Author: Samir

  • AI-Driven Risk Modeling and Fraud Prevention for a PE-Backed Fintech Platform

    AI-Driven Risk Modeling and Fraud Prevention for a PE-Backed Fintech Platform

    Industry: Fintech / Financial Services

    At a glance: MyntiQ Tech built a real-time AI risk and fraud engine for a private equity-backed digital lender. It scores borrowers with alternative data such as cash flows and utility history, and detects fraud with device fingerprinting and behavioral biometrics. It returns decisions in milliseconds and automates over 85% of them.

    Overview

    This case study details the deployment of an AI-driven risk modeling and real-time fraud prevention engine for a private equity-backed digital lending platform. By applying machine learning to multi-source financial data, the system flags fraudulent applications and calculates borrower risk profiles instantly.

    Business Problem

    The lending platform was constrained by high risk metrics:

    • Traditional, static credit scoring locked out credit-worthy borrowers with thin files, limiting user growth.
    • The platform was targeted by sophisticated loan application fraud, causing financial write-offs.
    • Manual underwriting reviews created transaction bottlenecks, causing customer drop-off.

    How MyntiQ Tech Helped

    MyntiQ Tech built a smart underwriting and security framework:

    • Advanced Risk Assessment: Trained machine learning models on alternative data sources, including transactional cash-flows and utility histories, to assess borrower risk accurately.
    • Behavioral Fraud Detection: Integrated device fingerprinting, behavioral biometrics, and IP verification to flag automated bots and stolen identities.
    • Real-Time Decisions: Built an API engine that processes application queries in milliseconds, approving low-risk users instantly.

    Business Outcomes Delivered

    • Reduced loan default rates significantly, protecting capital assets.
    • Increased applicant approval rates without raising overall risk exposure.
    • Identified and blocked fraudulent loan applications, saving millions in potential write-offs.
    • Automated over 85% of decisions, speeding up customer access to credit.

    Frequently Asked Questions

    How can AI score borrowers with thin credit files?
    Machine learning models trained on alternative data, such as transactional cash flows and utility payment history, assess risk for borrowers that traditional credit scores lock out.

    How does a digital lender detect loan application fraud?
    Device fingerprinting, behavioral biometrics and IP verification flag automated bots and stolen identities before an application is approved.

    How fast are credit decisions?
    The API decision engine processes applications in milliseconds, approves low-risk applicants instantly, and automates over 85% of decisions.

    Building lending or fintech AI? Talk to MyntiQ Tech or browse more case studies.

  • Predictive Maintenance and Quality Inspection in Manufacturing using AI

    Predictive Maintenance and Quality Inspection in Manufacturing using AI

    Industry: Manufacturing (Automotive)

    At a glance: MyntiQ Tech deployed computer vision and IoT analytics in an automotive parts plant. CNN-based cameras flag micro-defects on the line in milliseconds. Vibration, current and temperature sensors forecast machine failures up to two weeks ahead and trigger maintenance work orders in the MES.

    Overview

    This case study details the deployment of computer vision and IoT sensor models in an automotive parts assembly facility. The system automates quality control inspection on high-speed conveyor belts and predicts machine failures before they disrupt production schedules.

    Business Problem

    The manufacturing plant suffered from operational delays:

    • Unplanned robotic arm failures stopped production, costing thousands of dollars in lost manufacturing capacity.
    • Manual quality control inspection was slow and prone to human error, occasionally allowing micro-defects to slip through to assembly.
    • Data from machine vibration and temperature sensors went unmonitored, limiting maintenance to static calendars.

    How MyntiQ Tech Helped

    MyntiQ Tech built a smart industrial monitoring solution:

    • Computer Vision Inspection: Mounted high-speed industrial cameras on the assembly line, using convolutional neural networks (CNNs) to flag surface micro-scratches and structural defects in milliseconds.
    • Predictive Maintenance Engine: Connected vibration, current, and temperature sensors on critical machines to predictive analytics models, forecasting mechanical failures up to two weeks in advance.
    • Industrial IoT Integration: Integrated sensor feeds with the plant’s Manufacturing Execution System (MES) to trigger automatic work orders for maintenance crews.

    Business Outcomes Delivered

    • Reduced unplanned machinery downtime, protecting daily production quotas.
    • Increased quality inspection throughput, processing parts continuously.
    • Reduced defective parts escaping assembly to zero, eliminating related recall costs.

