Global AI & digital engineering partner. Agentic AI systems, RAG apps, unified BOT platforms, cloud & DevOps, mobile/web, Web3 & IoT. Enterprise to startup. DM.
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Jan 1, 2027: AI systems will face tougher disclosure expectations.
Can your AI show its receipts?
mobiloitte.com/contact-us?ut…
#AIGovernance #AIRegulation
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AI is easy to demo. Production is where architecture gets real.
Data ownership. System boundaries. Approvals. Rollback. Observability.
Treat the handoffs as part of the product—not plumbing.
mobiloitte.com/contact-us?ut…
#EnterpriseAI #SoftwareArchitecture
Made with AI
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McKinsey’s 2026 read: AI use is near-universal, 44% scaling enterprise-wide — yet high performers hold flat at ~6%. Two flat readings of that 6% while models leapt forward. Signal: the constraint is discipline, not capability. More of this weekly — follow along.
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2009: a mobile-app team in Okhla explaining what an “app” was. 2026: six offices, clients in 70+ countries, 5,000+ projects (figures: mobiloitte.com). The constant: the gap between demo and deployed — and the discipline that closes it.
mobiloitte.com/about-us/
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Hiring: Sr AI Engineer — Agentic AI, RAG & low-latency voice. 📍 Okhla, Delhi
The job? Make an AI agent interruptible mid-sentence without losing state.
If that problem sounds exciting rather than annoying, we should talk.
mobiloitte.com/careers
#AIJobs #Hiring
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Security shouldn’t be a final QA checkbox.
Ask one question: Which phase is hardening?
If it has no clear phase or exit criteria, security may still be an add-on.
mobiloitte.com/contact-us?ut…
#CybersecurityAwarenessMonth
Made with AI
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AI in banking doesn’t have to make the decision to add value.
It can read the documents, structure the data, flag exceptions—and leave judgement with people.
More: mobiloitte.com/industries/
#BFSI
Made with AI
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19.8M people died from cardiovascular disease in 2022 (WHO). Over 80% of these deaths are linked to risk factors that can be prevented or managed.
AI is helping doctors spot heart disease sooner.
#WorldHeartDay #DontMissABeat ❤️
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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
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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?ut…
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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?ut…
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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?ut…
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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?ut…
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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?ut…
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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?ut…
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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?ut…
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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?ut…
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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?ut…
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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?ut…
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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?ut…
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