Built the platform that powers an enterprise AI behavioural nudge product serving large enterprise customers
An engineering partnership on the platform build of an AI-driven behavioural nudge product for enterprise teams, with an AI engineer embedded alongside platform engineering on a Laravel backend.
Key Takeaways
Worxogo builds AI-driven behavioural nudge software for enterprise teams, helping organisations shift employee behaviour through targeted, contextual prompts rather than traditional training programs. Unico Connect built the platform on a Laravel backend with an AI engineer embedded alongside the core platform engineers.
The engagement covered the platform architecture, the AI behavioural model and the supporting product surfaces, and gave Worxogo the foundation they have since used to onboard large enterprise customers across their target market.
The Challenge
Worxogo was building in a category where the enterprise buyer is sophisticated, the customer expectation on platform quality is high, and the AI capability has to deliver a measurable behavioural effect at the individual employee level to be worth the enterprise contract. The product proposition is genuinely valuable: enterprise teams have struggled for decades to shift employee behaviour through traditional training, and a platform that uses contextual nudges grounded in behavioural science can move the needle in ways training and hope cannot. But building that platform is technically and conceptually demanding.
The challenge Worxogo brought to Unico Connect was that they needed an engineering partner who could carry both the platform engineering and the AI behavioural model in parallel, rather than treating AI as a separate later phase. The product depends on the AI behavioural model being right: which nudge to send, when to send it, in what context, to which employee. The platform depends on being able to support that model at enterprise scale, with the data infrastructure, the integration profile and the security posture that large enterprise customers require. Building these in sequence rather than in parallel would have meant the AI model was designed against assumptions about the platform that might not hold once the platform was actually built.
Worxogo’s target market was large enterprise customers, which raised the bar in specific ways. Enterprise buyers evaluate the platform’s architecture, security, scalability and integration capability before they evaluate the product itself. A platform that does not pass enterprise procurement is not going to get the chance to demonstrate the AI capability. The team needed a platform that was credible to an enterprise IT or HR leader at the technical level, while also delivering the AI capability that justified the contract. The product itself was also a strategic foundation, not just a one-time build, scoped for a platform that could be extended over time as the team grew, the model matured and new enterprise customers brought new use cases.
The AI behavioural model had to be designed against the platform it would actually run on, and the platform had to be credible to enterprise procurement before it could ever demonstrate the AI capability. Both had to be built together, and the architecture had to leave room for everything the product would become.
Our Approach
We engaged with the Worxogo team on the platform build with an unusual structure: an AI engineer embedded in the platform team alongside the core platform engineers. The decision to embed AI rather than treat it as a separate workstream was central to how the engagement worked. When AI is built against assumptions about the platform, and the platform is built against assumptions about the AI, the two tend to diverge in ways that are expensive to reconcile later. Embedding AI in the platform team meant the behavioural model and the data model evolved together, with both engineers in the same conversation about the architectural decisions that affected both.
Key decisions:
AI embedded, not a separate phase
The embedded engineer worked on the behavioural nudge model alongside the platform build rather than after it. The model and the data architecture were designed together, so the model could rely on the data infrastructure being shaped to support it, and the platform could be confident the integration layer would actually carry the AI workload at scale.
Laravel for the platform foundation
We chose Laravel because it is mature, well suited to the data model the behavioural platform required, and gave the team the development velocity to build the foundation without the cost of more exotic stack choices. For a platform that needed to demonstrate enterprise credibility quickly, the conservative stack choice was the right one.
Architected to extend, not to be feature-complete
We focused architectural attention on the parts of the platform that would actually carry the AI workload (the data model, the event capture infrastructure, the model integration layer) and structured the platform so Worxogo could layer in new capabilities, new enterprise integrations and new behavioural model iterations as the product matured.
The solution we built
The platform consists of the Laravel backend, the AI behavioural model integrated into the platform architecture, and the product surfaces required to deliver nudges to end users and reporting to enterprise buyers. From an end user’s view, the platform delivers contextual prompts in the moment they will actually shift behaviour, in the channels and contexts where employees already operate rather than in a separate app.
Laravel backend and data model
Carries the core data model, event capture infrastructure and the API surfaces the front-end and analytics views run against. The data model was designed specifically for the behavioural workload, capturing the right signals about each employee’s role, context and behavioural patterns to feed the model.
AI behavioural nudge model
The product’s intellectual property and Worxogo’s proprietary asset. Our role was to embed an engineer who worked alongside Worxogo’s team on the model’s development, the data infrastructure that supports it, and the integration with the platform architecture that lets it run at enterprise scale.
Model integration layer
The layer between the platform and the AI model was designed for reliability and to stay responsive, because the nudge delivery experience falls apart if the model is slow or unreliable. The integration was shaped to carry the AI workload at scale rather than at demo quality.
Contextual nudge delivery
The product surfaces deliver the contextual prompts that the behavioural model decides to send, to whom and when, landing in the channels and contexts where employees already work so the nudge arrives in the moment it can shift behaviour.
Enterprise analytics view
Reports on the behavioural impact of the nudges at the organisational level. Enterprise customers paying for the platform need to see the measurable behavioural shift that justifies the contract, so the analytics surface was designed for the questions an enterprise HR or operations leader actually asks.
Enterprise security and privacy posture
The platform maintains the security and privacy posture that large enterprise customers require, so it can pass enterprise procurement before it demonstrates the AI capability.
Tech stack
Outcomes & Impact
Platform foundation
A platform built to carry enterprise workloads
The engagement delivered the platform foundation Worxogo has since used to scale across enterprise customers in their target market, reaching large enterprise organisations using the platform to shift employee behaviour at scale.
AI capability
The behavioural model and platform stayed aligned
Embedding an AI engineer in the platform team kept the behavioural model and the platform architecture aligned rather than letting them diverge. For a product where the AI is the core IP rather than a feature, this alignment is what made the AI run at the scale and reliability enterprise customers expect.
Extensibility
Architecture that extends cleanly to new customers
The architecture extends to new behavioural models, new integration profiles and new enterprise customer requirements, which positioned Worxogo to onboard new customers as the product matured. The platform is now the operating foundation the business runs on, not a one-time deliverable.
What Our Clients Say
Ravi Bhamidipati
President, Worxogo Solutions
They were versatile and could easily translate the business requirements.
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