    Frequently Asked Questions

    How does AI inspect parts on a production line?
    High-speed industrial cameras feed convolutional neural networks (CNNs) that flag surface micro-scratches and structural defects in milliseconds, without stopping the conveyor.

    How far ahead can predictive maintenance forecast failures?
    In this plant, models using vibration, current and temperature sensor data forecast mechanical failures up to two weeks in advance.

    How do predictions become maintenance work?
    Sensor feeds are connected to the plant’s Manufacturing Execution System (MES), which creates work orders for maintenance crews automatically.

    Exploring AI or IoT for your plant? Talk to MyntiQ Tech or browse more case studies.

  • AI-Powered Customer Support Automation for a Global E-Commerce Platform

    AI-Powered Customer Support Automation for a Global E-Commerce Platform

    Industry: E-Commerce / Retail

    At a glance: MyntiQ Tech deployed an LLM-powered support assistant for a global e-commerce platform. It is trained on the brand’s policies and connected to Shopify and FedEx APIs for live order tracking. Basic questions, which made up over 70% of tickets, are automated, and sentiment analysis hands frustrated customers to human agents.

    Overview

    This case study details the implementation of an LLM-powered customer support chatbot for an international e-commerce platform. Integrated with inventory and logistics databases, the AI assistant automates customer inquiries and handles order-tracking updates at scale.

    Business Problem

    The e-commerce business faced scaling issues:

    • Holiday traffic spikes caused massive queues, leading to customer delays and cart abandonment.
    • Over 70% of support tickets were basic questions like “Where is my order?” or “How do I process a return?”
    • Staff spent hours copying tracking numbers, leaving little time for complex shipping disputes.

    How MyntiQ Tech Helped

    MyntiQ Tech built a smart conversational AI support system:

    • LLM Dialog Engine: Deployed an LLM trained on the brand’s policy docs to answer product and return questions accurately.
    • Real-Time API Integrations: Connected the chatbot with Shopify and FedEx APIs, allowing it to retrieve and share live tracking updates securely.
    • Sentiment Analysis Routing: Implemented sentiment analysis to transfer frustrated customers to human agents, along with the conversation log.

    Business Outcomes Delivered

    • Auto-resolved a high percentage of basic customer support tickets.
    • Reduced median support ticket response times, improving customer satisfaction metrics.
    • Protected support margins during high-traffic seasons without requiring temporary staff.

    Frequently Asked Questions

    Can an AI chatbot answer “Where is my order?”
    Yes. The assistant MyntiQ Tech built connects to Shopify and FedEx APIs to retrieve and share live tracking updates securely.

    How does the chatbot know store policies?
    The LLM is trained on the brand’s policy documents, so it answers product and return questions in line with those policies.

    When does the AI hand over to a human agent?
    Sentiment analysis spots frustrated customers and transfers them to a human agent along with the full conversation log.

    Scaling customer support with AI? Talk to MyntiQ Tech or browse more case studies.

  • AI-Based Medical Card Data Extraction for Appointment Scheduling

    AI-Based Medical Card Data Extraction for Appointment Scheduling

    Industry: Healthcare

    At a glance: MyntiQ Tech built an AI document extraction system that reads patient insurance cards from phone photos. OCR and machine learning pull out the insurer, member ID, group number and copay, which are pushed into the EHR through an API and checked against clearinghouses, speeding up check-in and reducing claim errors.

    Overview

    This case study outlines the development of an intelligent, AI-powered document extraction system designed to read and process patient medical insurance cards automatically. Integrated with a healthcare network’s scheduling portal, the solution automates patient check-in and data entry into Electronic Health Record (EHR) databases.

    Business Problem

    The healthcare provider faced operational slowdowns:

    • Clinic staff had to manually type patient and insurance details from physical card photos, leading to frequent typos.
    • Insurance verification was slow, resulting in billing errors and delayed approvals.
    • Entering data manually during peak check-in hours created long wait times in clinic lobbies.

    How MyntiQ Tech Helped

    MyntiQ Tech built a secure, AI-powered extraction pipeline:

    • Intelligent Document Processing: Built an OCR (Optical Character Recognition) engine trained to read insurance cards, correcting for low lighting or angled phone photos.
    • Structured Field Extraction: Developed machine learning models to extract fields like insurer name, member ID, group number, and copay details.
    • EHR Integration: Built an API that pushes structured data directly into patient profiles in the EHR system.
    • Insurance Check API: Integrated with clearinghouses to verify coverage status instantly.

