Beyond chatbots. Beyond automation.
An agentic AI system perceives its environment, plans across multiple steps, calls the right tools, and executes a goal with minimal human supervision. Where generative AI responds to a prompt, an agent owns the outcome.
The Genaxis Delivery Framework
A repeatable lifecycle that transforms your business
Four stages. One commitment to measurable outcomes. Each stage produces deliverables you review and sign off before we move on.
Stage 01 · Analyse and Advise
Uncover the highest-value AI opportunities
We assess your current processes, workflows, systems, and data to surface viable agentic AI use cases. Each one is sized against ROI and feasibility before we recommend a build path.
Current-state assessment and workflow mapping
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Use-case prioritisation with ROI modelling
AI opportunity roadmap and transformation blueprint
Stage 02 · Design and Build
Architect, integrate, and prototype with rigour
We design solution architecture and agent workflows, integrate with your enterprise systems and data, then build and test in a staging environment to ensure scalability, security, and value before go-live.
Solution architecture and agent workflow design
Integration with enterprise systems and data
Build, prototype, and test-run in staging
Stage 03 · Test and Train
Tune for reliability and prepare your team
We rigorously test the AI agents for performance and reliability, tune them for your operating environment, and train your internal teams to work alongside AI with confidence and clarity.
Performance and reliability testing
Agent tuning and optimisation
User training, change management, deployment readiness sign-off
Stage 04 · Review and Support
Continuous improvement and long-term governance
After deployment, we continuously review performance, provide ongoing support, and keep you informed of the latest AI advancements to drive long-term impact and compounding value.
Post-deployment monitoring and performance tracking
Continuous improvement and optimisation
Ongoing support, governance, and technology updates
Delivering Measurable Business Outcomes
Built for the boardroom
Every engagement is anchored to four outcome pillars. We size each agent to a target metric and prove movement against it.
FAQ
Everything you need to know about agentic AI deployment, governance, and engagement timelines.
What is agentic AI and how is it different from generative AI?
Generative AI creates content in response to a prompt. Agentic AI sits above it. An agent uses generative AI as one of its capabilities but wraps it in a goal-oriented decision layer that can plan, choose tools, integrate with your systems, and adapt in real time. Where workflow automation breaks when a process changes, agentic AI adjusts.
How long does it take to deploy an agentic AI solution?
A Discovery Sprint runs two to four weeks. A Pilot Build for a single high-value use case typically takes six to twelve weeks from kick-off to production. Multi-agent rollouts are scoped per programme and usually staged across quarters.
Is agentic AI safe for regulated industries in Singapore?
Yes, when designed correctly. Our delivery framework aligns with the IMDA Model AI Governance Framework for Agentic AI. We bound risk upfront through use-case selection, embed human approval at significant checkpoints, apply technical guardrails throughout the lifecycle, and provide audit-ready decision logs.
Can the agents integrate with our existing enterprise systems?
Yes. Our agents call APIs, query databases, read documents, post to Slack, raise tickets, and update CRMs including HubSpot, Salesforce, and custom enterprise setups. Integration is designed during Stage 02 of our delivery framework.
Are Genaxis AI engagements eligible for Singapore grants?
Selected solutions, including our Pebble AI offering, are designed to align with Singapore SME and AI adoption grant frameworks. We assess eligibility during the Discovery Sprint and advise on the appropriate grant pathway.
