Don't know what to automate first?
Walk it through your workflow — the one that eats your week — and it'll tell you straight whether that's an agent, an automation, or a build.
Process
One call. We learn how your operation actually runs, find the workflows quietly costing you time and money, and hand back a plan — what gets built, why it's worth it, and what changes once it's live. No code gets written until you've seen that plan.
The founders who ran that call are the ones who build it — custom to how you actually work, not a template. Your team stops doing the repetitive part, the hours and the costs that came with it go away, and from there the system just runs.
Capabilities
Voice agents that answer, qualify, and book around the clock — natural enough that callers forget they're talking to software.
Automation for the operational work that quietly eats a week — chasing failed payments and overdue invoices, process and CRM automation, real-time inventory and reorder tracking, and hiring or onboarding handled by systems instead of somebody's afternoon.
Growth systems that bring the right audience and convert them, plus bespoke software built around how your business actually runs — custom CRMs, client portals, and internal tools from the same hands that ship our own products.
Our own product, live at cursv.ai: a teacher uploads any assignment and Cursv solves it, then returns a complete answer key rendered in their own handwriting.
Track Record
Growth and savings created for clients through AI systems — the track record we point to when we scope a new build.
Repetitive processes handled seamlessly, so nobody on the team is stuck babysitting them.
Hours returned to people who used to spend them on work a system now owns — the same principle behind Cursv (cursv.ai), the hours we give back to teachers every week.
Voice agents on the line around the clock — qualifying, booking, and following up so nothing falls through.
Team
No account managers. No handoffs. The people on this page scope the work, write the code, and answer when something breaks.
Co-Founder
Arbaaz is an AI engineer at Microsoft, working on production machine learning systems at scale. He's been building with AI since 2019, back when language and image models were still rough and unreliable, years before ChatGPT existed.
At Rayth, he architects the AI systems behind every engagement — the models, pipelines, and infrastructure that turn a client's manual process into something that runs itself.
Co-Founder
Omar is a mechanical engineer by training who's been building with AI since 2019, starting with the earliest generation of language and image models. He ships vertical SaaS products end to end, including Cursv, DentalOS, and DetailersOS.
At Rayth, he owns product and delivery — turning a client's first call into a working system, not a deck of promises.
Co-Founder
Ramsey is a software engineer at Microsoft, building and shipping production software at scale. He's brought that same engineering discipline to AI systems since 2019.
At Rayth, he leads full-stack development — the backend, integrations, and infrastructure that keep client systems reliable long after launch.
What clients say
Every number below is the founders’ own track record. Client testimonials land here as engagements wrap.
$740K
Revenue generated
50K+
Tasks automated
29K+
Hours saved
Get started
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FAQ
Four things: AI agents that own a workflow, automations that remove busywork, full-stack AI products, and custom or fine-tuned models. Everything ships to production — we don't do pilots that live in a sandbox forever.
Fixed scope, fixed price, fixed date — agreed in writing before we start. No hourly billing, no surprise invoices. If the scope changes, we re-quote before we build.
Small automations ship in weeks; full products take longer. You'll have a date before we begin and a working demo every week while we build — so you never wonder where things stand.
No. Your data stays yours. Models we fine-tune on it belong to you, and we put that in the contract.
Common — usually the demo worked and the workflow didn't. We start from the workflow, wire the AI into the tools you already use, and instrument it so you can see it earning its keep.