What we do
We turn OpenClaw from an impressive open-source assistant into a controlled business capability with the right hosting, tools, permissions and operating boundaries.
The service
OpenClaw is an open-source personal AI assistant capable of holding memory, calling tools and acting across multiple channels — which is exactly why it needs deliberate boundaries before it touches real business systems. An assistant with unscoped access to email, calendars or internal APIs is a liability regardless of how capable the underlying model is. We treat governance as part of the build, not an afterthought bolted on later.
We start by mapping the actual use case: what decisions the agent should make on its own, what it should always escalate, and which systems it genuinely needs to touch. That scope determines the architecture — which model provider, how memory is stored, which skills get built versus deliberately left disabled, and what a sensible failure mode looks like when the agent hits something outside its remit.
The gateway runs in an isolated environment — containerised, network-restricted, with credentials scoped to only the systems it needs — so a compromised or misbehaving agent can't reach further than intended. We configure logging at the tool-call level, so every action the agent takes traces back to the request that triggered it, which matters as much for debugging as it does for audit.
Custom skills connect the assistant to WhatsApp, Telegram, Slack, email or internal APIs, built and tested against real workflows rather than demos. Once live, we monitor for failures, cost drift and unexpected tool use, and keep backups of configuration and memory so the assistant can be restored quickly if the host environment changes or a model provider update breaks something.
Capabilities
Designed for outcomes
What you receive
Technology
We stay technology-flexible and select the stack around your existing environment, security constraints, team capability and long-term cost.
Frequently asked
Either. Some clients prefer we host and manage the gateway on isolated cloud infrastructure with an SLA; others require on-premise or on-device deployment for data-residency or compliance reasons. The architecture and hardening approach stay the same either way — only who operates the underlying server changes.
Permissions are scoped per tool and per integration rather than granted broadly — the agent can only call the specific functions we've built and connected. Higher-risk actions, such as sending an external email or modifying a record, can require explicit confirmation instead of running autonomously, depending on how much autonomy the use case actually warrants.
It depends on the workload and data sensitivity. General-purpose reasoning and channel handling often run well on hosted models from Anthropic or OpenAI; workloads touching sensitive internal data sometimes call for a self-hosted or local model instead, trading some capability for keeping data off third-party infrastructure. We help weigh that trade-off case by case.
A focused single-channel deployment — one messaging channel, a handful of skills — typically takes two to four weeks from scoping to handover. Multi-channel deployments with several custom integrations and stricter governance requirements run six to ten weeks, most of that time spent on integration testing rather than initial setup.
Model providers update their APIs, channel platforms change their integration requirements, and usage patterns shift as teams find new applications — all of which need occasional attention. We offer an ongoing operations arrangement covering monitoring, backups and updates, though clients with internal technical capacity sometimes take over day-to-day operation after handover.
How we work
Define permitted jobs and risk boundaries
Deploy an isolated proof of value
Connect approved tools and channels
Harden, document and operate
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