Artificial Intelligence · Build
We fine-tune models only where the evidence supports it, after establishing that prompting, retrieval and better examples have genuinely been exhausted. Those approaches solve most problems more cheaply and are far easier to change later, so we test them first and will tell you when tuning is not worth it. When tuning is justified, we curate clean, representative examples with a held-out evaluation set and check that the data is properly licensed. Every tuned model is compared on quality, latency and total cost against the prompted baseline before we recommend adopting it.
In short
Fine-tuning adapts an existing model to your domain, format or tone using your own examples — when prompting alone is not enough. It is a targeted tool, not a default, and often not the cheapest answer.
Start a conversationWhat we do
We test prompting and retrieval baselines first and quantify exactly what fine-tuning would add over them. This often shows that a cheaper, more flexible approach already meets the need. When it does, we say so rather than proceeding to a tuning project you did not need.
We build clean, representative training examples with held-out evaluation data and confirm that the data is properly licensed for the purpose. A smaller, well-curated dataset usually beats a large noisy one. Licensing and provenance are checked up front, because they are difficult to unwind later.
We run tuning with tracked configurations so that every result is reproducible and comparable. Knowing precisely which data and settings produced a model is essential when iterating or auditing. It also means a good result can be rebuilt rather than rediscovered.
We compare quality, latency and total cost against the prompted baseline before recommending adoption. A tuned model that is only marginally better but harder to maintain is rarely the right choice. The comparison makes that trade-off explicit rather than assumed.
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
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.
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.
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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