Analytical Excellence Since 1996

Driving Business SUccess through
Marketing ANalytics

Suman API Gateway

High-performance API access to proprietary algorithmic engines.

Regularized Quantile Regressions

Advanced quantile-based regularized regressions performing multi-quantile optimization. Outperforms Quantile Regression and Quantile Boosting.

See Predictions & ROI Proof → View API Specs →
Fully Deployed. Provides robust quantile error minimization, beating best-in-class predictive models by a wide margin.

Extremely Robust Regressions

Maximally-Robust Linear Model for accurate ROI and elasticity estimates. Outperforms Quantile and HuberT.

See Predictions & ROI Proof → View API Specs →
Fully Deployed. Provides superior robustness against outliers for sensitive business metrics.

Doubly Robust Regularized Regressions

Engineered to provide robust predictive modeling with resilience to extreme data and missing values.

See Predictions & ROI Proof → View API Specs →
Fully Deployed. Mathematical framework remains stable to outliers, extreme data, and missing values.

Advanced Hybrid Factor Analysis

Continuous and discrete factor decomposition for hybrid datasets.

See Predictions & ROI Proof → View API Specs →
Fully Deployed. High-precision decomposition of mixed-scale factors.

Stochastic K-mcube Honest Clustering

Batch-based stochastic partitioning for Big Data containing millions+ data points with mixed variable types.

See Complexity & Proof → View API Specs →
Fully Deployed. Scales up naturally for large-scale enterprise datasets.

K-mcube Honest Clustering

State-of-the-art O(N*k*P) execution speed for mixed datasets containing Numeric, Rank, and Categorical variables.

See Complexity & Proof → View API Specs →
Fully Deployed for immediate use. Bypasses intractable O(N²) distance calculations.

The Foundation

Decades of expertise in Marketing Science and Econometrics.

Grounding corporate strategy in mathematical systems.

EZPredict™

High precision predictive modeling for categorical and continuous outcomes.

Used for customer prospecting and risk decisions where high accuracy is the primary ROI driver.

EZMap™

Identifies unique brand axes and positioning opportunities.

Deploy when re-assessing brand strategy in response to new competitive market entrants.

EZSegment™

Goal-based approach leveraging weighted hybrid clustering.

Identify targetable market segments for product design, placement, and pricing.

EZCompete™

Identifies SKU-level threats and quantifies susceptibility.

Essential for establishing defenses against market share erosion.

EZPromo™

Analyzes lift and profitability to clarify sales volume trade-offs.

Optimizes promotional spend by identifying which strategies drive true incremental profit.

Extremely Robust Regressions

Minimizing Median Absolute Deviations (MAD) to Eliminate Financial Distortion

While Ordinary Least Squares (OLS) squares errors—allowing a single extreme outlier to hijack the entire solution—our engine strictly minimizes the median of the absolute deviations (MAD), consistently outperforming HuberT and QuantileReg.

1. Stackloss Data

Chemical plant operational sensor spikes.

Coeff OLS Huber QuantReg Ext.Rob
const -39.900 -41.000 -39.700 -37.100
AIRFLOW 0.720 0.830 0.830 0.850
WATERTEMP 1.300 0.930 0.570 0.120
ACIDCONC -0.150 -0.130 -0.060 0.000
RobustR2 0.782 0.814 0.831 0.894

2. House Prices

Ames housing valuation skews.

Coeff OLS Huber QuantReg Ext.Rob
const -85.00k -78.00k -72.00k -65.00k
LotArea 0.650 0.710 0.750 0.820
OverallQual 27.40k 25.80k 24.90k 23.50k
GrLivArea 56.890 59.200 61.500 64.300
RobustR2 0.521 0.542 0.548 0.583

3. Wine Quality

Baseline error ratios and robust R2.

Metric OLS Huber QuantReg Ext.Rob
Error Ratio 0.160 0.160 0.150 0.112
RobustR2 0.841 0.844 0.852 0.891
Industrial Operations

Preventing False Alarms & Wasted Capital

A single faulty SCADA sensor or extreme outlier shouldn't distort your baseline cost-efficiency model. MAD minimization ensures your operating budget targets structural inefficiencies—not transient noise.

Bottom-Line Impact:

Stops costly false alarms and unnecessary emergency maintenance dispatches.

Real Estate & Lending

Surviving Distressed Comps

Foreclosures, fire sales, and luxury tear-downs routinely corrupt standard valuation models. Lock in true baseline market values to price portfolios accurately without being blindsided by localized anomalies.

Bottom-Line Impact:

Protects mortgage underwriting and collateral pricing from severe tail-risk distortions.

Viticulture & Winemaking

Isolating True Vintage Drivers

Strip away subjective sommelier scoring bias and extreme weather anomalies to identify the exact chemical and process factors that dictate true market value and product quality.

Bottom-Line Impact:

Directs R&D and QA spending toward variables that actually drive margin and consumer preference.

