Build Advanced Analytics & BI That Drive Decisions

Mobiloitte upgrades the full BI stack from the semantic metrics layer for DBT and governed data models to causal and predictive intelligence and real-time dashboards. Every decision becomes clear, repeatable, and tied to business outcomes.

Why Choose Us

Unlock The Possibilities

  • Warehouse/lakehouse first • dbt + metrics layer
  • Real-time streaming analytics with Kafka/Flink/Sparkr
  • MLOps & XAI dashboards • SOC2 / GDPR / HIPAA ready

Warehouse/Lakehouse-first BI

Replace black-box metrics with a warehouse-first BI platform using dbt models and a shared semantic layer.

Real-time Analytics & Streaming

Kafka, Flink, and Spark feed live dashboards, alerts, and decision engines.

Causal Inference & Experimentation

Move beyond correlation with causal inference for marketing lift, uplift modelling, and disciplined tests.

Forecasting & Scenario Planning

ARIMA/Prophet, GBMs, and DL forecasts with confidence bands and simple explanations great for predictive forecasting for demand planning.

Decision Intelligence Automation

Turn analytics into safe, policy-based actions that can trigger changes automatically.

Self-serve Analytics with Governance

Semantic layers and curated templates let teams explore without breaking metrics.

Metrics Store & Lineage

One source of truth with versioned metrics, lineage, and documented assumptions.

Cost & Performance Optimization

FinOps dashboards, partitioning, compaction, vectorisation, and caching.

ML & LLM-augmented BI

Narratives, anomaly explanations, and grounded NLQ for faster insights. .

Data Quality & Observability

Freshness checks, drift detection, anomaly alerts, and SLAs for reliable decisions.

Regulatory-grade Explainability

Explainable AI dashboards for executives, model cards, and audit logs.

24×7 Support & Enablement

SLAs, runbooks, governance reviews, and training for PMs, finance, and leaders.

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Who We Are

BI that your CFO, CDO, and PMs Equally Trust

Many teams face dashboards that disagree, metrics that drift, and SQL no one can defend. Mobiloitte fixes this with a warehouse/lakehouse core, a semantic metrics layer, and governed pipelines. Causal and predictive analytics sit on top, along with decision automation. Every metric is versioned, tested, and visible, so BI becomes easy to audit and safe to grow.

  • Well coded dbt tests, CI/CD, and visibility across analytics and models.

  • Responsive Embedded pods partner with finance, product, ops, and marketing..

  • Fast growing Scales with teams, events, SKUs, and markets.

  • Multipurpose Dashboards, ML, LLMs, and activation share one foundation.

Mobiloitte’s Comprehensive Advanced Analytics & BI Services

Mobiloitte Aligns KPI Trees, Metric Dictionaries, Semantic Layers, and Governance So No One Argues Over “Which Dashboard Is Right.” Ownership, SLAs, Cost Policies, and Compliance Are Set to Pass Internal and External Audits.

Deliverables:

  • KPI tree and semantic metric catalog

  • Data/metric contracts, lineage, and access rules

  • Warehouse/lakehouse architecture and modeling standards

  • Cost, SLA, and FinOps framework

  • Governance policies and documentation

Mobiloitte Delivers DBT Modelling, Streaming Pipelines, Operational Dashboards, Forecasting, Causal Inference, and Decision Automation. LLMs Add Narratives and NLQ With Grounding and Evals.

What you get

  • dbt models + semantic metrics layer + lineage

  • Kafka/Flink/Spark streaming → instant dashboards and actions

  • Causal inference, uplift modeling, forecasting, and scenario tools

  • Decision engines with policy enforcement (alerts → actions)

  • Narrative BI and NLQ with guardrails and evaluations

Mobiloitte Runs and Improves Analytics With Retraining, Data Quality Checks, CI/CD for BI Codes, FinOps, and Practical Training for Non-Technical Teams.

Included

  • Model registry, retraining jobs, and drift/latency alerts

  • Data observability (freshness, schema, anomalies)

  • FinOps dashboards, budgets, and optimization

  • Cross-functional enablement and playbooks

  • 24/7 SLAs and joint roadmap planning

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The process

How does it Works?

  • 01
    Define & Govern

    Agree on metrics, KPIs, governance, and Architecture. Pick a stack based on cost, control, and integrations.

  • 02
    Build & Operationalize

    Ship pipelines, dbt models, dashboards, forecasting/causal analytics, and decision engines with CI/CD and observability.

  • 03
    Optimize & Automate

    Retrain models safely, tune costs, and automate actions with clear guardrails and audit logs.

Tech we excel at

Snowflake • BigQuery • Redshift • Databricks • Delta/Iceberg/Hudi • dbt • Airflow/Prefect • Kafka/Flink/Spark • Looker • Power BI • Mode • Superset • Cube/Transform • MLflow • W&B • LangChain/LlamaIndex • Feast • ClickHouse • DuckDB

    Compliance & Explainability

    Compliance is built in: SOC2/GDPR/HIPAA/PCI alignment, access control, masking, consent awareness, lineage, model cards, policy automation, and audit logs. XAI views make it simple to explain why models and metrics acted the way they did.