    Business Outcomes Delivered

    • Reduced patient details entry times, allowing faster clinic check-ins.
    • Eliminated spelling and ID transcription errors, reducing billing claims rejections.
    • Improved patient intake, freeing clinic staff to focus on patient care.

    Frequently Asked Questions

    Can AI read insurance cards from phone photos?
    Yes. The OCR engine MyntiQ Tech built is trained on insurance cards and corrects for low light and angled phone photos.

    Which fields are extracted from a medical insurance card?
    Machine learning models extract the insurer name, member ID, group number and copay details as structured fields.

    How does extracted data reach the EHR?
    An API pushes the structured data straight into the patient’s EHR profile, and a clearinghouse integration verifies coverage instantly.

    Automating patient intake? Talk to MyntiQ Tech or browse more case studies.

  • Enterprise Data Warehouse & Reporting Modernization for a PE-Backed Pest Control Services Organization

    Enterprise Data Warehouse & Reporting Modernization for a PE-Backed Pest Control Services Organization

    Industry: Enterprise Services / Pest Control

    At a glance: MyntiQ Tech built a centralized data warehouse for a private equity-backed pest control company. It consolidates data from the different field-service systems used by acquired branches, and automated ETL with executive, operational and route dashboards replaced weekly spreadsheets with daily reporting.

    Overview

    This case study details the modernization of the reporting infrastructure for a private equity-backed pest control services organization. By consolidating operational data from various local offices into a unified data warehouse, MyntiQ Tech helped the client replace manual spreadsheets with automated, enterprise-grade dashboards.

    Business Problem

    Rapid growth through acquisitions led to severe operational challenges:

    • Acquired branches used different field-service software, leading to fragmented reporting.
    • Consolidating billing, field route efficiency, and customer retention data took weeks of manual work.
    • The Private Equity sponsors lacked real-time visibility into overall portfolio performance and profit margins.
    • Local branch managers could not track daily field routes or customer feedback easily.

    How MyntiQ Tech Helped

    MyntiQ Tech built a modern analytics pipeline:

    • Data Warehouse Integration: Constructed a centralized data warehouse that pulls information from multiple field ERP systems.
    • Automated Data Cleaning: Implemented ETL pipelines that clean and normalize customer records and transaction types.
    • Executive & Operational Dashboards: Designed high-level dashboards for the investment board and detailed operational screens for local team leaders.
    • Route Analytics: Added geographical reporting to analyze vehicle travel times and service efficiency.

    Business Outcomes Delivered

    • Replaced weekly spreadsheet updates with automated, daily dashboards.
    • Enabled private equity sponsors to monitor key growth and cost metrics in real-time.
    • Provided branch managers with toolsets to optimize vehicle scheduling, lowering fuel costs.
    • Built a scalable data platform that simplifies the integration of future acquisitions.

    Frequently Asked Questions

    How do PE-backed companies consolidate data from acquired businesses?
    A central data warehouse pulls data from each acquired branch’s field-service system, and ETL pipelines normalize customer records and transaction types into one model.

    What dashboards do private equity sponsors need?
    In this project, MyntiQ Tech built high-level dashboards for the investment board to track growth, cost and margin metrics, plus detailed operational screens for branch leaders.

    How can route analytics lower field-service costs?
    Geographic reporting on vehicle travel times and service efficiency helps branch managers optimize scheduling, which lowers fuel costs.

    Integrating acquisitions or modernizing reporting? Talk to MyntiQ Tech or browse more case studies.

  • Data Migration from Legacy POS to Acuity Logic

    Data Migration from Legacy POS to Acuity Logic

    Industry: Healthcare (Eyecare)

    At a glance: MyntiQ Tech migrated more than a decade of patient records, eye prescriptions and sales history from a legacy POS to Acuity Logic for an eyecare group. The work used custom ETL, deduplication, a weekend full load with daily delta updates, and checksum reconciliation. It was completed with zero downtime during clinic hours.