Integrate the Extremely Robust Regressions API directly into your analytics pipeline within minutes.

Step 1: Explore Interactive API Documentation

Examine request schemas, JSON payloads, and test parameters live via ReDoc.

Open API Specs →

Step 2: Make programmatic HTTP requests

Send JSON feature matrices to the production endpoint for instantaneous robust scoring:

curl -X 'POST' \ 'https://suman-mdad-regression.onrender.com/predict' \ -H 'accept: application/json' \ -H 'Content-Type: application/json' \ -d '{ "features": [1.20, 0.50, -0.80] }'

Doubly Robust Regularized Regressions

Robustness to Extreme Data and Missing Values for Unbiased Predictive Modeling

While standard Lasso regressions return undefined metrics under extreme data corruption and missing values, our Doubly Robust Regularized engine maintains structural integrity, delivering reliable coefficients and superior fit metrics (rounded to 3 decimal places).

1. Advertising Dataset

Normalized scale coefficients and fit measures.

Feature Lasso BCR (Doubly Robust)
TV 0.740 0.710
Radio 0.530 0.500
Newspaper 0.000 0.000
Fit Measure Lasso BCR
Mean Absolute Error 0.242 0.238
R-square --- 0.902
Deviation Accounted For --- 0.714

2. Vehicle Valuation Dataset

Normalized scale, no missing data.

Feature Lasso Suman Inc.
Mileage -0.190 -0.220
Weight 0.560 0.540
Engine Size -0.540 -0.540
HP 0.540 0.570
Fit Measure Lasso Suman Inc.
Mean Absolute Error 0.452 0.446
R-square --- 0.612
Deviation Accounted For --- 0.421
Car Price Predictions

Surviving Odometer Rollbacks and Incomplete Listings

Real-world pre-owned vehicle datasets are riddled with extreme valuation anomalies and massive gaps in optional feature descriptions. Standard models either throw out valuable rows or let outliers warp the entire pricing curve.

Bottom-Line Impact:

Automatically neutralizes extreme data distortions and circumvents missing data.

Advertising Impact on Sales

Shielding Media Attribution from Tracking Gaps

Modern omnichannel marketing datasets frequently suffer from broken tracking tags and erratic seasonal spend spikes. Standard regression algorithms collapse when faced with these gaps.

Bottom-Line Impact:

Delivers crystal-clear media mix attribution by immunizing ROI models against missing touchpoints and spend outliers.

Integrate the Doubly Robust Regularized Regressions API directly into your analytics pipeline within minutes.

Step 1: Explore Interactive API Documentation

Examine request schemas, JSON payloads, and test parameters live via ReDoc.

Open API Specs →

Step 2: Make programmatic HTTP requests

Send JSON feature matrices to the production endpoint for instantaneous robust scoring:

curl -X 'POST' \ 'https://drr-regression.onrender.com/predict' \ -H 'accept: application/json' \ -H 'Content-Type: application/json' \ -d '{ "features": [1.20, 0.50, -0.80] }'

Regularized Quantile Regressions

Enterprise-grade multi-quantile optimization engine for robust distributional forecasting.

Demonstrating superior error minimization by our Algorithm across both lower and extreme upper tails compared to current best-in-class methods (QuantileReg, LightGBM, and XGBoost). (Click any chart to enlarge)

Housing Dataset

Housing Dataset Benchmark

Superior lower-tail baseline control and halved absolute error in the upper tail.

Retail Sales Dataset

Retail Sales Dataset Benchmark

Maintains tight error bounds at low quantiles while suppressing tail expansion.

Synthetic Simulation

Synthetic Dataset Benchmark

Flawless error minimization across the entire spectrum from lower to upper quantiles.

Leverage quantile profiling to transition from average-case approximations to targeted, risk-adjusted decision-making.

Customer Lifetime Value (CLV)

Predict upper-quantile customer cohorts (Q ≥ 0.85) to isolate high-value VIP segments, optimizing luxury acquisition budgets and retention incentives without wasting spend on median averages.

Financial Risk & Exposure

Quantify extreme downside risks and tail-end credit defaults by modeling lower and upper error bounds accurately under severe economic volatility or market shocks.

Inventory & Demand Forecasting

Prevent stockouts during peak surges by targeting upper quantiles for safety stock, while minimizing overstock holding costs at lower quantiles during sluggish demand periods.

Integrate the Regularized Quantile Regression API directly into your corporate analytics infrastructure within minutes.

Step 1: Explore Interactive API Documentation

Examine request schemas, JSON payloads, and test parameters live via ReDoc.

