Industry solutions · Engineering

Engineering delivery that turns AI into production reality

Modernise your stack to support AI workloads without compromising reliability or cost
Ship AI features faster with strong MLOps, test automation, and observability
Blend fixed-scope projects with flexible engineering pods to match changing demand

When our engineering support is a strong fit

Signals that your organisation is ready to benefit from specialised engineering support for AI and data-intensive systems.

You have AI PoCs that can’t reliably reach production

Experiments show promise, but your current architecture, tooling, or processes make it hard to deploy and operate them safely.

Your platform isn’t ready for AI-scale workloads

Legacy systems, tight coupling, or on-prem constraints are limiting your ability to run data- and compute-heavy AI services.

You need to blend roadmap delivery with AI bets

Your engineering teams are fully committed to core product work, leaving little capacity to explore and ship AI capabilities.

You want stronger MLOps and observability

You lack consistent pipelines, monitoring, and governance for models and AI services across teams and environments.

You’re scaling engineering and need proven patterns

You’re growing teams and services and want repeatable architectures, tooling, and practices that support AI from the start.

You value a pragmatic, non-hype approach to AI

You’re looking for partners who will challenge assumptions, quantify value, and focus on durable engineering outcomes.

Example engagements

How engineering teams partner with us

Technology

AI-ready platform foundation

Re-architected a legacy monolith into a modular, cloud-native platform with data and feature stores that support multiple AI use cases. Enabled faster experimentation and reduced deployment risk for new AI services.

Financial services

Productionising successful AI PoCs

Took several ML PoCs from notebooks into hardened, monitored microservices with CI/CD, canary releases, and rollback strategies. Cut time-to-production from months to weeks while meeting compliance requirements.

E-commerce

Search and recommendation modernisation

Replaced brittle rules-based search with vector search and personalised recommendations, integrating with existing catalog and pricing systems. Improved conversion while keeping latency and infrastructure costs under control.

Logistics

Operational analytics and observability

Implemented unified logging, metrics, and tracing across services powering AI-driven routing and forecasting. Gave engineering and operations teams shared visibility into model performance and system health.

SaaS

Augmented engineering pods for AI features

Stood up cross-functional pods combining our engineers with the client’s product and data teams to deliver AI-driven features. Increased delivery capacity without disrupting existing roadmaps.

Our approach

Engineering-first delivery for AI systems that last

We combine pragmatic architecture, strong engineering practices, and clear governance so your AI initiatives move from experimentation to dependable production services.

Assess architecture, data, and delivery constraints

We review your current stack, delivery model, and AI ambitions to identify what’s blocking safe, repeatable delivery of AI capabilities.

Shape a practical AI and platform roadmap

Together we prioritise initiatives by impact and feasibility, defining where fixed-scope projects and flexible pods make the most sense.

Design target architecture and operating model

We define reference architectures, environments, and MLOps practices so AI services can be developed, tested, and deployed like any other critical system.

Build, integrate, and harden AI services

Our teams work alongside yours to implement services, APIs, pipelines, and automation, with security, performance, and observability built in from day one.

Operationalise, measure, and iterate

We help you embed runbooks, SLOs, monitoring, and feedback loops so engineering, product, and data teams can safely evolve AI features over time.

Business Outcomes

Clear, prioritised roadmap for AI delivery aligned to engineering capacity and risk
Modern platform foundations that support multiple AI use cases, not just one-off PoCs
Faster cycle times from experiment to production with robust CI/CD and MLOps
Reduced operational risk through observability, testing, and clear ownership
Stronger collaboration between engineering, data, and product teams

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