We build the systems that power your business

From Building API of MVPs to Machine Learning Models.
 We Develop, Scale, and Connect.

Tools & Services

Google Cloud Platform (GCP)

  • Cloud Functions, App Engine, and Compute Engine setup
  • Firestore and BigQuery integration for data warehousing
  • Cloud IAM and VPC configuration for secure infrastructure
  • Cloud Scheduler and Pub/Sub for event-driven automation
  • GCP Load Balancing and CDN configuration for scaling

Amazon Web Services (AWS)

  • Lambda functions and API Gateway deployment
  • RDS and DynamoDB provisioning for scalable storage
  • CI/CD pipelines via CodePipeline and CodeBuild
  • S3 + CloudFront for static asset delivery
  • IAM role management and cross-service security policies

Microsoft Azure

  • Azure Functions and Logic Apps for serverless workflows
  • Cosmos DB and Azure SQL database design
  • Azure DevOps for deployment and release automation
  • Azure Blob Storage and CDN integration
  • Azure AD B2C for secure identity and access control

API & Integration Services

  • REST and GraphQL API development with auth layers
  • Middleware engineering and service orchestration
  • 3rd-party SaaS integrations (Stripe, Twilio, HubSpot, etc.)
  • Webhook listeners and event queue integrations
  • API rate limiting, versioning, and lifecycle management

Segment (Engineering Use)

  • Custom event instrumentation via Segment SDKs
  • Source-destination mapping and identity resolution
  • Transformations and warehouse sync setup
  • Reverse ETL pipelines to sync analytics with CRMs
  • Consent and compliance enforcement via Segment protocols

LookML / Looker Modeling

  • Data model creation using LookML best practices
  • Exploration design and governed metric definitions
  • Git version control and multi-environment setup
  • Persistent derived tables (PDTs) for performance
  • User-level access and permission modeling

Coding & Scripting

  • Python
  • Javascript/node.js
  • React Front End
  • ETL pipelines and data automation scripts
  • Flask or FastAPI backend micro-services
  • API and cloud-native script deployment
  • Web scraping and data collection bots
  • Pandas and NumPy-based analytics modules

Backend Development

  • Express.js server architecture and REST APIs
  • Realtime apps with WebSockets or Socket.IO
  • Microservices and serverless function builds
  • JWT-based auth systems with Passport.js
  • Queue systems with Bull or RabbitMQ integrations

Web Application Development

  • Full-stack SPAs with React, Vue, or Svelte
  • Backend services with authentication and role access
  • SSR/CSR implementation and performance tuning
  • CI/CD deployment with Netlify, Vercel, or Docker
  • SEO optimization and schema implementation

Mobile App Development

  • iOS and Andrroid Custom App Development
  • Cross-platform builds using React Native or Flutter
  • Firebase backend integration and push notification setup
  • OTA update delivery and Play/App Store readiness
  • Offline-first app architecture and sync systems
  • Integration with native modules
    (camera, biometrics, etc.)

Mobile App Analytics

  • Event schema design for in-app analytics
  • Firebase, Mixpanel, or Segment integration
  • Funnel, retention, and crash analytics setup
  • A/B testing and feature flagging via analytics tools
  • Attribution tracking with AppsFlyer or Branch.io

AI & Machine Learning

  • ML model deployment with Vertex AI or SageMaker
  • Data pipelines for training set generation
  • AutoML and inference API integrations
  • TensorFlow model training
  • Scalable serving infrastructure with GPU/TPU instances

Full Stack Development
Case Studies

AI and Machine Learning for Predictive Analytics

Client: Regional Healthcare Provider
Focus: Full stack development, artificial intelligence, and advanced analytics

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Problem:
 The client faced escalating hospital readmission rates and a lack of real time insight into patient risk factors. Their existing systems were siloed, with no intelligent layer between clinical data and decision making. This reactive model resulted in costly inefficiencies and compromised continuity of care.

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Solution:
 Adaptive Analytix engineered a predictive analytics solution from the ground up. We began by architecting a machine learning model tailored to the client’s patient population, trained to flag individuals at high risk of readmission using historical EHR data, comorbidity trends, and external health indicators.

To operationalize this intelligence, we developed a fully integrated internal dashboard designed for seamless alignment with their existing electronic health record system. The dashboard surfaces real time risk scores, actionable insights, and care prioritization tools for providers.

