We place senior AI engineers inside your team, on your tools, in your stand-ups. Not a consultancy. Not a pilot. Real people, joining your roadmap.
Sachith at his Desk
Onboarding day, April 2026
“Most teams I've worked with treat AI as a feature you bolt on later. At Stekz, the LLM layer is wired into the platform before the first product is built. It changes how you design everything.”
Chandra builds digital products on Stekz's proprietary platform, MyStekz — each one architected around Domain-Driven Design, meaning the product logic maps directly to how the client's business actually operates, not to generic technical assumptions.
Every product built on MyStekz connects automatically to BEP, Stekz's own LLM layer. That means conversational AI isn't a feature request that comes later — it's available from the first release, wherever it creates value. The primary development environment is Claude Code.
“We don't start with ‘what can AI do?’ We start with the manual, repetitive work our engineers are doing, and we automate it. The goal isn't to replace humans. It's to free them up for higher-level problem solving. When AI handles the boilerplate, engineering velocity goes up exponentially.”
Sachith builds generative AI workflows inside Okra.ai's internal platforms, automating tasks that engineers previously did by hand. If there's an established workflow for it, he looks for a way to let an agent do it.
“The problem with most enterprise AI is that it's top-down. Leaders read about a tool and try to force it into a workflow. True impact comes from the bottom up.”
“When you look at the raw data, the inefficiencies become obvious. You see where people are spending hours on tasks a model could do in seconds. That's where you start. You build small, targeted solutions that solve real problems, and you scale from there. It's not about finding a use case for AI; it's about finding a use case for efficiency.”
Most of what you need is already here. Senior AI engineers, hand-picked for your stack, embedded in your team within weeks. If you're not sure where to start, that's what the second layer is for.
Senior AI, ML and MLOps engineers placed inside your team. They join your stand-ups, ship to your repo, and report to your tech lead. We handle the people: you run the roadmap.
For partners who want to build genuine AI capability inside their teams — not just adopt the tools, but develop the judgement to use them well.
Most "AI engineers" stop at Copilot. Ours start there. We gave our stars an unfamiliar problem and 4 hours to build a multi-agent system. The team that placed third went back to work on Monday — and started rolling their QA automation workflow into production at Stekz.
Real multi-agent architecture, not single-prompt demos.
That's the benchmark. This is the talent you'd be hiring.
An opinionated 5-step assessment to figure out whether you're ready to embed AI engineers — and what you should do first. Written from ten years of placing engineers.
We will email it to you. No nurture sequence, no sales call — unless you ask for one.