AI Enablement at Ryewalk

AI enablement at Ryewalk is a structured, multi-phase program designed to develop deep, applied AI capability across our teams. This program reflects our commitment to systematically embedding AI into our operations, tools, and client delivery.

Phase 1: Building the Foundation

We started with a four-week learning curriculum covering the core concepts every team member needs to work effectively with AI. This spans AI and ML fundamentals, natural language processing, generative AI, prompt engineering, retrieval-augmented generation, and the Model Context Protocol (MCP). The content is curated and progressive where each week builds on the last, drawing from open courses as well as proprietary learning content, alongside hands-on walkthroughs. The goal was to ensure everyone shares a common understanding of how these systems work under the hood.

Phase 2: Tool Enablement

With the foundations in place, we're now focused on empowering teams with the right tools to deliver at their best. This phase is about giving people agency: letting teams identify what works for them and ensuring they're set up to accelerate with top-quality.

The toolset spans the full development and automation stack: Claude Code, Cursor, Antigravity, n8n, Codex, CodeRabbit, and Agentforce, among others. Alongside tool adoption, we're building a library of reusable skills and workflows so that good patterns are shared across the organisation rather than siloed within individual teams.

Phase 3: Productising Agents

The next phase takes this further, moving from enablement to execution. We'll be turning the agents and workflows we develop into reliable, production-grade products that deliver measurable value to the business.

What does this mean for our clients?

Everything we build internally, we test on ourselves first. Our teams use the same tools, workflows, and agents before we ever recommend them to a client. This means our advice is grounded in real experience, not theory.

Tried and tested before it's implemented. Internal experimentation is our quality gate. If a tool or approach doesn't hold up in our own environment, it doesn't make it to a client engagement. When we do bring something forward, it's because we've already worked through the rough edges.

Because we're continuously evaluating and adopting new tools, our clients benefit from a partner who's already across what's emerging, not catching up to it.

AI is moving fast, and staying current requires more than a one-off implementation. We work alongside our clients on an ongoing basis to ensure their AI capability keeps pace with the landscape, adapting as models, tools, and best practices evolve.

Through this, we ensure our AI solutions are contextual in nature and truly meaningful in impact.