Artificial Intelligence · Build
We build recommendation systems that are judged on business results — conversion, revenue, engagement — rather than on offline metrics that often fail to translate. They are designed to behave sensibly for new users and new items, where naive systems break down, and to avoid collapsing onto the same handful of bestsellers for everyone. Where behavioural data is sparse, we are honest that content and rule-based approaches may serve better, and we start there rather than forcing a model. Improvement is settled by controlled online experiments, not by a metric that looks good in a notebook.
In short
Recommendation systems suggest products, content or actions based on behaviour and context, measured against real business outcomes. An offline score that never moves revenue or engagement has proven nothing.
Start a conversationWhat we do
We capture the behaviour that genuinely predicts intent, with event definitions your analytics team can trust and reuse. Getting the signals right matters more than the sophistication of the model that consumes them. Clear definitions also prevent the quiet drift where the same event means different things in different places.
We build retrieval and ranking stages sized to your catalogue and latency limits, so recommendations are both relevant and fast enough to serve in place. Separating candidate generation from ranking keeps the system tractable as the catalogue grows. The ranking logic is documented so it can be reasoned about rather than treated as a black box.
We handle new users and new items deliberately, since these are where recommendations most visibly fail. We also prevent the system collapsing onto a few popular items, which erodes discovery and long-tail sales. The rules balance relevance against the variety that keeps the experience useful.
We run controlled experiments measuring conversion, revenue or engagement against a real control group. Offline metrics inform the build but do not settle whether a change helped. This is the only reliable way to prove that a recommendation change earned its place.
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.
Related services
Predictive analytics estimates likely outcomes — churn, default, demand, failure — so decisions can be taken earlier. A prediction only has value when a process is ready to act on it.
Natural language processing extracts structure and meaning from text: classification, entity extraction, sentiment and routing. Its value depends on a precise taxonomy and honest per-category accuracy, not on the model alone.
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.
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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