Smart Solutions, Real Impact
Your Vision, Our Craft
Connecting Your World
Mobiloitte delivers enterprise conversational AI with LLMs, RAG-grounded bots, automation, and guardrails helping teams in support, sales, HR, ops, and ITSM boost productivity and ROI.
Bots that search, fetch data, run workflows, create tickets, call APIs, and close the loop so work really gets done.
Vector search, hybrid retrieval, and re-ranking keep answers grounded and reduce hallucinations.
Policy-controlled access to CRM, ERP, ITSM, HRIS, billing, and internal APIs.
Agents coordinate, keep state, hand off to humans, and give full visibility of actions.
Deploy on web, mobile, email, multichannel AI bot for WhatsApp and Slack, SMS, IVR/voice, and Teams with shared context.
Defense against injection/jailbreaks, toxicity filters, PII scrubbing, content classifiers, and RBAC.
LLM & human evals with rubric scores and CI/CD ensure quality, while consent-based personalization stores profiles, preferences & history.
Routing, caching, quantization, and distillation to keep responses fast and costs low
Dashboards for intent coverage, containment, CSAT, FCR, hallucination rate, cost, and latency.
Self-hosted models and vector DBs for regulated or air-gapped environments.
24/7 support, audit logs, policy control, and explainability tools.
We provide 24×7 operations, playbooks, and incident response services specifically designed for critical workloads.
Many “AI chatbots” only chat. Mobiloitte ships production agents: grounded by your data, controlled by policies, visible in real time, and measured against business KPIs. With function calling, RAG, safety layers, and LLMOps, the bot completes tasks accurately, safely, and at a fair cost, and results are easy to prove.
Well coded Testable, safe agent architectures that follow clear rules.
Responsive Embedded pods share targets for quality and business KPIs..
Fast growing Built to handle more use cases, languages, channels, and models.
Multipurpose One platform for support, HR, ITSM, finance ops, and sales.
Mobiloitte Helps Pick High-Impact First Moves Such as LLM-Powered Customer Support Automation, Internal IT/HR Tasks, Sales Enablement, and Knowledge Assistants, Then Sets Up Guardrails and Policies So Teams Stay in Control.
Use-case ROI vs. complexity matrix
Architecture plans (RAG, function calling, agent orchestration)
Safety, compliance, and governance policies
Prompt and evaluation plan
Build / buy / hybrid recommendations
Mobiloitte Connects Agents to CRMs, ERPs, ITSMs, HRIS, and Data Platforms, With RAG, Policy Filters, and Multi-Channel Delivery Plus Evals and Monitoring From Day One.
Tool-using LLM agents with full action logs
RAG pipelines (chunking, hybrid retrieval, re-ranking, prompt compression)
Channel adapters for web, mobile, WhatsApp, voice, Slack, and Teams
Agent analytics (coverage, containment, CSAT, FCR, hallucination rate)
Built-in security (RBAC, masking, PII scrubbing, audit logs)
Mobiloitte Runs and Improves the Platform With LLMOps, CI/CD, Eval Suites, Cost-Latency Tuning, and Red-Teaming to Keep Risks Low and Quality High.
Prompt/version management, regression tests, sandboxing
Cost, latency, and throughput tuning (quantization, routing, caching)
Regular LLM + human evaluations
Policy violation monitoring, red-teaming, toxicity/jailbreak defense
24/7 SLAs, incident playbooks, and governance reviews
Choose LLMs, vector DBs, and orchestration; set KPIs, policies, and data contracts
Launch RAG, tool calling, multi-channel delivery, and LLMOps with guardrails and evaluations. Test on real user journeys and ops metrics
Run continuous evals, tune cost/performance, red-team regularly, and expand use cases across functions and regions.
OpenAI, Claude, Llama, Mistral, Mixtral • LangChain, LlamaIndex • vLLM, Triton, Ray • Pinecone, Weaviate, Milvus, pgvector • MLflow, W&B • Airflow/Prefect • Kafka/Flink/Spark • Twilio/WhatsApp, Slack/Teams, IVR/telephony • Pydantic/JSON schema validators
Responsible AI is built in, not bolted on. Guardrails, filters, prompt isolation, policy enforcement, audit logs, and SOC2/GDPR/HIPAA/PCI-aligned controls come standard. Dashboards explain what the bot did and why, so leaders, CISOs, and regulators can trust the system.
They use RAG grounding, guardrails, schema/JSON validation, and policy engines to control tool use. Ongoing evals track hallucinations, toxicity, and policy adherence. When uncertain, the agent can pause or hand off to a human.
LLMOps versions prompts, tools, datasets, and models and adds tests and monitors like production software. It brings safety checks, cost controls, and eval pipelines designed for LLMs. Without LLMOps, bots are hard to audit and expensive to run.
Yes. Tool/function calls run behind RBAC, policy layers, and secrets management. The agent only uses whitelisted tools within limits, and every action is logged. Sensitive workflows can run on-prem or air-gapped.
They use rubric scoring (LLM + human), automated eval suites, and live conversation sampling. They measure instruction-following, correctness, groundedness, latency, and cost. Failing cases trigger prompt/tool/model updates via CI/CD.
A classic chatbot answers with fixed flows or FAQs. Their LLM agents can look up facts (RAG), call tools, follow policies, and complete tasks end-to-end with traceable actions. The result is less handoff and higher task completion.
Yes. They support self-hosted/open-source models, vector DBs, and policy engines in private cloud or air-gapped setups. You get the same features without sending data to public clouds.
They use multilingual embeddings, translation pipelines, language routing, and LoRA adapters for domain terms. Performance is checked per language to reduce bias and meet local requirements.
They use dynamic model routing, caching, prompt compression, quantisation, and distillation. Costs are tracked per successful task, not just per token. Budgets and alerts prevent surprises.
System prompts are isolated, user inputs are sanitised, and outputs must match schemas. They run attack simulations, apply toxicity filters, and restrict tools to least-privilege access.
Yes. Multi-agent orchestration assigns roles, shares memory, and records every step. Agents can review each other’s work and escalate to humans when needed.
A RAG-based MVP with tools, guardrails, and evals typically ships in 4–8 weeks. Scaling to more channels, languages, and complex workflows follows in 8–12+ weeks.
It supports them. The bot handles repetitive tasks so people focus on edge cases and strategy. With clear thresholds and audits, responsibility can expand safely over time.
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If a chatbot is polite but unhelpful, it’s time to upgrade. Mobiloitte delivers conversational AI that finishes tasks, follows rules, and shows clear results ready for production from day one.