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      Frequently Asked Questions

      How does a semantic metrics layer stop “dashboard wars”?

      Mobiloitte defines metrics once in code with tests and version control. All dashboards, notebooks, models, and activations pull from the same rules. Finance, product, and marketing see the same numbers, and changes roll out everywhere at once.

      • RAG = vector DB + retriever at query time
      • Fine-tuning/LoRA = learn behavior and formats
      • Start with RAG + prompts; add fine-tuning for stable behavior or scale
      What’s the advantage of causal inference over traditional analytics?

      Correlation shows relationships; causality shows impact. Causal models and uplift analysis reveal which actions truly move KPIs. This helps teams invest in strategies that create real lift, not just nice-looking charts.

      • Metrics: groundedness, recall@k, task accuracy, toxicity
      • Methods: hybrid retrieval, constraints, JSON/schema checks, re-ranking
      • Ongoing evals + human review
      Do they support real-time dashboards and alerts?

      Yes. Kafka/Flink/Spark streams power minute-level views, anomaly alerts, and safe automatic actions. Observability, SLAs, and playbooks keep real-time reliable

      • Llama/Mistral with vLLM/Triton/Ray
      • Weaviate, Milvus, pgvector, OpenSearch
      • SOC2, HIPAA, GDPR alignment
      Can LLMs be used safely inside BI workflows?

      Yes. Grounded NLQ and narratives speed insight, while guardrails, evaluations, and PII controls protect accuracy and privacy. Cost and latency are monitored like any production service.

      • Route easy vs. hard queries
      • Semantic/exact cache; shorter contexts
      • Budgets, alerts, team chargebacks
      How are forecasting models made trustworthy?

      Clear models (Prophet/ARIMA) and advanced ML/DL are paired with confidence bands, backtests, and plain-language explanations. Results tie to business outcomes like inventory, staffing, and cash-flow risk.

      • Track prompts, tools, datasets, indexes, models
      • Measure hallucination, toxicity, groundedness, cost/latency
      • Red-teaming and safety policies
      What does decision automation mean here?

      Analytics can trigger policy-based actions, discounts, budget shifts, throttles, or alerts with human override. Every action is logged, understandable, and reversible.

      • Metrics: accuracy, precision/recall, groundedness, instruction-following
      • Human review for high-stakes tasks
      • Regression tests for prompts, retrievers, models
      How are analytics costs controlled as usage scales?

      A FinOps framework tracks costs by team and workload. Heavy queries are redesigned; partitioning, caching, and vectorisation keep spending in check. Storage and compute choices follow growth needs, not vendor hype.

      • Language-specific evals
      • Custom dictionaries/ontologies
      • Route to best model per language
      Can governance be added to an existing BI mess?

      IYes. Mobiloitte centralises metrics into a catalogue and semantic layer, removes duplicates, and migrates logic to tested dbt models. CI/CD manages changes without stopping the business.

      • Sanitize external content
      • Schema/regex/Pydantic checks
      • Continuous attack simulation
      • Least-privilege tools
      Do they support on-prem or sovereign deployments?

      Yes. Open-source stacks can run on-prem or in a private cloud with full control of data, models, and policies without losing modern features.

      • Pinecone: managed speed, higher TCO
      • Weaviate: feature-rich hybrid, open-source/managed
      • pgvector: simple Postgres path for mid-scale
      • Milvus/Zilliz: high-scale, GPU-friendly
      How can non-technical teams self-serve without chaos?

      Role-based dashboards, a semantic layer, and safe explore surfaces give speed without metric drift. Training covers interpretation, experimentation, and governance.

      • Abstractions (LangChain/LlamaIndex or custom services)
      • Decoupled RAG parts (retrievers, rankers, indexers)
      • Infra-as-code for portability
      What is the typical MVP timeline?

      A governed MVP (semantic layer + core dashboards) usually ships in 4–8 weeks. Streaming, forecasting, causal inference, and automation follow in 8–12+ weeks, released in stages for quick wins.

      • Automated and manual tests
      • Explainability and lineage
      • GDPR, SOC2, HIPAA, PCI alignment
      How is success measured for BI modernisation?

      Mobiloitte tracks adoption, decision cycle time, data trust scores, alert quality, experiment throughput, and revenue/efficiency gains from automated decisions. BI is built to pay for itself and the team shows how.

      • 2-4 weeks: discovery, architecture, governance, ROI
      • 4-8 weeks: MVP (RAG/LLM app + evals/guardrails)
      • 8-12 weeks: hardening, scale, optimization, docs, training

      Did you not get your answer? Email Us Now!

      That's right

      Turn BI into a competitive weapon

      Dashboards alone are not enough. Mobiloitte delivers an analytics backbone that moves KPIs, not just reports them, with streaming, predictive, explainable, and governed workflows.