Ryz Labs handles enterprise AI implementation with senior AI pods that take a working pilot to production with your team: use-case scoping, data access and security reviews, evals, guardrails, integration into the systems you already run, rollout to real users, and monitoring and cost control once it is live. Every engineer comes from the top 1% of the tens of thousands we interview and works on your team on US business hours, including New York hours. The pod stays until your people can run the system without us.
Most enterprises already have a pilot that impressed a steering committee. AI implementation services exist for the gap between that demo and a system that passes security review, handles real traffic, has an owner and a budget, and that the business actually uses. That gap is mostly engineering and organizational work, not model selection. If you are still choosing the use case, start with AI consulting; if you need a new system built from scratch, see AI development; if the job is connecting a model to one system, see AI integration.
What happens after you contact us: We'll sign your NDA before we look at the pilot. The first conversation covers what the pilot does today, who sponsors it and which reviews it still has to pass. It ends with a written plan, a price and the names of the people who would do the work.
Talk. We learn why the pilot stalled: security review, missing data access, unclear ownership, quality on real inputs, or cost at scale.
Match. We propose a pod scoped to that gap: a tech lead, an ML engineer for evals and models, backend engineers for integration, and data or platform engineers if access and deployment are the blockers.
Join. The pod works in your cloud accounts, repos, CI and standups, with weekly demos to the business owner. Access stays under your control and you own all code and IP.
Grow. Take the next use case on the same foundation, or hand the system to your team once they own it in practice.
Week 1 is typically access, running the pilot against real inputs, and listing every review it must pass with the people who sign each one. By month 1 the usual picture is an eval set with a baseline score, the security package submitted, and the integration built in a staging environment. By month 3 the system is often live for a pilot cohort with monitoring and cost reporting, and the rollout to more users is planned from what that cohort showed. Each stage depends on scope and how fast your reviews and access move.
Pilots rarely fail because the model is weak. They stall on the work around it. These are the failure modes a senior pod plans for:
Proof from production: an AI pod built marketing compliance review for a leading alternative asset manager, processing 8,000+ documents and cutting review time from days to hours. See the case study and more case studies.
Typical Ryz cost is $7,000 to $15,000 per engineer per month: mid-level $7,000 to $10,000, senior $10,000 to $15,000, and leads $15,000 or more, quoted per team.
Project cost is team size × months × monthly rate, so a production-readiness pod that runs three months costs from $105,000. Every quote is scoped per team, and you get a plan, a price and the names of the people before you start.
A dedicated AI pod team fits when one group should own the path to production: reviews, evals, integration and rollout, with a tech lead accountable for it. Staff augmentation fits when your AI team already owns the plan and needs senior capacity: you can hire AI engineers who join your sprints and report to your leads. Either way, the engineers work on your team, in your cloud and repos. Many are based in Latin America, so they share your working day for reviews and launch calls. For how we approach this at scale, see enterprise AI.
Ryz doesn't sell a proprietary AI platform, so if you want a vendor product configured for you, a platform vendor fits better. If you need a board-level transformation program across the whole company, that belongs with a large consultancy. And if your team needs engineers on European or Asian hours, a global network will give you better overlap.
It is the work of taking an AI use case from a pilot to a system the organization runs: passing data and security reviews, proving quality with evals, adding guardrails, integrating with existing systems and identity, rolling out to users, and monitoring quality and cost after launch.
AI consulting decides what to build and proves it on your data. AI integration connects a model to specific systems. AI implementation covers the whole path to production for a use case that already exists as a pilot, including reviews, rollout, change management with your teams and handover.
Cost is team size × months × monthly rate. Typical Ryz rates are $7,000 to $15,000 per engineer per month; a three-person production-readiness pod starts at $35,000 per month and a full pod of about seven senior engineers starts at $75,000.
After the scoping call we propose a pod with names, and engineers can start in under 2 weeks once you say go. Most of the timeline after that depends on scope and your onboarding, especially access and review schedules.
That is the goal. Your engineers review the code throughout, own the eval set and runbooks before launch, and the pod hands over once your team handles releases and incidents on its own.
Questions we didn't answer? Email info@ryzlabs.com.
Thanks — your message has been sent. We’ll get back to you soon.
Something went wrong while sending your message. Please try again or email info@ryzlabs.com.