Artificial Intelligence · Build
We build computer vision where the capture conditions — lighting, angle, resolution, variability — are controllable enough for it to work reliably, such as inspection, counting, detection and document imagery. We assess that feasibility first, because most vision failures are capture failures, and we will tell you when the camera setup rather than the model is the limiting factor. Our datasets include the awkward cases that decide real accuracy, not just the clean examples that flatter it. Where people are visible we involve our data protection practice on lawful basis, retention, minimisation and on-device processing.
In short
Computer vision extracts information from images and video: detection, classification, measurement and inspection. Whether it works at all is usually decided by the capture conditions, not the model.
Start a conversationWhat we do
We assess lighting, angle, resolution and variability before committing to a build, because most vision failures trace back to how images are captured. This often reveals that improving the capture setup is cheaper and more effective than a more elaborate model. It also prevents paying for a model that cannot succeed on the images it will actually be given.
We build a representative labelled dataset that includes the awkward, borderline and rare cases that determine real-world accuracy. A dataset of only clean examples produces a model that fails exactly where it matters. Coverage is documented so you know what the model has and has not been shown.
We train or adapt detection and classification models and measure them against a defined baseline. Reporting failure cases honestly is as important as reporting the headline accuracy. The aim is a model whose limits are known before it reaches the field, not after.
We deploy where latency, bandwidth and privacy require, including on-device inference when images should not leave the site. On-device processing can also be the cleanest answer to privacy obligations. The deployment design follows the operational constraints rather than a default architecture.
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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