Artificial Intelligence · Build
We build the operational discipline around models — versioned data and models, automated deployment, drift monitoring and a tested rollback path — so production models stay trustworthy over time. Every production prediction can be traced to the exact data, features and model version that produced it, which matters when a decision is later questioned. Retraining is driven by measured drift or accuracy decline rather than a calendar, so effort follows need. Degradation is caught by monitoring and alerting, not by the business noticing worse outcomes first.
In short
MLOps puts models into production repeatably: versioning, deployment, monitoring, retraining and rollback. Without it, a model that worked at launch degrades unnoticed until the business feels it.
Start a conversationWhat we do
We version data, features, code and models together so that any production prediction can be reproduced exactly. When a decision is challenged, you can show precisely what produced it. Reproducibility is also what makes debugging and auditing a model tractable rather than guesswork.
We promote models through environments with tests, approval and staged rollout rather than a manual hand-off. Staged rollout limits the exposure of a new model until it has proven itself on live traffic. Automation removes the fragile, undocumented steps that tend to live in one person's head.
We track input distribution, prediction distribution and outcome accuracy, with alerting when they move. Drift in the data a model sees is an early warning that its assumptions are breaking down. Catching it in monitoring is far cheaper than discovering it through poor decisions.
We define retraining triggers tied to measured decline and keep a tested path back to the previous model. Retraining on evidence rather than habit avoids both stale models and needless churn. A rehearsed rollback means a bad deployment is an inconvenience rather than an incident.
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
AI integration with enterprise systems embeds AI output into ERP, CRM, service and workflow platforms where the work actually happens. An AI tool that sits outside the systems of record gets used inconsistently and trusted even less.
AI strategy and consulting decides where artificial intelligence is worth using in your organisation, and what it will take to run it responsibly. It is as much about the cases to decline as the ones to pursue.
Machine learning development builds and validates models on your own data, with honest evaluation and a route into production. A model that cannot beat a simple baseline is not worth deploying, and we say so.
Questions
Straight answers to what buyers ask us before an engagement begins.
Enquiries
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Prefer email? contact@fidovaadvisory.com
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