Artificial Intelligence · Build
We build agent-based automation with bounded permissions, approval steps for consequential actions, and a full record of what the system did and why. Agents read content they do not control, so we treat prompt injection as a live threat and test for it as part of delivery rather than discovering it in production. Access to each connected system is scoped as narrowly as the task allows, with credentials the agent cannot escalate. We will decline to build open-ended access to production systems, because that is where automation stops being an asset and becomes a liability.
In short
AI agents and automation let software take multi-step actions on your systems, within explicit limits and with an audit trail. The safety of an agent is defined by what it cannot do, not by how capable it is.
Start a conversationWhat we do
We decide exactly which steps an agent may perform on its own and which require a person to approve. Consequential or irreversible actions sit behind an approval gate by default. Drawing this boundary first is what makes the rest of the design safe to build on.
We give the agent narrowly scoped access to only the systems a task needs, with credentials it cannot use to grant itself more. Least privilege limits the blast radius if the agent is manipulated or simply errs. Each connection is justified against the task rather than provisioned for convenience.
We log every action alongside the inputs that led to it, so the system's behaviour can be reconstructed after the fact. Consequential steps are made reversible wherever the underlying systems allow. This is what lets you answer, with evidence, what the automation did last week and why.
We test against adversarial and edge inputs, including prompt injection carried in the content the agent reads. Retrieved content is treated as untrusted, and what the tools can do is constrained so that a successful injection has limited reach. Containment is assumed to be needed, not hoped to be unnecessary.
What you receive
Documented output your teams keep and can act on without us in the room.
When this work helps
How an engagement runs
Understand the workflow, the data available, the constraints, and what a good outcome would look like. We establish the problem precisely enough to judge whether AI is even the right instrument for it.
Review data protection, security, oversight, and feasibility for the specific use cases on the table. We report which cases are worth building, which are better solved another way, and what would have to be true to proceed safely.
Set out the approach, controls, governance, and evaluation measures before build begins. Deciding data boundaries, oversight, and how success will be measured up front keeps them from becoming retrofits.
Support implementation and evaluate performance, error modes, and control effectiveness in use. We measure the system against the criteria agreed at design, and report honestly where it falls short.
Where we work
Fidova advises on AI adoption and AI governance from Delhi, India, and works with organisations internationally, including teams that must satisfy overlapping regimes such as India's data protection law, the EU AI Act, and GDPR.
Engagements run in English, are scheduled around your time zone, and can be delivered remotely end to end, with on-site time planned where a phase genuinely benefits from being in the room.
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Questions
Straight answers to what buyers ask us before an engagement begins.
Enquiries
Your enquiry lands in the same inbox as every other on this site and is read by us. Tell us the requirement and we will reply with a practical next step.
Prefer email? contact@fidovaadvisory.com
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