Your agents, running where your data lives
Agents that go live in your own cloud account, built to pass your security review, with load issues found before deploy and your team running them.
AI agent implementation for a global automotive enterprise.
Supply chain intelligence agents reducing manual coordination overhead.
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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HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark.
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Bilingual English/Arabic agent inside ARIJ's Moodle environment, answering only from ARIJ's own knowledge base.
Read case studyA notebook agent hits security review, data residency and real traffic at once.
Where it stallsAPI keys in a developer's .env file.
What we do Secrets in your vault, scoped per service.
Where it stallsThe agent runs on a vendor platform.
What we do In your cloud account or private network.
Where it stallsNo rollback plan, nobody on call.
What we do One-step rollback, runbooks for your on-call.
Environment review first, on-call handover last.
We start with your cloud: network, identity, data residency and the security review.
ARIJ's agent runs inside ARIJ's own Moodle and answers only from ARIJ's knowledge base. We start from our open-source template, the same layout we run in our own systems.
Each release keeps the last one ready, and we rehearse runbooks with your team before handover.
Open standards, open-source tooling and published research, all public, so you can check our engineering before you hire us.
Shaping agentic AI
The Linux Foundation formed it around Anthropic's MCP, OpenAI's AGENTS.md and Block's goose. AWS, Google and Microsoft are platinum members. In its own words: “Together, we're shaping the future of agentic AI.”
Open source
Framework contributors
We maintain our own open-source agent framework and contribute fixes to the frameworks our agents run on, through official partnerships with their teams. Breaking changes reach us before they reach your codebase.
Research
Peer-reviewed papers on agent architectures, evaluation and AI security, and an open benchmark anyone can rerun against our results.
Track record
Our client agents were in production before most of today's agent frameworks had a stable release. You get a team that already knows what breaks between a promising demo and a system people rely on every day.
The agent drafts each workflow and checks it in several steps before it runs, so engineers skip hours of setup.
2 hrsto prepare a workflow by hand
3 minto generate one with the agent
Three agents and 15 tools walk customers through print-order configuration, across more than a billion product combinations.
~20% → ~40%conversion to a final sale
96%+web-to-print orders handled end to end
The model answers the assessment questions. The guideline decides the disposition, the same way every time.
44% → 98%accuracy, raw LLM vs guideline-executing
94%weighted F1 across 591 scenarios
An English and Arabic agent inside ARIJ's Moodle that answers only from ARIJ's own knowledge base.
1% → 100%of inquiries answered, desk vs agent
22countries the network covers
Each path starts with a free call with a founder or senior AI engineer. A build goes ahead only after its Proof of Value meets the agreed threshold.
We map one workflow with your experts and agree a success threshold before any build.
Best whenYou want the success test agreed before you commit to a build.
Picked for your environment, with the model as a replaceable part.
Infrastructure code in your repository, rollback rehearsed with your team before handover.
Agents that issue refunds need identity, authorisation, approval and audit before the call lands. What an agentic AI control plane is, and when you need one.
Why system access (VPNs, API keys, ERP exports) belongs on the critical path of an agentic AI project, with a named owner and deadline.
One agent or several? Deterministic orchestration beats both when the flow is predictable. Pros, cons and the architectures, with production examples.