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AI advisory at Fidova starts with the question most projects skip: which decisions or tasks genuinely improve with AI, and what has to be true about data, security, and oversight before that is safe to do. In several cases the honest answer is that better data, a clearer process, or conventional automation would deliver more at lower risk, and we say so rather than sell a programme. Where AI does earn its place, we establish what data it may use and on what basis, where a person must stay in the decision, and how the system will be evaluated before it is relied upon. Our cybersecurity practice can test the same system for prompt injection and data leakage, so the risk is measured rather than merely described. The connected view matters because an AI feature is still software carrying data, security, and governance consequences that rarely stay within one team.
In short
Fidova's AI advisory helps organisations decide where artificial intelligence creates real value, and implement it with data protection, security, human oversight, and governance designed in. We begin with whether AI is the right instrument at all, and treat the controls around it as part of the build rather than paperwork added once something already works.
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What we do
Identify where AI improves a real workflow, and set aside the cases where a simpler change or better data would do more. We weigh each candidate against the effort, risk, and data it demands, so attention goes to the uses that repay it. The output is a shortlist you can defend to a board, not a list of experiments.
Establish what data a system may use, on what lawful basis, with what retention, and what must never leave your control. That classification is done first, because it constrains everything downstream, including whether a hosted model is acceptable at all. The result is a clear boundary around the data an AI system may touch, decided before anything is wired up.
Assess prompt injection, data leakage, model and supply chain risk, access control, and the integration surface around AI features. We test these as working systems rather than reviewing them on paper, because the exposure is concrete. Findings come with severity, evidence, and remediation so they can be acted on, not just noted.
Define where a person must remain in the decision, how outputs are reviewed, and who is answerable for what the system produces. Oversight is matched to consequence — heavier for decisions that affect people or money, lighter where a mistake is cheap. The aim is accountability that is clear before a problem occurs, not assigned afterwards.
Create usable internal policy, approval routes, an inventory of AI use, and the evidence you need for customers, auditors, and regulators. Policy is written to be read and followed rather than filed, and mapped to the obligations behind it. The result lets you answer how AI is governed with records instead of assurances.
Support the build, then measure quality, error modes, and drift against the outcome the use case promised. We define evaluation criteria for the specific use case and report what the system actually does against them, including how it fails. That keeps vendor accuracy claims as claims until they are tested in your context.
Sub-services
Each service below has its own page setting out the work, the deliverables and the questions buyers ask before starting.
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.
Generative AI solutions apply text, image and code generation to specific business tasks, with grounding, review and cost control built in. The discipline is in choosing tasks where the output can be checked and the cost of an error is understood.
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.
Retrieval-augmented generation systems answer questions from your own documents and data, with citations and permission awareness. Most of their accuracy depends on retrieving the right passages, not on the model that writes the answer.
AI agents and automation let software take multi-step actions on your systems, within explicit limits and with an audit trail. The safety of an agent is defined by what it cannot do, not by how capable it is.
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.
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.
Conversational AI handles customer and employee questions in chat, with grounded answers and a clean handover to a person. An assistant that hides the route to a human does more harm than good.
Intelligent document processing extracts and validates data from invoices, forms, contracts and scanned records. Extraction without validation produces data that looks precise and cannot be trusted.
AI-powered search improves how people find information across your products, documents and internal systems. Better content structure and ranking often matter more than replacing the search platform.
Fraud detection models identify suspicious transactions and behaviour, balancing losses prevented against legitimate customers blocked. The threshold is a business decision about that trade-off, not a technical default.
Forecasting models project demand, revenue, capacity or cash with stated uncertainty rather than a single false-precision number. A range you can plan buffers against is more honest, and more useful, than a point estimate.
Personalization engines adapt content, offers and journeys to the individual, within clear consent and privacy limits. The line between helpful and intrusive is a design constraint, not an afterthought.
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.
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.
What you receive
Every engagement produces 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. Where a phase benefits from being in the room — discovery workshops, go-live support, or on-site review — we plan that time explicitly.
Connected expertise
Selected work
Customer support
One grounded AI at the centre of every support channel — web chat, email, messaging and live phone calls — resolving routine enquiries around the clock and escalating to a person only when it should.
Sales enablement
Real-time intelligence that listens to live sales calls, transcribes both sides, reads sentiment and buyer interest, and delivers in-the-moment coaching — with live quality control and performance-based lead routing for managers.
Gaming
A white-label machine-learning platform that turns a gaming company's own player data into recommendations, churn risk, lifetime value and real-time decisioning — PII-free by design, and honest about what the models do and do not yet know.
Questions
Straight answers to what buyers ask us before an engagement begins.
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