Start by identifying high-value use cases where AI can reduce manual effort or improve decision-making — such as automated reporting, predictive maintenance, or customer analytics. Next, audit your data readiness: clean, centralised data is the foundation for any AI initiative. Then partner with an experienced applied AI consultancy like Hawkfry to build a phased roadmap, run a proof-of-concept, and scale what works. We guide clients from strategy through to production deployment and team handover.

An applied AI consultant bridges the gap between cutting-edge AI research and real business outcomes. They assess your data maturity, identify where AI will deliver the highest ROI, design the technical architecture, and manage implementation end-to-end. Unlike pure-play data scientists, applied AI consultants focus on production-grade solutions that integrate into existing workflows and deliver measurable value.

Generative AI (like ChatGPT or image generators) creates new content — text, images, code. Applied AI is the broader discipline of using any AI technique — machine learning, NLP, computer vision, or generative models — to solve a specific business problem. Applied AI focuses on practical outcomes: better forecasts, smarter automation, and faster decisions. Generative AI is one tool in the applied AI toolkit.

Costs vary widely depending on scope. A focused proof-of-concept might start from £20k–£50k over 4–8 weeks. A full-scale AI platform build including data engineering, model development, and production deployment typically ranges from £100k–£500k+. Hawkfry offers flexible engagement models — from fixed-price sprints to managed services subscriptions — so you can start small and scale investment as you see results.

Applied AI innovation refers to the practical implementation of artificial intelligence technologies to solve real-world business problems. It goes beyond theoretical research to deliver production-ready solutions — such as intelligent data pipelines, predictive analytics dashboards, and automated decision-support systems. For asset-owning businesses, this means turning operational data into strategic advantage through custom AI models, modern data platforms, and AI-augmented workflows.

We provide a suite of offerings spanning professional services, managed services, and AI & data applications. Our professional services include AI strategy, data engineering, and analytics consulting. Managed services cover ongoing platform operation, monitoring, and optimisation. Our AI & data applications are purpose-built tools for asset intelligence, decision-making, and team capability building. See our solutions page for more information.

Managed services and AI & data applications are priced on a subscription basis, giving you predictable monthly costs and continuous improvement. Professional services are priced on a project or time-and-materials basis depending on scope. We always start with a scoping workshop to ensure pricing aligns with expected outcomes.

Yes, we currently operate across EMEA, APAC, and LATAM. Our consortium model means we can assemble the right team for any timezone and regulatory environment, whether you need on-site presence or fully remote delivery.

We specialize in working with asset owners and operators (property, infrastructure, utilities), private equity firms, digital natives, and AI & data startups. Our deep experience in asset-heavy industries means we understand the unique data challenges — fragmented systems, legacy platforms, and complex reporting requirements — that these organisations face.

We implement industry-standard security protocols and compliance measures to protect all client data. Our systems undergo regular security audits, and we maintain strict access controls. We typically build on Google Cloud or AWS, leveraging their compliance certifications including SOC 2, ISO 27001, and GDPR.

Our focus on asset-owning businesses, our specialised consortium of experts, and our commitment to practical outcomes distinguish us from other providers. We emphasise production-grade, results-driven solutions over theoretical AI applications. Our Build-Operate-Transition model means you get a working solution fast, with a clear path to in-house ownership.

Implementation timelines vary based on project scope and complexity. A proof-of-concept typically takes 4–8 weeks. Simple production implementations may take 6–12 weeks, while more complex enterprise-scale solutions could take several months. We provide detailed timelines during the consultation phase and use a phased approach so you start seeing value early.