Mobiloitte | Agentic AI, RAG & Digital Engineering @Mobiloitte
Global AI & digital engineering partner. Agentic AI systems, RAG apps, unified BOT platforms, cloud & DevOps, mobile/web, Web3 & IoT. Enterprise to startup. DM. mobiloitte.com India •SG • UK • US • UAE • SA Joined April 2013-
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80%+ of world trade by volume moves by sea, carried by 2.57M seafarers. In 2026, IMO adopted the first global code for autonomous ships. The AI era at sea is starting with rules and with people. Happy #WorldMaritimeDay
In regulated workflows, the model is not the deliverable. The auditable process is. Outputs that cannot be explained, versioned, or reproduced are unusable in banking, insurance, and healthcare. mobiloitte.com/contact-us?utm…
Fine-tuning is not for missing facts. Its real signal is behavior variance you cannot eliminate. If the model behaves correctly most of the time, but not reliably enough for production, tuning may be a candidate. mobiloitte.com/contact-us?utm…
Prompting first. Then retrieval. Then fine-tuning. That is the escalation path. Most teams underinvest in prompt design, conclude the model cannot do it, and jump two rungs too early. mobiloitte.com/contact-us?utm…
Before fine-tuning, run the diagnosis: Facts wrong = retrieval problem. Behavior inconsistent = fine-tuning candidate. Instructions ignored = prompting problem. The right fix starts with the right diagnosis. mobiloitte.com/contact-us?utm…
One AI project is an achievement. Repeated AI delivery is a capability. That requires ownership, reusable foundations, honest evaluation, governance, monitoring, and a real operating model. mobiloitte.com/contact-us?utm…
AI adoption is not automatic. A technically successful system with no users delivers nothing. Production AI needs workflow change, business ownership, training, rollout planning, and trust. mobiloitte.com/contact-us?utm…
Most enterprises do not need to fine-tune an LLM. They reach for it too early and spend months arriving where retrieval would have started. Fine-tuning is powerful — but only for a narrow band of problems. mobiloitte.com/contact-us?utm…
AI use-case selection is the cheapest risk control in the whole programme. Pick use cases with a measurable problem, workable data, manageable integration, realistic accuracy bar, and real adoption path. mobiloitte.com/contact-us?utm…
The best AI production playbook has five phases: Problem definition. Data assessment. Honest POC. Production build. Deployment, adoption, and operation. Skip one and risk the whole rollout. mobiloitte.com/contact-us?utm…
The gap between AI pilot and AI production is not one step called deployment. It is seven streams: Data. Reliability. Integration. Scale. Governance. Operations. Adoption. mobiloitte.com/contact-us?utm…
The POC trap is simple: The proof of concept works, so people assume the product is nearly done. But the POC only proved feasibility. Integration, governance, reliability, adoption, and scale still remain. mobiloitte.com/contact-us?utm…
The real test of AI ethics is not the policy document. It is how the system treats users with the least power to detect, contest, avoid, or recover from harm. mobiloitte.com/contact-us?utm…
Designing for vulnerable users is not a trade-off. Clear communication, accessibility, robustness, and recourse improve the system for everyone. mobiloitte.com/contact-us?utm…
A clean AI pilot can look impressive. Production is different. Production has messy data, edge cases, real users, security, integration, governance, and scale. That is where the real work begins. mobiloitte.com/contact-us?utm…
AI pilots often die before production for one simple reason: They were launched to “use AI,” not to solve a measurable business problem. No number to move means no bar for success. mobiloitte.com/contact-us?utm…
Most enterprise AI pilots do not fail because the model is weak. They fail because the business problem, data, integration, adoption, governance, or production path was never properly planned. mobiloitte.com/contact-us?utm…
Consent does not fix every AI ethics problem. If a user cannot realistically understand the interface, the consent is not meaningful. Design must carry the duty. mobiloitte.com/contact-us?utm…
AI systems often fail vulnerable users in four ways: Typical-user design. Thin training data. Limited testing. Weak recourse. These failures compound. mobiloitte.com/contact-us?utm…
The same person can be vulnerable to one AI system and not another. Nothing about the person changed. The system relationship changed. mobiloitte.com/contact-us?utm…
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