We design and build practical AI-enabled workflows, automation and digital operating models — and integrate them into how organisations already work.
Most organisations do not need a new AI strategy. They need a small number of things to actually work: a process that runs without being chased, a report that assembles itself, an enquiry answered accurately at two in the morning, internal knowledge that can be found. We work at that level.
Systems that carry out multi-step tasks with tool access, defined limits and a human checkpoint wherever one is warranted.
Processes that are repetitive, rule-bound or spread across too many tools, rebuilt so they run on their own and surface only the exceptions.
Internal tools and integrations built with AI in the development loop, which shortens the distance between describing something and using it.
Production, review, approval, publication and measurement run as one pipeline, with editorial judgement retained by people.
Monitoring and synthesis of information across defined sources, with every claim kept traceable back to where it came from.
Search, retrieval and structured records built around an organisation's own material, with interfaces its people will actually open.
The work is practical rather than advisory. We take a defined operational problem, build a working system around it using current AI tooling and conventional software, test it against real conditions, and hand it over with the supervision it needs to be trusted.
We look at how the work is done now: the tools, the handovers, the exceptions. Nothing is proposed before that.
A working version on a narrow slice of the problem, early enough to be judged rather than imagined.
Measures agreed before the build, so the result can be assessed honestly — including when it does not hold up.
Documentation, escalation paths and the points where a person stays in the loop. Automation nobody can inspect is not finished automation.
Areas in which we have built and run this kind of work. Client names and performance figures are withheld unless we hold written permission to publish them.
Content operations that need to run continuously: commissioning, production, review, publication and archiving, held together as one system rather than a sequence of manual handovers.
Teams receiving more messages than they can triage, where the useful work is accurate routing and consistent first replies, with people retained for anything unusual.
Reporting that previously required manual assembly from several systems, and monitoring across a defined set of external sources.
We work across commercial and open-weight models — among them GPT, Claude, Qwen, GLM, Kimi and MiniMax — and self-host where that is the better answer. None of this is a partnership or a reseller arrangement; it is a working requirement.
Which model a workflow uses is an engineering decision, taken on task performance, cost, latency, reliability, privacy and data handling, governance and operational fit. Those factors change often, so systems are built for the model to be swapped without rebuilding the workflow around it.
Mention of technologies does not imply partnership, endorsement or reseller status.
Part of our time goes into work with no client attached: testing agent architectures, orchestration patterns and supervision models before they go anywhere near an organisation's operations.
Some of that testing runs on open agent infrastructure such as OpenClaw, used as a reference implementation for multi-agent orchestration and tool-integrated workflows. It is an environment we experiment in, not a dependency — delivery work stays platform-agnostic.
Most work begins with a short scoping engagement: we look at one process in detail and come back with what can be built, what it would take, and whether it is worth doing. Build engagements are scoped and quoted individually. Ongoing arrangements are structured according to operational need, complexity and scope.
The Inove Group is exploring how its applied AI and automation capability could contribute to a future Scotland-based research and innovation programme, subject to academic, institutional and funding partnerships.
Testing what happens when Scottish SMEs move from using AI tools to redesigning real operations around AI.
At the centre of that exploration is a proposed anchor initiative — the Scotland AI-Enabled SMEs Lab — a 24-month applied research and demonstrator programme examining how Scottish SMEs can move from individual, tool-level AI use towards deeper operational adoption, and what that does to productivity, capability and work itself.
The Inove Group is a Brazilian company, registered in Rio de Janeiro as Inove Soluções em Comunicação Ltda. We began in communication and digital operations and moved into automation and applied AI as the work required it.
That order matters. Our applied AI work has developed through operating under real conditions — deadlines, volume, exceptions and accountability — giving us a practical perspective that complements formal research and evaluation.
No automated replies on this form. We respond within one business day.
The first conversation is about a specific operation, not a package. If a short scoping engagement makes sense, we will say what it would cover.
Some problems do not need AI, and some need a supplier closer to your systems. We will tell you which.