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Mobiloitte’s AI integration platform connects CRMs, ERPs, data, and APIs to LLMs/ML with LLMOps, MLOps, governance, and guardrails, optimising latency, cost, and risk for enterprises.
Swap OpenAI, Anthropic, Llama, Mistral, or classic ML without rewriting apps.
Add RAG and vector database integration (Pinecone, Weaviate, Milvus, pgvector, OpenSearch) with grounding, re-ranking, and evaluations.
Manage prompts, models, datasets, and versions with CI/CD, eval pipelines, drift checks, and cost monitoring.
Run a secure AI gateway for internal tools with policies, rate limits, observability, and backups.
Trigger real-time event-driven AI workflows via Kafka, Flink, Spark, webhooks, or queues.
RBAC/ABAC, PII scrubbing, masking, secrets management, audit logs, and red-teaming.
Dynamic model routing, caching, quantisation/distillation, and FinOps dashboards.
Let LLMs use approved tools (CRM, ERP, data services) under strict policies.
Run OSS LLMs in AWS/Azure/GCP, private cloud, or on-premise AI deployment for regulated industries.
SOC2, HIPAA, GDPR, PCI; prompt/model lineage and explainability dashboards.
Track quality, groundedness, hallucination rate, latency, cost, throughput, and ROI not just tokens.
Runbooks, incident playbooks, SLAs, and follow-the-sun support.
Most AI pilots live in notebooks and fail in production. Mobiloitte turns models into reliable services: an AI gateway handles auth, routing, logging, evaluations, safety, and cost controls. Apps call one secure interface. Behind the scenes, the platform routes, retries, caches, grounds, and governs so teams scale safely without lock-in.
Well coded Typed contracts, schema validation, CI/CD, test suites, blue/green releases.
ResponsiveShared SLOs, budgets, and policy ownership with embedded pods.
Fast growing Horizontal scale, queues/backpressure, async pipelines, batched/vectorised inference.
Multipurpose One place for chat, agents, RAG, predictive ML, analytics, and automation.
Mobiloitte Maps Systems, Data, Workflows, Compliance Needs, and ROI Goals, Then Designs a Model-Agnostic Layer With the LLMOps/MLOps, Governance, and Security Required to Grow.
Reference architecture (gateway, routing, RAG, observability, evaluations)
Build/buy/partner strategy (platforms, vector DBs, eval frameworks)
Policy & governance blueprint (access, lineage, retention, red-teaming)
FinOps design and cost/latency/performance goals
Plan for integrating tools and data (CRMs, ERPs, data lakes, event buses)
Mobiloitte Sets Up Middleware, Adapters, SDKs, RAG Layers, Vector DBs, Model Routers, Tool/Function Calling, and LLMOps/MLOps Pipelines Ready for Safety, Evaluations, and Audits.
AI gateway/service mesh with authentication, policies, routing, retries, caching
RAG pipelines (chunking, hybrid retrieval, re-ranking, prompt compression)
Integration adapters for Salesforce, SAP, ServiceNow, Workday, Snowflake, Databricks, Kafka, and more
LLMOps/MLOps CI/CD, prompt/model registries, eval suites
Real-time analytics dashboards (quality, cost, latency, hallucination, ROI)
Mobiloitte Runs the AI Platform With Continuous Evals, Drift Detection, Cost Optimisation, Safety Monitoring, and Governance Audits With 24/7 Production Support.
Ongoing evaluations (hallucination, groundedness, toxicity, bias)
Versioning and regression testing for prompts, models, datasets
Routing, distillation, quantization to control cost/latency
Policy enforcement, red-teaming, PII leak checks, SOC2/GDPR/HIPAA alignment
SLAs, runbooks, incident response, quarterly ROI/architecture reviews
Review use cases, systems, compliance, and ROI; propose a secure, model-agnostic integration architecture with LLMOps/MLOps.
Launch gateway/middleware, RAG, adapters, safety filters, eval suites, and observability. Test with synthetic and real traffic to meet ROI and cost goals.
Monitor, retrain, reroute, and reprice models as usage grows. Add new tools and teams while keeping the platform safe, fast, and cost-efficient.
OpenAI • Anthropic • Llama • Mistral • Mixtral • vLLM • Triton • Ray • LangChain • LlamaIndex • Pinecone • Weaviate • Milvus • pgvector • OpenSearch • MLflow • W&B • BentoML • Airflow/Prefect • Kafka/Flink/Spark • Snowflake • Databricks • BigQuery • dbt • Kong/Envoy/API Gateways • OPA/OpenFGA for policy
Prompt isolation, output/schema validation, lineage, audit logs, red-team pipelines, model cards, and retention controls come standard. Designs align with SOC2, HIPAA, GDPR, PCI, and ISO/IEC 42001 so leaders and regulators can trust the platform.
They place a neutral gateway between apps and models. Prompts, evaluations, and routing live in your platform, not a vendor SDK. Switching or mixing models becomes a policy change, not a rebuild.
Input/output filters, PII scrubbing, and policy engines (OPA/OpenFGA) protect data. Role-based access limits tools, and schema validation controls outputs. All actions are logged for audit.
Yes. Adapters cover Salesforce, SAP, Workday, ServiceNow, Snowflake, Databricks, Kafka, and more. Auth, secrets, and policies are consistent across integrations for strong governance and shared observability.
MLOps manages classic ML (features, training, serving). LLMOps adds prompt/version control, RAG index governance, safety evals, and cost routing. Skipping LLMOps makes LLM features risky and expensive.
They run task-specific evals for accuracy, groundedness, latency, cost per success, and support KPIs (CSAT/FCR). In production, dashboards show deflection, time saved, and revenue impact—so keep/kill calls are clear.
Dynamic routing, caching, prompt compression, distillation/quantisation, and batching/speculative decoding reduce spending. Teams track cost per successful task, not just tokens, with FinOps alerts.
Yes. Self-hosted/open-source models, vector DBs, and gateways run fully on-prem or on a sovereign cloud. You keep RAG, evals, LLMOps/MLOps, and policy layers with data staying inside your network.
A policy layer checks intent, scope, RBAC/ABAC, and rate limits before any action. Tool calls are logged, tested, and replayable. Agents run with least privilege; violations are blocked and surfaced.
They monitor eval scores, latency, cost, groundedness/hallucination, toxicity, and task success. The platform can roll back, reroute, or retrain automatically. Regression tests protect production.
Yes. They simulate prompt injection, jailbreaks, and exfiltration. Findings feed updated guardrails, policies, prompts, and models, closing the loop.
Often 3–6 weeks for an AI gateway plus 1–2 use cases. Broader rollouts (more systems, teams, and regions) and hardening usually add 6–12+ weeks.
The gateway is extensible: new models, vector DBs, and eval frameworks plug in behind the same interface. Policy and governance sit above providers, and quarterly reviews keep ROI and architecture current.
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Vendors change, models evolve, and rules tighten. With Mobiloitte’s policy-driven, model-agnostic layer, teams adopt the best tech now and later without replatforming or losing compliance.