Enterprise AI, built by a senior team in your stack

If you searched for "enterprise AI solutions," here's our honest take: the solution you need is usually a team, not another platform. Most enterprises already have the models, the cloud and the data. What they lack is senior engineers who can turn a promising pilot into a system that runs in production, passes security review and keeps working after launch. That's what our AI pod teams do.

What an AI pod team is

An AI pod is a dedicated team of senior engineers that works as one unit in your company. A typical pod is about seven people: a tech lead, an ML engineer and backend engineers, with data, frontend or design added when the work calls for it. They join your repos, your CI and your standups, run weekly demos, and build on the stack you already use: AWS, Azure, GitHub, Postgres, and Anthropic and OpenAI models.

This is forward-deployed engineering. The pod works in your cloud, alongside your team, so nothing you depend on lives in a vendor's black box. When the work is done, the pod can hand the whole system over to your engineers, or stay and keep growing it.

Why enterprise AI stalls, and how a pod fixes it

• Pilots that never reach production. A demo proves the idea; production needs evaluation, monitoring, access control, integration with real systems and an on-call owner. A pod is staffed to do all of it.
• Platforms that don't fit your stack. Off-the-shelf AI products force your process into their shape. A pod builds on your infrastructure and your data, under your security policies.
• Consultancies that hand over slides. Strategy decks don't ship software. Our engineers write the code, review it with your team and stay accountable for it running.
• Hiring that takes quarters. Recruiting a senior AI team in-house takes months. A pod is scoped, matched and working in your repos in a fraction of that time.

What our pods have shipped

Our pods have production systems behind them, not just prototypes:

• AI fraud detection for a global fleet company: 244K+ invoices scored, under 30 seconds per invoice, $5.94M in confirmed fraud, validated by the client's fraud team.
• AI marketing compliance for a global capital management firm: 8,000+ documents reviewed, turnaround cut from days to hours.
• AI voice platform: more than 1M outbound calls.
• AI driver-support agent: 218K conversations a year, in three languages.
• AI real-estate agent working 24/7.

Read the details in our case studies.

How it works

1. Talk. Tell us what you want built and where it has to run.
2. Match. We scope a dedicated team to your stack. You get a plan, a price and the names of the people who would do the work.
3. Embed. The pod joins your repos, CI and standups and runs weekly demos.
4. Grow. Add people or disciplines as the work grows, or take the whole system over when it's done.

Who we work with

We're trusted by Fortune 500 engineering teams and serve enterprises across industries, including financial services, healthcare, logistics, real estate and software. Our engineers are senior, hand-vetted (only the top 1% make it through our vetting process) and based in Latin America, working on US business hours, so review and decisions happen the same day.

When a pod is not the right fit

• You want a board-level transformation program or a management-consulting engagement. A big consultancy is built for that.
• You want to buy a finished AI product off the shelf. A platform vendor will be faster.
• You need follow-the-sun coverage across Europe and Asia time zones.

Comparing options? See our guides to AI pod team providers, forward-deployed engineering firms and partners that take an AI pilot to production, or how we compare with Tribe AI and Thoughtworks.

FAQ

Do you sell an AI product or platform?

No. We provide dedicated engineering teams that build AI systems in your stack. You own the code, the infrastructure and the result.

How big is an AI pod?

A typical pod is about seven senior engineers, including a tech lead and an ML engineer. We size it to the work, and you can add people or disciplines as it grows.

Where does the work happen?

In your cloud and your repos, under your security policies. Our engineers work on US business hours, including New York hours.

How is it priced?

It's typically $7,000 to $10,000 a month per mid-level engineer and $10,000 to $15,000 for senior engineers; we scope a quote to your team. Before you sign, you get a scoped plan, a price and the names of the people who would do the work. See rates by role, or model your team in the talent cost calculator.

Questions we didn't answer? Email info@ryzlabs.com.