Open API Specs →

Step 2: Make programmatic HTTP requests

Send JSON feature matrices to the production endpoint for instantaneous multi-quantile scoring:

curl -X 'POST' \ 'https://suman-regularized-quantile-regression.onrender.com/predict' \ -H 'accept: application/json' \ -H 'Content-Type: application/json' \ -d '{ "features": [1.20, 0.50, -0.80], "quantiles": [0.10, 0.50, 0.90] }'

Advanced Hybrid Factor Analysis

Rigorous multiway data decomposition and psychometric modeling frameworks

Recommendation Engine Benchmark Results

Model Architecture Test RMSE Test R²
Hybrid Factor Analysis: OLS Regression 0.989 0.226
Hybrid Factor Analysis: Random Forest Regressor 1.045 0.135
Hybrid Factor Analysis: Gradient Boosting Regressor 1.011 0.191
Hybrid Factor Analysis: AdaBoost Regressor 1.098 0.045
FastFM SGD Regressor (no HFA) 1.130 -0.012
FastFM ALS Regressor (no HFA) 1.040 0.143
FastFM MCMC Regressor (no HFA) 1.007 0.198
myFM Regressor (no HFA) 1.003 0.204
Transformer (no HFA) 1.117 0.204
Recommendation Engines

Superior Product & Content Personalization

By feeding HFA-derived embeddings into clean regression layers, platforms can predict user product ratings and preferences with significantly higher test accuracy than black-box deep learning or standard Factorization Machines.

Bottom-Line Impact:

Boosts click-through and conversion rates by eliminating noisy recommendation errors.

GenAI & Agentic Workflows

High-Precision Structured Embeddings

Modern GenAI agents require rich structural encodings that merge user demographics, geographic context, and product attributes without losing mathematical interpretability or suffering from data sparsity.

Bottom-Line Impact:

Provides AI agents with robust memory and customer context priors for hyper-targeted interactions.

Cross-Sectional Promotion Analysis

Multi-Attribute Promotional Lift & Response Modeling

Seamlessly synthesize heterogeneous survey data, user location metrics, and behavioral profiles into cohesive latent vectors that isolate true promotional sensitivity and price elasticity.

Bottom-Line Impact:

Identifies which promotional channels and offers drive incremental volume rather than cannibalizing baseline sales.

Integrate the Advanced Hybrid Factor Analysis API directly into your analytics pipeline within minutes.

Step 1: Explore Interactive API Documentation

Examine request schemas, JSON payloads, and test parameters live via ReDoc.

Open API Specs →

Step 2: Make programmatic HTTP requests

Send JSON feature matrices to the production endpoint for instantaneous hybrid factor scoring:

curl -X 'POST' \ 'https://suman-hfa-optimized.onrender.com/predict' \ -H 'accept: application/json' \ -H 'Content-Type: application/json' \ -d '{ "features": [1.20, 0.50, -0.80] }'

K-mcube Honest Clustering

State-of-the-Art Speed & Math Rigor for Mixed-Scale Data

Clustering mixed data honestly without distorting variable scales while maintaining mathematical efficiency.

Theoretical Complexity Lower Bound

State-of-the-Art O(N · k · P) Speed

Where N is data points, k is clusters, and P is variables. Because exact clustering is NP-Hard, execution faster than O(N · k · P) does not exist.

Distance-Based Failure:

Distance matrix clustering is a non-starter at O(N²). DBSCAN is typically O(N log N) and O(N²) worst-case.

Scale Integrity

Honest Variable Treatment

Rigorously preserves individual measurement scales by honestly treating Numeric, Rank, and Categorical variables as they should be—without forcing arbitrary numeric conversions.

Granular Evaluation:

Explicit fit measures are independently calculated and reported across each distinct variable type.

Batch-based stochastic clustering naturally scales up to massive datasets containing millions+ data points. Here is how honest mixed-data clustering drives enterprise value:

Market Segmentation

Customer Profiling at Scale

Analyze millions of data points across numeric (spend), categorical (region), and rank (loyalty tier) variables simultaneously. Stochastic batching ensures natural scale-up without memory overflow.

Fraud Detection

Identifying Anomalous Clusters

Detect irregular transaction patterns in real-time by clustering massive mixed-type datasets, relying on the state-of-the-art O(N·k·P) execution speed to isolate fraudulent groupings instantly.

Product Assortment

Optimizing SKU Placement

Group diverse product catalogs using rank (popularity) and categorical (department) data combined with numeric metrics (margin), allowing honest distance calculations to dictate natural assortment clusters.

Deploy Honest Clustering or Stochastic Honest Clustering directly into your production pipelines.

Step 1: Explore Interactive API Documentation

Access Swagger and ReDoc interactive specifications for both clustering engines:

Step 2: Make programmatic HTTP requests

Send JSON payloads containing mixed numeric, categorical, and rank features:

curl -X 'POST' \ 'https://suman-honest-clustering.onrender.com/predict' \ -H 'accept: application/json' \ -H 'Content-Type: application/json' \ -d '{ "data": [ {"numeric_var": 42.5, "rank_var": 2, "category_var": "Segment_A"} ] }'
Expanded Benchmark View