How a Cloud Cost Optimisation SaaS Uses OpenClaw to Automate Internal Operations
Follow Rabbit helps enterprise teams cut Google Cloud waste at scale. Now, in an early-stage engagement with Blue Canvas, they're using OpenClaw to tighten their own internal operations with persistent memory, custom skills, workflow automation, and multi-agent orchestration.
Engagement Snapshot
Why This Client Matters
Follow Rabbit at a Glance
- • Google Cloud cost optimisation platform focused on BigQuery and wider GCP efficiency
- • Automates cost-focused code reviews and optimisation PRs
- • Covers BigQuery, GKE, Cloud Storage, Compute Engine, Cloud Run, and anomaly detection
- • Operates as Follow Rabbit PTE Ltd and is a Google Cloud Partner
- • Working with major brands, including Lufthansa Group, Rakuten, Trivago, and Nordstrom
Customer Logo Wall
Text wordmarks used here, no external assets hotlinkedThe Challenge
Even Strong SaaS Teams Hit Internal Ops Friction
- • Internal knowledge gets scattered across chats, docs, and ad hoc processes
- • Customer onboarding steps can become inconsistent as the team grows
- • Reporting and internal updates eat time that should go into product and delivery
- • Competitive monitoring and content tasks slip when engineering work takes priority
- • Smart teams still need a system for repeatable execution, not just clever people
That's the context for this project. Follow Rabbit already helps enterprises optimise cloud spend. The OpenClaw engagement is about applying the same operational discipline internally, so routine work gets captured, delegated, and run consistently.
The OpenClaw Solution
Persistent Memory
Keep client context, internal process notes, onboarding checklists, and workflow state in one place, so repeat tasks do not restart from zero each time.
Custom Skills
Build targeted OpenClaw skills for Follow Rabbit's real workflows rather than forcing everything into generic chat prompts or brittle automation tools.
Automated Workflows
Turn repetitive internal work into scheduled or trigger-based routines, including reporting, task prep, summaries, and handoffs.
Multi-Agent Orchestration
Split research, writing, monitoring, and operational follow-up across specialist subagents instead of jamming every task into one general assistant.
Operational Visibility
Create clearer internal reporting loops so key updates, tasks, and recurring priorities do not stay trapped in someone's head.
Flexible Rollout
Start with a focused setup, then expand into higher-value automations once the operating rhythm and team fit are proven.
Implementation Breakdown
Phase 1: Initial OpenClaw Setup
Core Foundations
- • OpenClaw environment configured for internal operations use
- • Memory structure shaped around recurring team workflows
- • Initial skills designed around Follow Rabbit's operating model
- • Business context and repeatable tasks documented for reuse
- • Basic orchestration patterns set up for specialist subagents
Current Focus
- • Reduce admin overhead around onboarding and internal follow-up
- • Improve consistency across recurring operational tasks
- • Capture team knowledge in a form agents can use repeatedly
- • Build a clean base for further workflow automation
- • Keep rollout practical rather than overengineering early
Status: Early-stage engagement with the initial OpenClaw setup and workflow design in progress.
Phase 2: Workflow Rollout
Likely Automations
- • Automated customer onboarding checklists and handoffs
- • Internal reporting summaries and action tracking
- • Competitive monitoring with structured updates
- • Content assistance for internal and external communications
- • Follow-up reminders and recurring operational prompts
What OpenClaw Adds
- • Statefulness across tasks instead of isolated one-off chats
- • Skills built for the company's actual workflows
- • Agents that can hand work between each other cleanly
- • Reusable prompts, memory, and guardrails for repeat work
- • A practical path from ad hoc tasks to systemised operations
Approach: Roll out high-frequency use cases first, then widen the footprint once the first internal loops are stable.
Phase 3: Operational Expansion
Potential Next Steps
- • Broader multi-agent handoffs across operations and content
- • Better reporting visibility for leadership updates
- • More proactive monitoring of market and competitor movement
- • Tighter internal documentation and memory hygiene
- • Expanded workflow coverage as the team proves ROI internally
Expected Payoff
- • Less manual coordination work
- • Faster onboarding and task execution
- • More consistency across recurring processes
- • Better visibility on what needs action next
- • More time kept for product and customer value work
Important: These are the operational gains the engagement is designed to deliver, not claimed outcomes from a finished deployment.
Operational Use Cases We're Building For
Internal Operations
OpenClaw Architecture Fit
Why the Fit Makes Sense
Serious Product Team
- • Strong technical buyer profile
- • Clear operational leverage points
- • Real need for repeatable internal systems
- • High-value time best spent on product and clients
Good OpenClaw Use Case
- • Recurring workflows with context dependency
- • Multi-step internal processes
- • Clear benefit from memory and orchestration
- • Scope to add more skills over time
Commercial Model
- • £1,000 initial setup
- • £500 monthly retainer
- • Practical phased rollout
- • Room for expansion as value becomes visible
Current Project Position
Blue Canvas is working with Rory and Aco on an early OpenClaw rollout. This page reflects the direction, design, and operating model of the engagement, not a finished transformation story.
Need OpenClaw for Internal Ops?
If your team already has strong products and solid people but too much manual internal work, OpenClaw can give you a proper operating layer, not just another chatbot.
What Blue Canvas Can Build:
- • OpenClaw setup and configuration for your business
- • Custom skills built around your actual workflows
- • Persistent memory and operating rules
- • Multi-agent task orchestration
- • Ongoing monthly support and iteration
Early-stage work, practical rollout, no made-up claims.