In four 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 curriculumRiyalabs designs, builds, and operates AI co-workers using your existing data and existing tools – under human approval. We start with one workflow, not a transformation.
Each co-worker reads the right approved sources, prepares the work, and asks for human approval before anything goes out. One workflow first. Not a transformation.
Drafts board-ready summaries, explains variance, and flags anomalies — every number cited to its source row.
Summarize context, draft next steps, update CRM fields, wait for approval before sending.
Classify requests, prepare context, route work, flag exceptions before they grow.
Combine data from tools, records, and documents into a review-ready decision brief.
Search approved knowledge sources, summarize information, and cite where answers came from.
Promote a successful conversation into a repeatable, approval-gated workflow your team can call — built around your data, your approvers, your cadence.
The underlying capabilities every co-worker draws from. Deep expertise lives here — you rarely have to think about it.
Source-cited dashboards, refreshed on schedule, every chart explained in plain language.
Business questions answered with citations. Likely causes identified across approved sources.
What-if scenarios on your data — pricing, capacity, forecast variance — with assumptions you can audit.
A business goal tracked week-by-week. The next step recommended and routed to the right owner.
Always-on watcher across your data streams. Outliers and drift flagged before they grow.
Emails, packets, briefs — drafted with citations, queued for human approval before they go out.
Strategy, build, and run – the whole loop, one engagement. We stay around after launch, not just for the keynote.
We start with one workflow, not a company-wide transformation. Existing data. Existing tools. Human approval. Measurable value.
Book a workflow assessment →A practical, week-anchored engagement — not a giant platform rollout. Each week has a single named deliverable.
By the end of week one, the workflow is scoped and the data path is documented — with a single success metric.
Week two ends with a co-worker drafting against your real data — into a queue, not into the wild.
By week four the gate is live, the team is using it, and the metric is moving against the baseline.
Worried your data isn't AI-ready? That's our work. Mapping, cleaning, and shaping happen inside the same four weeks — never as a six-month precondition.
A co-worker for the workflow each team already repeats every week.
Operational trust signals – not certifications we haven't earned yet. These are baseline design principles for every Riyalabs co-worker.
Managed delivery, hybrid, or fully customer-hosted. We work the model that fits your team.
Riyalabs runs the co-worker on your behalf — agents, credentials, and data all stay inside your cloud account. We handle uptime, prompt tuning, model selection, and weekly reviews; you keep oversight and approvals.
Data stays customer-side. Riyalabs hosts the agent runtime in our environment, calling into your data via a least-privilege connector you control.
Agent runtime, credentials, data, and logs all stay in your environment. Your team operates day-to-day; Riyalabs is the implementation + support partner.
Riyalabs will help assess whether it's a good candidate for your first AI co-worker, what data it needs, what controls are required, and how to prove value safely.