    Overview

    This case study covers the complex migration of patient records, clinical eye prescriptions, and transactional sales history from an outdated legacy POS system to Acuity Logic. MyntiQ Tech designed and executed a secure data migration pipeline that ensured complete data integrity with zero disruption to daily clinic workflows.

    Business Problem

    The eyecare group faced a challenging transition:

    • Over a decade of patient, prescription, and financial data was locked in an old database, which was prone to data corruption.
    • Differences in database schemas between the old POS and Acuity Logic risked messing up prescription records during import.
    • Clinics had to remain open, meaning the migration could not cause system downtime during patient hours.
    • Manual verification of thousands of patient records was impossible, making automated validation essential.

    How MyntiQ Tech Helped

    MyntiQ Tech built a dedicated, secure data migration pipeline:

    • Data Transformation (ETL): Built custom scripts to extract, clean, and map legacy database tables to the Acuity Logic structure.
    • Data Cleaning: Developed deduplication scripts to identify and merge duplicate patient profiles before migration.
    • Delta Migration Workflow: Performed a full initial migration over a weekend, followed by automated daily delta updates to capture new clinic activity.
    • Automated Reconciliation: Implemented checksum checks to verify that every patient record, transaction log, and prescription matched the source.

    Business Outcomes Delivered

    • Successfully migrated thousands of patient records and prescriptions with 100% data accuracy.
    • Completed the migration with zero downtime during business hours, protecting clinic operations.
    • Ensured patient clinical history was instantly available to doctors on the new Acuity Logic platform.
    • Standardized database entries, eliminating duplicate records.

    Frequently Asked Questions

    How do you migrate to Acuity Logic without clinic downtime?
    MyntiQ Tech ran a full initial migration over a weekend, then automated daily delta updates to capture new clinic activity until cutover, so clinics stayed open throughout.

    How is data accuracy verified in a patient data migration?
    Automated checksum reconciliation confirms that every patient record, transaction log and prescription matches the source system.

    What happens to duplicate patient records during migration?
    Deduplication scripts identify and merge duplicate patient profiles before the data is loaded into Acuity Logic.

    Planning an eyecare or healthcare data migration? Talk to MyntiQ Tech or browse more case studies.

  • End-to-End Jewellery ERP Implementation – From Pure Gold Procurement to Retail Sales

    End-to-End Jewellery ERP Implementation – From Pure Gold Procurement to Retail Sales

    Industry: Luxury Goods / Jewellery

    At a glance: MyntiQ Tech built a custom jewellery ERP that tracks gold, silver and gemstones from procurement and refining through manufacturing to retail. It combines a milligram-level metal weight ledger, live spot-price retail pricing, unified inventory, and RFID store and vault audits.

    Overview

    This case study details the deployment of a specialized enterprise resource planning (ERP) platform for a premier jewellery manufacturer and retailer. The platform tracks precious metals and gemstones from raw procurement and refining through design, manufacturing, and multi-location retail distribution.

    Business Problem

    The client operated with significant operational risk:

    • Siloed systems across precious metal purchasing, production workshops, and retail stores made end-to-end tracking difficult.
    • Tracking material loss and weight discrepancies of gold and silver during refining and polishing was manual and lacked accountability.
    • Inaccurate store stock counts led to security concerns and delayed inventory replenishment.
    • Product pricing was static, meaning store prices did not reflect rapid fluctuations in gold and platinum spot market prices.

    How MyntiQ Tech Helped

    MyntiQ Tech built a custom jewellery ERP system:

    • Metal Weight Ledger: Developed a double-entry tracking ledger for metals, auditing weight down to the milligram across refining, casting, and polishing.
    • Live Price Engine: Integrated real-time precious metal market feeds, automatically recalculating retail item prices based on spot prices and margins.
    • Unified Inventory Management: Created a unified stock system spanning manufacturing centers, vaults, and retail display cases.
    • RFID Integration: Implemented RFID-enabled store checkouts and daily vault audits to automate inventory verification.

    Business Outcomes Delivered

    • Achieved precise, real-time tracking of metal weights, reducing inventory shrinkage.
    • Automated spot price updates protected retail profit margins during volatile market shifts.
    • Reduced store inventory audit times from hours to minutes, improving store security.
    • Optimized manufacturing cycles, matching workshop production schedules with retail sales velocity.

    Frequently Asked Questions

    How does a jewellery ERP track gold loss during manufacturing?
    A double-entry metal weight ledger audits gold and silver weight down to the milligram across refining, casting and polishing, so material loss is recorded at every stage.

