every other board did, not because the math says it will return.
We've seen the receipts: hallucinated customer support replies, recommendation engines that quietly degraded conversion, auto-generated content that tanked SEO, and the compliance headache 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
Three commitments we make. Most AI partners can't.
We'll tell you when not to build.
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 low-cost SaaS tool. We'll tell you that in the first conversation. We'd rather lose the build and earn the trust than take your money and waste your year.
We're engineers first, model-wranglers second.
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, and compliance — the unsexy work that determines whether your AI feature survives contact with real users.
We price on outcomes, not tokens.
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.
What we build
What we actually build
AI-native product engineering
When AI is the product. Custom LLM applications, RAG systems with real retrieval pipelines, and agentic workflows that actually agent — built with the same engineering discipline as any other software, so they work the hundredth time, not just in the demo.
Workflow automation that earns its keep
Document processing, data extraction, classification, and summarisation applied to specific workflows where the unit economics actually work. We measure cost per task before we build, not after.
AI feature integration for existing products
Adding a model to a product that already works, without breaking the parts that work. Search improvements, smart suggestions, and customer-facing copilots, built with proper evaluation, fallbacks, and the ability to turn the model off when it fails.
Internal tools & operator copilots
The unsexy, high-ROI category most agencies skip. Internal tools that multiply your team's leverage, built fast, scoped tight, often shipped in under six weeks.
Custom model fine-tuning & evaluation
When off-the-shelf models aren't enough. Fine-tuning, evaluation harnesses, prompt optimization, and model selection, with a clear answer to whether it's actually better than the baseline.
Before we build
The Critonyx AI Diagnostic
A one-week structured engagement. We look at the business case, the data, the workflow, and the ROI math, and produce a written recommendation. Sometimes that recommendation is yes, here's the architecture and the roadmap. Sometimes it's no, here's the simpler solution that will save you a great deal of money.
Most founders save several times the diagnostic cost in the first month, usually by not building something they were about to commit to.
You walk out with
- Use case validation: does AI actually solve the business problem?
- Data audit: do you have what you'd need to make it work?
- Build-vs-buy-vs-skip recommendation
- Cost and ROI modelling
- A written architecture brief, or a written do-not-build memo
How we work
The ADAS Framework, adapted for ai & machine learning
Architect
Before any model gets called, we map the system around it. Data sources, evaluation criteria, fallback behavior, cost ceiling, and success metric — the boring decisions that determine whether the AI ships or stalls.
Deliver
Specialist AI engineers, weekly cadence, milestone-priced. We build the pipelines, the prompts, the evaluation harnesses, and the production guardrails, not just the demo.
Advise
Long-term partnership for founders navigating AI's fastest-moving questions: when to switch models, how to handle costs at scale, and where the regulatory floor is shifting.
Scale
Once the system works, we scale it without the cost curve eating you alive. Caching strategies, model routing, evaluation in production, and monitoring that tells you before the customer notices.
Proof
“We walked in convinced we needed a custom model. Critonyx walked us out of a costly mistake and into a solution that worked better.”
Founder, B2B SaaS
Read the case studyThe process
What happens after you reach out
30-minute fit call
You describe the problem. We ask the hard questions: workflow today, cost of getting it wrong, who's using the output, and the 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.
The Diagnostic
Optional but recommended. One week, written recommendation. You leave with clarity even if we never work together.
Scoping & architecture
If we're building, we map the system: data, models, evaluation, fallbacks, cost ceiling, and milestones. Outcome-based pricing, agreed before kickoff.
Build & deploy
Most AI engagements have a working pilot in 4 to 8 weeks. Production-grade systems in 12 to 16. Weekly demos, no surprises.
Partnership
Models drift, costs change, and regulations move. We stay on as your AI partner long after launch, because the launch is the easy part.
The smartest AI decision a founder can make is sometimes not to build.
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 thirty 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.
Start the conversation
Ready to ship?
If you're a founder or operator building something serious, and you're tired of hourly billing, slow timelines, and partners who don't understand your business. Let's talk.
Prefer email? Write to us directly at info@critonyx.com