Smart Solutions, Real Impact
Your Vision, Our Craft
Connecting Your World
Mobiloitte designs, builds, and runs LLM apps, reliable RAG search, and multi-agent systems. With LLMOps and model governance services, clear evals, and safety guardrails, teams can track quality, cost, compliance, and risk from day one.
Mobiloitte helps pick use cases that are practical, safe, and profitable before any code is written.
Copilots, domain copilots, agents, summarisers, code assistants, and knowledge bots that solve real work problems.
Hybrid retrieval, smart chunking, re-ranking, and prompt design to keep answers grounded and correct. (Fits enterprise RAG search solutions.)
LoRA/QLoRA and adapters to teach models domain behaviour with less cost and faster response.
Versioning, CI/CD, evaluations, human review, drift checks, and continuous improvement.
Toxicity filters, jailbreak and prompt-injection defense, PII cleanup, and policy engines.
Dynamic routing, caching, pruning, quantisation, and multi-model orchestration for AI cost
Run on AWS, Azure, GCP, private cloud, or secure on-prem clusters ideal for on-premise AI
Dashboards for quality, hallucination rate, toxicity, latency, and spend.
Access control, lineage, anonymisation, retention, and responsible AI frameworks (GDPR/SOC2/HIPAA).
Text + image + audio + video pipelines for richer use cases and true multimodal generative AI
We provide 24×7 operations, playbooks, and incident response services specifically designed for critical workloads.
Many GenAI projects stop at the demo stage. Mobiloitte moves them to production. Senior architects convert fuzzy ideas into reference designs, safety patterns, LLMOps pipelines, and releases that create business value. Deep integration with data, identity, and compliance makes solutions easy to check, control, and scale.
Well coded Clean, testable code and infra; visibility from day one.
Responsive Built-in pods for discovery, experiments, and quick iteration.
Fast growing Built for scale: multi-model routing, autoscaling, and cost limits.
Multipurpose Modular accelerators so every business unit sees value faster.
Mobiloitte Reviews Business Goals, Data Quality, Governance, and Tech Stack to Shape a Clear AI Roadmap, What to Build First, How to Govern It, How to Measure ROI, and Which Platform Choices Reduce Long-Term Risk
Use-case portfolio development
Data and governance maturity assessment
ROI estimation for AI initiatives
Low-risk architectural planning
Mobiloitte Builds LLM Apps for Daily Work: Ops Copilots, Knowledge Assistants, Code Assistants, Multi-Agent Workflows, and Domain Q&A.
Data pipelines for LLM applications
RAG and custom LLM app development
Safety layers implementation
Regular evaluations tracking business KPIs
Mobiloitte Turns AI Into a Product With CI/CD, Model Registries, Eval Frameworks, Observability, Policy Enforcement, and Smart Cost Control.
Scalable workloads with monitoring
Cost control and drift detection
Human feedback integration
Audit-ready documentation
Mobiloitte and the client shape a use-case portfolio, check data and governance maturity, estimate ROI, and plan a low-risk architect
Data pipelines, RAG, LLM apps, LLMOps, and safety layers go live. Regular evals track business KPIs and policy limits.
Workloads scale with monitoring, cost control, drift detection, human feedback, and audit-ready documentation.
OpenAI, Claude, Llama, Mistral, Mixtral, DeepSpeed, vLLM, Ray, Triton, LangChain, LlamaIndex, MLflow, Weights & Biases, BentoML, Nvidia Triton, Hugging Face, Pinecone, Weaviate, pgvector, Milvus, OpenSearch, Kafka, Flink, Spark, Airflow, dbt, Delta Lake, Iceberg.
Solutions satisfy product teams and regulators alike. Access controls, PII scrubbing, bias testing, explainability, model cards, and audit logs come standard supporting LLMOps and model governance services and HIPAA-compliant AI solutions for healthcare.
RAG keeps answers current by pulling facts from a company’s data without changing model weights. Fine-tuning teaches stable formats, styles, or tasks for lower cost and latency.
Evaluations (groundedness, source overlap, and task accuracy), retrieval metrics, and guardrails are used, plus feedback loops and monitoring to limit drift.
Yes. Open-source LLM stacks and on-premise vector DBs run with strict RBAC, secrets, and network isolation, beneficial for regulated industries.
Model routing, caching, prompt compression, distillation/quantisation, batching, and speculative decoding are tracked with FinOps dashboards for AI cost optimisation.
LLMOps adds prompt/version control, RAG index tracking, LLM-specific evals, guardrails, and policy enforcement.
Task-specific eval suites (LLM-as-judge + human) are tied to KPIs and automated in CI/CD and production.
Yes. Multilingual embeddings, adapters/LoRA, tokeniser-aware preprocessing, and language-aware routing are used.
Input/output validation, separated system prompts, policy engines, red-teaming, and RBAC/tool allow-lists are combined.
Choice depends on scale, latency, ops maturity, and lock-in tolerance; selection follows workload and TCO analysis.
Yes. Adapter-based designs allow swapping models, vector DBs, and clouds.
Yes. Adversarial testing, fairness checks, toxicity tests, model cards, data sheets, and audit logs are delivered.
A governed MVP often ships in 4-8 weeks with clear scope and data; full hardening and scale follow in 8-12 weeks or more.
Did you not get your answer? Email Us Now!
Mobiloitte ensures the first LLM use case is solid, and the AI portfolio can grow without lock-in or surprise costs.