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Strategy | Delivery | AI

Turning ideas into production systems

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Opportunity

The pressure to deliver has not changed. AI changes how you do it.

The delivery challenge is the same as it has always been. Build solutions customers value, ensure they are commercially viable and deliver efficiently. AI is now seen as a powerful tool to help achieve those goals. Boards are pushing for it and expectations are high, but it is often treated as a silver bullet. In practice, the hard questions remain. Where does AI actually fit. Which problems are worth solving with it. How do you deliver real value without increasing risk or slowing teams down. That is where we come in.

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icon Rising Expectations Without Clear Answers

The pressure to deliver outcomes has not eased. Organisations are still expected to ship solutions customers value, that make commercial sense and that are delivered efficiently. What has changed is the assumption that AI should materially improve how this happens. Boards expect AI investment, CFOs want measurable return and CIOs worry about falling behind. At the same time, leaders remain cautious about risk, security, quality and initiatives that consume time without delivering value.

icon High Activity, Unclear Impact

Most organisations now sit in an awkward middle ground. AI tools are switched on and teams are actively experimenting. There is plenty of movement but limited momentum. Results are inconsistent, difficult to measure and hard to scale safely. Without a clear path from experiment to production, effort accumulates without compounding into real capability or outcomes.

icon Lack of Alignment on Where AI Fits

There is often no shared understanding of which problems are worth solving with AI, where it fits into delivery or how success should be defined. Without this alignment, work fragments into pilots and proofs of concept that never make it into production. Teams are busy but direction is diffuse and progress is hard to sustain.

icon Delivery Strain and Capability Gaps

Delivery teams feel the impact most directly. Product ideas stall as teams are stretched across too many initiatives. Engineering capability grows unevenly as new expectations are added without clear patterns or guidance. The issue is rarely ambition or access to technology. It is the absence of clear decisions, focused delivery and repeatable ways of working. That is the gap we help organisations close.

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How Revity helps

We help organisations move from idea to production, applying AI only where it genuinely improves speed, quality or scale. We help set clear direction and outcomes, support leaders to build a practical understanding of AI and run focused experiments with real users to validate value. We deliver solutions alongside your teams, training developers as part of the work so capability grows while outcomes are achieved. Delivery scales through small, accountable teams that build, learn and improve iteratively.

Our Expertise

icon Leadership Clarity and Direction

We help leaders move from pressure and uncertainty to clear decisions. This pillar focuses on building a practical understanding of AI across the executive and senior delivery layer, aligning on where AI creates real value and defining what success looks like. The outcome is shared language, aligned priorities and a realistic roadmap that balances ambition, risk and delivery constraints so teams can move forward with confidence.

icon Focused Experimentation with Purpose

We help turn strategy into evidence. Instead of scattered pilots, we run targeted experiments tied to specific commercial hypotheses. Teams co-build proofs of concept and prototypes that test feasibility, impact and adoption in weeks, not months. The result is clear data on what to scale, what to iterate and what to stop, alongside a credible business case for production.

icon Scaled Delivery and Capability Uplift

Move from experiments to real delivery. We embed experienced engineers into your teams to ship production features while uplifting how your organisation designs, builds and delivers software with AI. This creates consistent patterns, faster cycle times and higher quality outcomes. Capability is transferred as work is delivered so teams become confident, self-sustaining and measurably more productive.

icon Proven Experience in High-Risk Transformation

Our approach is grounded in having done this before at scale. We bring first-hand experience from complex migrations to Kraken and from building and operating a digital bank at Afterpay. These environments demanded reliability, regulatory awareness, strong engineering discipline and zero tolerance for theory without execution. This experience shapes how we design AI adoption that works in real production systems, not just in controlled experiments.

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Our story

Built on 25 years of experience

Revity was founded by technology and business leaders with over 25 years of experience delivering complex programs inside large organisations. We have led work across contact centres, digital platforms and large-scale technology transformations where outcomes mattered, timelines were real and failure was not an option.

We started Revity because we saw the same problems repeating. Teams under pressure, big expectations and too much distance between strategy and delivery. What made the difference in our own careers was not theory or tooling. It was strong engineering teams, clear decisions and a focus on getting things shipped.

Today, we bring those lessons to organisations facing similar challenges. We work alongside teams, help them build capability and deliver meaningful change, drawing on hard-earned experience from environments where scale, risk and delivery discipline were non-negotiable.

Our Experiences

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Energy Retailer Transformation

Business and Technology transformation of a large Energy Retailer. Over 4M customers migrated. The full technology stack was migrated from legacy platforms to a a modern technology stack and software delivery processes. All business functions were also rebuilt from the ground up during this program. Technology stack based on AWS, Twilio, Databricks. Manual task automation via RPA.

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Intelligent voice platform for Contact Centre

Migrated contact centre from a legacy platform to a modern cloud-based tech stack. Implementation of an intelligent IVR to allow customers to self-serve, deflect calls to digital channels (website, native apps and LiveChat) and intelligent call routing. Workspace/soft phone for customer care with customer insights and the "next best action" using AI

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AI-based Voice bot

Voice and chat bot for handling customer calls. Performed voice-to-text, advanced prompt engineering and text-to-voice. Tested really well with the customers with both positive and negative test scenarios. Responsible and ethical AI considerations built into the platform.

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Digital Transformation

In a competitive market, service and customer experience differentiate businesses. Enhanced digital platforms over 18 months, emphasising a customer-first approach and a flexible cloud-native architecture using AWS. This transformation led to increased deployment efficiency, allowing up to 40 releases daily, limited only by customer and organisational readiness.

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We'd love to chat to you about our experiences!

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