Artificial Intelligence · Build
We select and adapt language models against your actual task, privacy and cost constraints rather than against a leaderboard. That frequently means a smaller or self-hosted model where data sensitivity rules out a public API, and we assess that trade-off honestly because self-hosting carries real operating cost. Every choice is benchmarked on your own examples, since a model's general reputation says little about how it handles your specific inputs. We build a repeatable evaluation harness so that any later change to a model or prompt can be judged rather than guessed at.
In short
Large language model development covers selecting, adapting, hosting and evaluating LLMs for your own tasks and data sensitivity. The right model is the one that meets the task within your privacy and cost limits, which is often not the largest.
Start a conversationWhat we do
We compare candidate models on your own examples across quality, latency, privacy terms and cost rather than on published benchmarks. Vendor accuracy claims are treated as claims until they are tested against your inputs. The comparison gives you a defensible basis for the choice and a record of why the alternatives were rejected.
We design the instructions, the way context is assembled and the structure of the output so that results are consistent and can be parsed reliably by downstream systems. Small changes in how context is built often matter more than the choice of model. The aim is output that integrates cleanly rather than prose that a system then has to interpret.
Where data residency or sensitivity requires it, we assess and deploy models within your own boundary. We are candid that self-hosting trades convenience for control and carries ongoing operating cost, and we quantify that against a hosted option. The recommendation follows your obligations and economics, not a preference for either path.
We build repeatable tests so that a model or prompt change can be judged on evidence rather than impression. This lets you upgrade models or adjust prompts without silently regressing on cases that used to work. Over time the harness becomes the institutional memory of what good output looks like.
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
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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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