AI & Data Solutions for Real-World Impact

AI solutions are production systems that use data and models to automate workflows, support decisions, or assist teams inside real business processes. Enterprise AI typically includes integrations, access control, monitoring, and measurable KPIs. For foundational AI/ML capabilities, explore our AI/ML solutions hub

Common solution types include NLP systems, computer vision systems, predictive analytics, intelligent document processing, and GenAI applications. The approach depends on the workflow and the quality targets.

Both. Existing models and managed tools can accelerate pilots, while custom models are used when higher accuracy, domain specificity, control, or governance requirements apply.

Success metrics are tied to the workflow, such as automation rate, response quality score, cycle-time reduction, escalation rate, cost per transaction, and adoption by the target teams.

RAG (Retrieval-Augmented Generation) grounds AI responses in approved documents or knowledge sources. It is typically used when answers must reflect internal policies, product documentation, or enterprise knowledge.

Yes. AI solutions commonly integrate with CRM, ERP, ticketing/helpdesk platforms, data warehouses, and internal APIs so AI outputs connect to real operational workflows. See our customer experience automation solutions

Enterprise AI typically includes secure authentication, role-based access where needed, protected APIs, encryption in transit, audit-friendly logging, and controlled access to data sources and tools.

Quality evaluation typically includes test sets, accuracy/quality scoring, safety checks, and workflow validation with real examples. For GenAI, evaluation also tests groundedness, refusal behavior, and consistency.

A common process is: discovery and KPIs → architecture and data boundaries → build and integration → QA and security checks → controlled rollout → monitoring and iteration.

Change control typically includes versioning prompts/configs/models, gated releases, rollback plans, and regression evaluation so updates do not degrade quality or introduce new risks.

Mobiloitte typically delivers use-case discovery, KPI definition, data readiness assessment, model selection or development, AI application engineering, integration with business systems, and monitoring after launch. Our Converiqo AI platform

AI solutions focus on end-to-end applications and workflow outcomes, including integrations and user experience. AI/ML solutions focus more on model development, evaluation, and lifecycle management such as drift monitoring.

Start with one use case, examples of real inputs/outputs, and any available historical data. Even limited data can work for a pilot if scope is narrow and evaluation criteria are clearly defined.

Use evaluation gates, grounded retrieval when needed, safe fallback or escalation when confidence is low, and controlled permissions for any tool actions. Logging and monitoring help detect quality drift.

Hallucinations are reduced by grounding answers in approved sources, defining refusal behavior for missing information, using evaluation tests, and adding human review for high-risk responses or actions.

Yes, when properly governed. Automation typically uses tool permissions, allowlisted actions, approvals for sensitive steps, and audit logs so actions are traceable and controlled.

Yes. Regulated deployments typically require stricter access boundaries, audit logs, approved sources, change control, and human-in-the-loop escalation before customer-facing release. We serve BFSI, healthcare, and manufacturing

Timelines depend on data readiness and integration scope. A focused pilot for one workflow can be delivered faster than a production rollout with governance, multi-team adoption, and monitoring.

Yes. Monitoring typically covers performance, latency, error rates, usage/adoption, and quality drift. Support includes incident handling and iterative improvements based on production feedback.

Common failure reasons include unclear KPIs, poor data readiness, no workflow integration, lack of evaluation and monitoring, and missing governance for access and actions. Addressing these early improves outcomes.