    Can jewellery prices update automatically with gold rates?
    Yes. A live price engine reads real-time precious metal market feeds and recalculates retail prices from spot prices and margins.

    How does RFID help jewellery stores?
    RFID-enabled checkouts and daily vault audits automate inventory checks, cutting store audit times from hours to minutes.

    Looking for a jewellery or manufacturing ERP? Talk to MyntiQ Tech or browse more case studies.

  • AI-Driven Data Analytics

    AI-Driven Data Analytics

    Industry: SaaS / Enterprise Software

    At a glance: MyntiQ Tech built a conversational AI analytics platform for a SaaS enterprise. Managers ask questions in plain English and an NLP layer turns them into SQL. Machine learning models flag anomalies in minutes and forecast signups, resource use and retention.

    Overview

    This case study details the development of a conversational, AI-driven data analytics platform designed for a high-growth SaaS enterprise. The solution integrates machine learning and Natural Language Processing (NLP) to enable business managers to query databases using plain English, automatically detect anomalies, and forecast key operational metrics in real-time.

    Business Problem

    The enterprise faced hurdles in turning raw data into actionable strategies:

    • Non-technical teams faced constant delays because they relied on data engineering resources to write SQL queries and generate reports.
    • Critical operational anomalies, such as sudden drops in user signup rates or unusual API error spikes, often went unnoticed for days.
    • The company lacked proactive forecasting tools, making it difficult to allocate server resources and plan marketing spend.

    How MyntiQ Tech Helped

    MyntiQ Tech built a smart analytics layer over the client’s data warehouse:

    • Conversational Analytics: Developed an NLP system that translates natural language questions into accurate SQL, allowing managers to ask questions like “What was the churn rate by category last month?”
    • Automated Anomaly Detection: Built machine learning models that scan database metrics to automatically flag and alert engineers of unusual spikes or drops in traffic.
    • Predictive Forecasts: Implemented time-series forecasting models to project client signup trends, resource utilization, and retention metrics.

    Business Outcomes Delivered

    • Empowered non-technical teams to query data instantly, removing dependency on database engineers.
    • Detected operational anomalies in minutes rather than days, protecting service levels and customer experience.
    • Improved resource forecasting accuracy, reducing monthly infrastructure costs through optimized server scaling.

    Frequently Asked Questions

    What is conversational analytics?
    Conversational analytics lets non-technical users ask data questions in plain English, such as “What was the churn rate by category last month?” An NLP layer translates the question into SQL.

    How does AI detect anomalies in SaaS metrics?
    Machine learning models scan database metrics continuously and alert engineers to unusual spikes or drops, such as falling signup rates or API error spikes, in minutes rather than days.

    What can AI forecasting predict for a SaaS business?
    In this platform, time-series models project signup trends, resource utilization and retention, which helps plan server capacity and marketing spend.

    Want AI on top of your data warehouse? Talk to MyntiQ Tech or browse more case studies.

  • Enterprise Data Warehouse & Analytics Platform for Large U.S. Eyecare Organization

    Enterprise Data Warehouse & Analytics Platform for Large U.S. Eyecare Organization

    Industry: Healthcare (Eyecare)

    At a glance: MyntiQ Tech built an enterprise data warehouse and analytics platform for a U.S. eyecare organization with 550+ practices. It standardized 300+ KPIs, delivered 70+ role-based dashboards, and used a StarRocks OLAP database to bring queries from minutes to seconds.

    Overview

    This case study details the development and rollout of a large-scale enterprise data warehouse and analytics platform for one of the largest eyecare organizations in the United States. Operating across more than 550 practices nationwide, the platform unified operational, clinical, marketing, and financial analytics into a single, governed digital environment.

    Business Problem

    The organization faced severe reporting bottlenecks:

    • Operational, clinical, and financial data was decentralized across 550+ practices, each running its own reporting logic.
    • KPI definitions varied by region and division, making enterprise-wide performance comparison unreliable.
    • Reporting cycles were slow, depending on manual exports that lagged business reality by weeks.
    • The legacy database infrastructure could not support the reporting requirements of 300+ clinical and financial KPIs or handle new acquisitions.

