Multi-agent systems for workflows
a single agent cannot own.
Increase sales, reduce costs, boost customer satisfaction, and automate workflows using multi-agent systems.
Three-agent product advisor guiding customers through print-order configuration.
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Text-to-workflow agents building validated node graphs inside the platform.
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Bilingual English/Arabic agent inside ARIJ's Moodle environment, answering only from ARIJ's own knowledge base.
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HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark.
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Supply chain intelligence agents reducing manual coordination overhead.
AI agent implementation for a global automotive enterprise.
Specialised agents split a workflow no single agent can own end-to-end — validation, routing, retrieval and correction each handled by the agent built for it, then handed on.
Agentic systems smoothen the ordering process and make it more comfortable, especially when the products being configured are sophisticated. Mixam's published figure is 11.76%, scoped to day 1 of its Australian launch.
Agentic systems deliver immediate, accurate and reliable responses that take time and effort to prepare manually.
With better user experience and comfort, customers increase interactions with the system — be it in e-commerce, patient management or education.
Production workflows split across specialised agents — text-to-workflow generation, print-order configuration, clinical-guideline retrieval, and multilingual knowledge support.
“My wish was to come to at least an 80% success rate in the workflow results, and by the time we finished the project, the success rate is, I believe, over 95.4%, so it definitely exceeded expectations.”
95.4%
Success rate in workflow results
ARIJ Network · Investigative journalism
A bilingual English/Arabic autonomous agent embedded in ARIJ's Moodle environment, answering only from ARIJ's own knowledge base — across 22 countries.
1% → 100%
Knowledge-inquiry response rate before and after
Schmitt-Thompson · Clinical triage
Nurse-triage guidance where a wrong recommendation is a patient-safety event — staged retrieval and validation rather than one model answering in a single pass.
44% → 98%
raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark
An end-to-end multi-agent implementation framework that starts from business consultation, followed by technical implementation, and ends with knowledge and ownership transfer — delivering bespoke agentic AI services to mid-market leaders.
A consulting-led planning phase delivered through focused workshops that converts AI ambitions into a concrete, measurable implementation plan. We align stakeholders on business outcomes, prioritize the best-fit use cases (value vs feasibility vs risk), and map current workflows (As-Is) against the target operating model (To-Be) — defining what the agent should do, what information it needs, and when it must escalate to humans.
A rapid, agile delivery phase that turns the selected use case into a working Proof of Value and then iterates toward a production-ready MVW — proving ROI before full-scale rollout. With clear success metrics defined in Phase 1, we build in small, decisive increments: each sprint delivers tangible capability, tested with real users and real data.
The phase where AI becomes real operating capability — not a standalone tool. We embed Vstorm experts into your delivery rhythm to drive adoption, ensuring knowledge transfer and measurable outcomes. We focus on the organization as much as the technology: standardizing how teams use the agent, designing handoffs and accountability, training users, and setting up governance.
Our engineers build systems where the model is a component only, not a hard-coded foundation. We ensure your solution's independence by:
Map the workflow, prove the orchestration on real data, and deploy with monitoring and team handoff.