In 2—4 weeks we ship a working co-worker for one real business workflow – your data, your tools, human approval.
See the engagementYour data does not need to move to Riyalabs. We map the workflow, identify the minimum context, and choose the deployment model that fits your risk level.
Read the security briefCohort-style learning labs for marketing, finance, ops, and exec teams. Hands-on with real workflows, not slides.
See the curriculumYour team gets the working co-worker. Riyalabs runs the observability, guardrails, evaluation, output quality, and agent security behind it – so you don't have to staff an MLOps team to ship one workflow safely.
Observability, guardrails, evaluation, output quality, and agent security. Each one is its own surface – and Riyalabs runs all five on your behalf so your team gets the best co-worker without having to staff this work.
We trace every action, approval, and source. You get a clean weekly read.
We define and enforce what the co-worker can – and explicitly cannot – do.
We score every release – accuracy, citation grounding, regressions – before users see it.
We track style, format, factuality, and acceptance – and tune the co-worker until users keep its drafts.
We actively defend against prompt-injection, jailbreaks, and data exfiltration – and red-team our own deployments every release.
A monitoring surface is only useful if someone reads it. We do – these are the four screens we build and watch for every co-worker we ship. You see the summary; we handle the noise.
Active sessions, queued tool-calls, and the user on the other end of each one. One click to drop into any trace.
Pending high-impact actions awaiting a named approver – with the source context and the suggested response side by side.
Rolling 7-day score for how many claims trace cleanly to a real source. Trend line, regressions flagged, per-workflow breakdown.
Rolling 7-day count of prompt-injection attempts, exfil flags, and tool-call anomalies – with the source and severity attached.
Monitoring isn't a generic dashboard pack – it's specific to the workflow, the data sources, and the approval boundaries. Here's the path from launch to long-term operations.
You don't need to staff an MLOps team. Every Riyalabs co-worker comes with these five disciplines from day one – operated by us, summarized for you. Bring us your workflow and we'll show you exactly what we'll be watching on your behalf.