    How MyntiQ Tech Helped

    MyntiQ Tech built a robust data warehouse and analytics layer:

    • Enterprise Data Warehouse: Created a centralized data warehouse with standardized dimensional models to unify clinical and operational data.
    • High-Performance OLAP: Implemented a StarRocks OLAP database to enable sub-second query speeds across multi-million row datasets.
    • Governed KPIs: Standardized and operationalized 300+ KPIs spanning clinical performance, marketing ROI, supply chain, and revenue cycle management.
    • Role-Based Dashboards: Designed and deployed 70+ interactive, role-based reports tailored for practice managers, regional directors, and executive leadership.

    Business Outcomes Delivered

    • Created a single source of truth for 300+ KPIs, aligning operational definitions nationwide.
    • Reduced query execution times from minutes to seconds, improving user engagement and decision speed.
    • Shifted reporting latency from weeks to near real-time, allowing clinics to adjust operations dynamically.
    • Built a highly scalable data infrastructure that supports new practices without performance loss.

    Frequently Asked Questions

    How do you standardize KPIs across hundreds of healthcare practices?
    MyntiQ Tech built standardized dimensional models in a central data warehouse and governed 300+ KPI definitions, so every region and division measures performance the same way.

    How fast can an enterprise healthcare data warehouse be?
    With a StarRocks OLAP database, queries on multi-million row datasets dropped from minutes to seconds, and reporting moved from weeks of lag to near real time.

    Can the platform absorb new acquisitions?
    Yes. The infrastructure was built to add new practices without losing performance.

    Modernizing healthcare analytics? Talk to MyntiQ Tech or browse more case studies.

  • Cloud-Native Analytics Platform for Managed Connectivity & Communications

    Cloud-Native Analytics Platform for Managed Connectivity & Communications

    Industry: Telecommunications

    At a glance: MyntiQ Tech built a cloud-native analytics platform on AWS for a managed connectivity and communications provider. A StarRocks OLAP data warehouse unifies data from eight transactional systems covering SD-WAN, Voice, SIP and UCaaS. Analytics are served through a secure API, so reporting no longer slows production databases.

    Overview

    This case study outlines how MyntiQ Tech designed and implemented a cloud-native analytics platform on AWS for a managed connectivity and communications service provider. By unifying data from multiple operational systems into a centralized analytics layer, the organization gained fast, reliable business insights without impacting the performance of its core transactional databases.

    Business Problem

    The telecommunications provider faced critical data challenges:

    • Operations spanned multiple managed services (including SD-WAN, Voice, SIP, and UCaaS) which were supported by eight independent transactional systems.
    • Data was siloed across these systems, making cross-product performance and customer lifetime value reporting slow and manual.
    • Analytical queries were run directly on live production databases, impacting system performance and risking downtime.
    • The existing reporting infrastructure could not scale to support increasing data volumes or concurrent analytical queries.

    How MyntiQ Tech Helped

    MyntiQ Tech developed a high-performance analytics solution:

    • Cloud-Native Architecture: Designed a secure analytics framework on AWS optimized for scale, speed, and reliability.
    • Centralized Data Warehouse: Built a unified OLAP data warehouse using StarRocks to consolidate data from all eight operational systems into a unified model.
    • Data Orchestration: Implemented cloud-native pipelines with automated data reconciliation checks to ensure consistency across sources.
    • Decoupled Presentation Layer: Exposed analytics via a secure API, allowing operations, finance, and leadership teams to pull data into custom dashboards.

    Business Outcomes Delivered

    • Delivered query performance that was significantly faster for complex, cross-product business analytics.
    • Eliminated analytical search loads on transactional systems, improving core database stability.
    • Created a single, consolidated view of customer accounts, service usages, and recurring revenue.
    • Supported data-driven decision-making across leadership, finance, and support departments.

    Frequently Asked Questions

    Why shouldn’t analytics run on production databases?
    Analytical queries on live transactional databases slow them down and risk downtime. A separate OLAP warehouse takes that load off the core systems.

    What is StarRocks used for in telecom analytics?
    In this project, StarRocks is the OLAP data warehouse that consolidates data from eight operational systems into one model for fast cross-product analytics.

    How do teams get dashboards from the analytics platform?
    Analytics are exposed through a secure API, so operations, finance and leadership teams can pull data into their own dashboards.

    Need a unified analytics platform? Talk to MyntiQ Tech or browse more case studies.