Artificial Intelligence · Build
We improve models already in use across data quality, hyperparameters, quantisation, batching and serving, with each change measured against a fixed evaluation set so improvement is proven rather than hoped for. In our experience the biggest gains come from data quality — labelling consistency, class balance and leakage — followed by inference engineering, with architecture changes usually the smallest lever. We can often cut inference cost materially through quantisation, caching, batching and routing simple cases to smaller models. We report the measured quality impact of each optimisation rather than assuming it is free.
In short
Model training and optimisation improves accuracy, latency and cost through better data, tuning, and inference engineering. The largest gains usually come from data quality, not from a change of architecture.
Start a conversationWhat we do
We fix labelling errors, class imbalance and leakage, which usually yields more improvement than any model change. Cleaning the data a model learns from is unglamorous and reliably effective. It is the first place we look when accuracy has stalled.
We search hyperparameters and architectures with every run tracked and reproducible. Disciplined tracking turns tuning into a methodical search rather than a sequence of lost experiments. It also lets a promising result be rebuilt and verified rather than remembered.
We apply quantisation, distillation, batching and caching to cut latency and cost. Each technique is measured for its effect on quality, because speed and cost savings are only worth having if accuracy holds. The result is a serving profile tuned to your real workload.
We right-size hardware and concurrency so throughput matches real demand rather than a peak that rarely occurs. Over-provisioned serving quietly wastes money; under-provisioned serving fails users. Matching capacity to actual load keeps both cost and responsiveness in check.
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
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.
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.
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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