AI & intelligent services
From business problems to AI that works in the real world.
We design, build and operate AI across enterprise workflows and industrial operations — from focused PoCs to secure, production-scale systems.
Enterprise AI · Industrial AI · Agentic automation · Intelligent applications
The seven services
AI & intelligent services, organised.
Every engagement starts in one of these seven. Most end up drawing on three or four, which is why the same team runs all of them.
01 / Advise
AI Strategy & Advisory
Work out which AI opportunities are real, what they are worth and the order to do them in — before anyone builds anything.
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02 / Transform
AI Transformation Services
Rebuild an end-to-end process around AI — the workflow, the systems, the roles and the measures — so the gain is structural.
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03 / Automate
Agentic AI & Automation
Agents that carry multi-step work across your systems under guardrails, evaluation and human approval.
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04 / Ground
Generative AI Solutions
Copilots, assistants and knowledge systems grounded in the material you approve — with citations, permissions and review.
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05 / Build
Enterprise AI Engineering
Custom AI applications, enterprise integration and deployment into the environment your security team already accepts.
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06 / Govern
AI Governance & Responsible AI
The framework, guardrails, evaluation and audit trail that let a risk committee approve an AI system.
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07 / Operate
AI Managed Services
We run what we built — and what others built — with monitoring, evaluation, upkeep, cost management and real support.
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SEVEN SERVICES. ONE TEAM. ONE PLATFORM.
01 — Two AI capability tracks
AI for the enterprise. AI for the physical world.
Whether the challenge sits inside a business process or a physical operation, we start with the problem and apply the right combination of AI, data, software and automation.
Enterprise AI
Make knowledge, decisions and workflows intelligent.
AI that works across your people, data, documents and enterprise systems — helping teams find answers, decide better and automate complex work.
Capabilities
Typical problems we solve
“Our teams spend too much time finding information.”
“This process crosses multiple systems and still depends on email and spreadsheets.”
“We have the data, but getting useful insight takes too long.”
“We want AI agents, but they must work securely with our enterprise systems.”
Industrial AI
Bring intelligence closer to your operations.
AI applied to physical environments, operational data, assets and field activity — so organisations see what is happening, catch what needs attention and decide faster.
Capabilities
Typical problems we solve
“Can AI identify defects automatically?”
“Can we detect abnormal equipment behaviour before it becomes a problem?”
“Can field teams get instant answers from manuals, history and operational data?”
“Can we combine visual, sensor and enterprise data to understand operations?”
02 — Where they come together
From operational signal to enterprise action.
Physical world
Cameras · assets · equipment · sensors · field operations
Industrial AI
Detect · predict · understand
ZeaIQ — AI Foundry
Reason · orchestrate · govern
Enterprise AI
Decide · automate · collaborate
Enterprise systems
ERP · EAM · CRM · service management · data platforms
Business outcome
Action · alert · approval · work order · dashboard · decision
One event, end to end
- Camera detects an equipment anomaly
- AI analyses the event
- An agent retrieves maintenance history
- Checks the relevant procedures
- Assesses severity
- Recommends an action
- A human approves where required
- The enterprise system raises the workflow
03 — Start with one problem
Prove the value before you scale it.
Bring us a meaningful business problem and we will work out whether AI can solve it, what data it needs and how to prove the value quickly.
Discover
Define the problem, the available data and the success criteria.
Prove
Build a focused AI PoC against measurable outcomes.
Pilot
Connect real systems, users, security and governance.
Scale
Move into production and keep improving it.
04 — How we deliver
Three stages, one accountable team.
01 / Build
Design & build
We design AI around a real business or operational problem — not around a technology demo.
- —Use-case discovery & solution architecture
- —Agents, copilots & intelligent applications
- —GenAI, ML, vision & multimodal AI
- —Evaluation, security & guardrails
02 / Integrate
Connect to your environment
AI creates value when it works with the data, systems and operational environment you already have.
- —Enterprise applications, APIs & databases
- —Documents, knowledge & operational data
- —Cameras, sensors & IoT platforms where applicable
- —Cloud, on-premises, hybrid & sovereign
03 / Operate
Governed & running at scale
We don't stop at go-live. We govern, monitor, support and continuously improve your AI as business needs, data and models evolve.
- —Responsible AI & human oversight
- —Security, RBAC, guardrails & auditability
- —Agent / model evaluation & observability
- —MLOps, LLMOps & continuous optimisation
05 — Technology independent
The right AI for the problem. Not the other way around.
Models
LLMs · SLMs · ML · vision · multimodal
Data
Structured · documents · images · video · operational · sensor
Intelligence
RAG · agents · prediction · detection · analytics
Integration
APIs · enterprise applications · databases · IoT platforms
Deployment
Cloud · private cloud · on-premises · hybrid · sovereign
Governance
Identity · guardrails · evaluation · human oversight · audit
06 — Powered by our AI Foundry
We don't start every AI project from zero.
Our AI Foundry — built on ZeaIQ and supported by the Zealogics products — gives our teams reusable foundations for data connectivity, agent orchestration, security, governance, dashboards and deployment. That is why we can move from problem to PoC to production quickly and still fit your environment.
07 — Problem first. Technology second.
What problem are you trying to solve?
“Our people cannot find the information they need.”
Knowledge AI
“Too much work is still manual.”
Agentic automation
“We have data but not timely decisions.”
Decision intelligence
“We manually inspect images or video.”
Computer vision
“We want to predict equipment problems earlier.”
Industrial AI
“Our field teams need better decision support.”
Operational AI
“We have an AI idea but do not know whether it will work.”
AI PoC
08 — Work in practice
From business problem to working system.
Client names stay confidential — the work is described, the logos are not.
Procurement intelligence
Problem
Fragmented supplier, market and procurement information made evaluation slow and inconsistent.
Built
Multi-agent procurement intelligence solution
Connected
Enterprise procurement data plus external intelligence sources
Outcome
Evaluation consolidated into one governed workflow
Engineering knowledge assistant
Problem
Procedures, drawings and historical tickets were spread across systems and hard to search.
Built
Grounded knowledge assistant with retrieval over controlled sources
Connected
Document repositories, ticketing and engineering records
Outcome
Answers traceable to source documents
Operational reporting automation
Problem
Managers rebuilt the same operational reports manually every cycle.
Built
Natural-language data exploration with generated dashboards
Connected
Operational databases and reporting layer
Outcome
Reporting shifted from manual assembly to review
Have a problem worth solving?
Bring it to us.
Whether it is an enterprise workflow, an operational challenge or simply an AI idea you want to validate, we will help work out the most practical way forward.