AutoML Platform | krtrimaIQ Cognitive Solutions

No code platform for Enterprise Analytics

Analytics Delivered Via Managed Services

Testimonials

“Reduced false positives by 65% in credit card fraud detection application within a month”

Bank Operating in Nigeria

Challenges Faced

More than 80% of traditional AI projects fail before delivering measurable outcomes—often because of poor data infrastructure, skills shortages, and long development timelines

From silod healthcare data to messy transactional data, analysts across industries spend the majority of their time integrating and cleaning the data, rather than focusing on generating actionable insights

Traditional tools require extensive coding expertise or long setup times just to visualize a few charts — delaying critical decisions in time-sensitive operations.

Teams rely on static reports that miss key insights like outliers, trends, or variable impact slowing smart decisions.

How Traditional Data Preperation Hurts

Inflexible Toolchains

Traditional analytics stacks require switching between tools for cleaning, exploration, and modeling causing workflow disruptions and integration hassles.

No Context for Variables

Legacy tools often miss feature importance and data relationships, leaving teams guessing which variables really influence healthcare outcomes.

Manual Data Preparation Overload

Analysts often spend hours on repetitive cleaning and structuring tasks — slowing down insights and wasting valuable time meant for diagnosis and action.

Delayed Risk Identification

Without real-time profiling and anomaly detection, critical patient risks and trends go unnoticed until it's too late, affecting both outcomes and decisions.

Introducing MLAutomata: A Trustworthy Ally For Data scientists

MLAutomata is a no-code platform that helps businesses adopt machine learning faster, without the need for deep technical skills.

Smarter ML Made Simple

From data prep to model deployment, MLAutomata offers ready-to-use tools, built-in explainability, and expert support to simplify the entire ML workflow.

Benefits Of MLAutomata

Faster & Smarter Decision Making

From pre-processing through profiling, MLAutomata compresses weeks of prep into minutes, surfacing critical features early so healthcare professionals can act on insights right away.

No Code, No Install

Access MLAutomata through a cloud-based interface no setup required, and no technical expertise needed. Start exploring, analyzing, and predicting without writing a single line of code.

Out Of The Box Explainability

MLAutomata utilizes SHAP values to reveal feature contributions, offering global and local explanations, meeting regulatory compliance.

Analytics as a Service Support

MLAutomata supports teams through an on-demand managed service model, offering expert guidance and analytics services whenever you need them.

Custom Training for Teams

MLAutomata offers training services catering to organization specific needs.

Pre Built Solutions

Regression

Classification

Clustering

Regression

Classification

Clustering

Time Series Analysis

Anomaly Detection

Recommendation System

Predictive Model Life Cycle Management Solution

Case Studies

Deployment Of Credit Card Fraud Detection Algorithms For A Bank Based in Nigeria

Client's existing suite of solutions are based on traditional rules engine that raise alarms, which need to be investigated further manually. The more alerts raised, higher the effort to investigate and conclude. The main aim of this model is to reduce the number of false positives generated from the Rules Engine model. The solution includes exploratory data analysis, statistical analysis, classification and anomaly detection techniques to reduce false positives and detect fraudulent transactions with higher accuracy.

False Positive Reduction (FPR) model has reduced the number of false positives by 73%. This will significantly reduce the efforts of the investigators and also in reducing missing all the suspicious or fraudulent transactions.

Promotion Mix Modelling

Today brand teams have many different channles and tactics at their disposal for building brand equity and boosting sales. However, with the increasing complexity of the promotional landscape it becomes critical to make data-driven decisions about how much to invest in each channel.

PMM informs marketing strategy and tactics by helping brand teams understand the effectiveness of each marketing channel such as advertising, in-store, direct-to-customer (DTC), digital and social media

Client's existing suite of solutions are based on traditional rules engine that raise alarms, which need to be investigated further manually. The more alerts raised, higher the effort to investigate and conclude. The main aim of this model is to reduce the number of false positives generated from the Rules Engine model. The solution includes exploratory data analysis, statistical analysis, classification and anomaly detection techniques to reduce false positives and detect fraudulent transactions with higher accuracy.
False Positive Reduction (FPR) model has reduced the number of false positives by 73%. This will significantly reduce the efforts of the investigators and also in reducing missing all the suspicious or fraudulent transactions.
Today brand teams have many different channles and tactics at their disposal for building brand equity and boosting sales. However, with the increasing complexity of the promotional landscape it becomes critical to make data-driven decisions about how much to invest in each channel.
PMM informs marketing strategy and tactics by helping brand teams understand the effectiveness of each marketing channel such as advertising, in-store, direct-to-customer (DTC), digital and social media