Proprietary BI and data warehouse licenses drain IT budgets while trapping enterprise telemetry in closed silos. Nisarga engineers Zero-Lock-In Open-Source Analytics & Decision Intelligence Platforms—unifying structured ERP databases and unstructured AI-DMS extracted tables across all five tiers of analytical maturity: Descriptive, Diagnostic, Predictive, Prescriptive, and Cognitive / Autonomous.
Each analytical tier answers a fundamentally different class of enterprise question, requiring a distinct mathematical formulation, open-source compute engine, and AI model architecture.
Real-Time State Aggregation, Semantic Metric Layers & Sub-Second OLAP
Technical Formulation: Transforms high-velocity transactional streams (CDC logs, IoT telemetry, and AI-DMS extracted SQL tables) into governed dimensional models (Star/Snowflake schemas) and pre-aggregated HyperLogLog / quantile rollups.
How AI Elevates It: Instead of static SQL views, LLM-powered Semantic Metric Layers translate natural-language executive questions into dialect-accurate SQL over governed KPI definitions—eliminating metric drift across finance, supply chain, and sales.
Automated Root-Cause Analysis (RCA), Causal Discovery & Anomaly Attribution
Technical Formulation: Moves beyond correlation ($R^2$) to structural causal inference. Constructs Directed Acyclic Causal Graphs (DAGs), runs multi-dimensional OLAP drill-downs, and computes SHAP (Shapley Additive Explanations) values to isolate which exact dimensions drove a KPI variance.
How AI Elevates It: Unsupervised Isolation Forests, Variational Autoencoders (VAEs), and Structural Causal Models automatically traverse millions of dimensional combinations in seconds to surface true root causes (e.g., isolating whether a margin drop stemmed from vendor freight surcharges, SKU mix shift, or regional machine downtime).
Probabilistic Time-Series Forecasting, Survival Analysis & Deep Tabular ML
Technical Formulation: Estimates conditional probability distributions P(Yt+k | X1..t) across multi-horizon time series and tabular feature stores, outputting calibrated P10 / P50 / P90 quantile prediction intervals rather than brittle single-point guesses.
How AI Elevates It: Combines gradient-boosted decision trees (LightGBM / CatBoost) with zero-shot Time-Series Foundation Models (Chronos-T5, TimesFM, Lag-Llama, Temporal Fusion Transformers) to forecast demand spikes, equipment Remaining Useful Life (RUL), credit default risk, and customer churn.
Mathematical Operations Research, Stochastic Simulation & Contextual Bandits
Technical Formulation: Converts predictive probability distributions into mathematically optimal action policies by solving Mixed-Integer Linear Programs (MILP), Constraint Satisfaction Problems, and Monte Carlo Markov Decision Processes:
min cᵀx subject to Ax ≤ b, x ∈ ℤⁿ × ℝᵐ
How AI Elevates It: Pairs Operations Research solvers (Google OR-Tools, Pyomo) with Contextual Multi-Armed Bandits and Bayesian Optimization to compute optimal inventory safety stocks, vehicle routing schedules, dynamic pricing tiers, and hospital shift allocations under real-world budget and capacity constraints.
Multi-Agent Reasoning Loops, Unstructured + Structured Fusion & Closed-Loop Action
Technical Formulation: Unifies structured lakehouse tables with unstructured enterprise knowledge (contracts, emails, 99.9% VLM-extracted documents, and knowledge graphs). Stateful ReAct / Plan-and-Execute LLM Agents autonomously decompose goals, write and execute SQL/Python in sandboxed runtimes, invoke prescriptive solvers, and trigger downstream ERP/API transactions.
Self-Learning Governance: Operates within constitutional policy guardrails—executing low-risk remediations autonomously (e.g., auto-rebalancing cloud workloads, blocking fraudulent transactions, issuing replenishment POs) while escalating high-impact decisions with full causal & citation provenance.
A sovereign, vendor-neutral Medallion Lakehouse and Multi-Agent AI architecture that runs on Kubernetes across on-premises bare metal or any cloud VPC.
dbt Core)How data inputs, algorithmic models, open-source tooling, and decision latency evolve across the five analytical stages.
| Analytical Tier | Core Question | Mathematical & AI Models | Open-Source Tooling | Enterprise Decision Mode |
|---|---|---|---|---|
| 1. Descriptive | What happened? | OLAP cube rollups, window functions, HyperLogLog sketches, Semantic SQL mapping. | ClickHouse, Iceberg, Trino, dbt, Apache Superset | Human-driven reporting & live KPI monitoring |
| 2. Diagnostic | Why did it happen? | Structural Causal Models (SCM), SHAP feature attribution, Isolation Forests, VAEs. | DoWhy, EconML, SHAP, PyOD, Apache Pinot | Automated root-cause isolation & variance attribution |
| 3. Predictive | What will happen? | Time-Series Foundation Models, Gradient Boosted Trees, Survival & Hazard models. | Chronos-T5, TimesFM, LightGBM, PyTorch TFT, MLflow | Probabilistic P10/P50/P90 forecasting & early warning |
| 4. Prescriptive | How should we optimize? | Mixed-Integer Linear Programming (MILP), Monte Carlo simulation, Contextual Bandits. | Google OR-Tools, HiGHS, Pyomo, Optuna, Ray RLlib | Constraint-validated optimal action recommendations |
| 5. Cognitive / Autonomous | How do we reason, act & self-adapt? | Multi-Agent ReAct LLMs, GraphRAG, Tool-Calling Solvers, Continuous RLHF/DPO loops. | vLLM, Qwen2.5 / Llama-3.3, LangGraph, Qdrant, Neo4j | Closed-loop autonomous execution with policy guardrails |
Partner with Nisarga's data & AI architects to deploy an open-source Lakehouse and 5-tier AI analytics platform tailored to your enterprise.