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Frontend: Built in React for responsive, cross device usability

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Backend: Developed in Python using Flask to handle model inference and secure API endpoints

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Data Pipeline: Deployed using AWS Glue to automate ETL from multiple sources, including patient records, third party health APIs, and historical treatment logs

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Outcome:
 Within three months of deployment, the organization achieved a 12% reduction in readmission rates. Clinical teams reported improved workflow coordination, earlier intervention, and greater confidence in prioritizing at risk patients. The system remains in active use and has since been scaled to additional facilities.

E-Commerce Infrastructure Overhaul and Performance Optimization

Client: Mid-size Apparel Retailer
Focus: Full stack development, frontend performance, and analytics-driven architecture

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Problem:
 The client’s digital storefront was struggling with high bounce rates, poor mobile load times, and inconsistent analytics across key conversion points. Fragmented backend logic, unoptimized frontend rendering, and a generic data layer were bottlenecking both customer experience and marketing performance.

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Solution:
 We executed a ground-up rebuild of the e-commerce platform with a performance-first mindset. The frontend was re-engineered using Next.js, optimized for server-side rendering, dynamic content hydration, and minimal cumulative layout shift.

The backend was refactored to decouple API services and streamline product catalog and checkout processes. We transitioned key endpoints to Node.js, integrating with a MongoDB datastore for scalability and lower query latency during peak hours.

In parallel, we reconstructed the entire data layer. Using Google Tag Manager and GA4, we implemented event-level tracking tailored to high-value customer actions including product views, variant selections, cart additions, and checkout flow progression.

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Frontend: Rebuilt using Next.js with a focus on render efficiency and mobile responsiveness

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Backend: Modular API architecture built with Node.js and MongoDB for speed and scalability

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Analytics Stack: Custom data layer architecture deployed in GTM and GA4 with full cross-device attribution

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Outcome:
 The new system delivered immediate gains:

  • Mobile conversion rate increased by 28%
  • Page load times were reduced by 2x across all devices
  • Bounce rate dropped by 35%, especially on product detail and checkout pages
  • Enhanced data fidelity enabled better ad optimization and retargeting, driving a measurable uplift in return on ad spend

The client now operates with a faster, more flexible platform built to scale with seasonal demand, evolving product lines, and omnichannel campaigns.

Scalable Learning Platform for Certification Delivery and Attribution Visibility

Client: Online Professional Course Provider
Focus: Full stack development, marketing analytics, and conversion tracking infrastructure

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Problem:
 The client operated a static learning management system that couldn’t scale with demand or support granular attribution. Users could access content, but the platform lacked real time performance tracking, marketing insight, and dynamic personalization. Conversion events were loosely defined, making ad performance nearly impossible to quantify across multiple channels.

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Solution:
 We architected a new LMS built for performance, modularity, and analytics from day one. The core platform was built using Django for its admin efficiency and security model, while the frontend was constructed in React to deliver a responsive, seamless user experience across mobile and desktop.

To enable intelligent marketing operations, we integrated Google Analytics 4, Facebook Conversion API, and HubSpot into a single orchestrated system. This ensured attribution continuity across email campaigns, paid ads, and organic discovery while giving sales and ops teams real time lead data from course engagement to checkout.

We also implemented user cohort tracking and custom events tied to certification completions, upsells, and multi touch journeys, all visualized in Looker Studio dashboards.

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Frontend: React application delivering dynamic content, built in course progression logic, and user level state retention

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Backend: Django architecture managing secure course access, enrollment, and payment handling

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Attribution Layer: GA4, HubSpot, and Facebook CAPI orchestrated for complete funnel visibility

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Outcome:

  • 300% increase in return on ad spend within two months
  • Attribution gaps closed across Google, Meta, and email channels
  • Course engagement time increased by 46%, driven by UI improvements
  • Sales team adopted Looker dashboards for lead prioritization and cohort insights

The platform now operates as a scalable, data rich LMS capable of powering B2B and B2C certification programs with full transparency into what drives conversions.

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Martech Ecosystem Engineering for Scalable Lead Generation

Client: B2B SaaS Startup
Focus: Backend development, data engineering, and marketing stack unification

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Problem:
 The client was juggling disconnected tools across their marketing, sales, and analytics workflows. Campaign performance was difficult to measure, user journeys couldn’t be reliably attributed, and internal teams lacked confidence in the data being surfaced. Their GTM timeline was suffering due to siloed systems, inconsistent tracking, and limited automation.

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Solution:
 We engineered a centralized martech ecosystem designed for performance, attribution integrity, and scale. Starting with a clean infrastructure audit, we mapped out and connected the client’s core systems: Segment, GA4, Stripe, and Salesforce.

On the backend, we deployed a server-side tracking environment using Google Tag Manager’s server container, hosted on Google Cloud Platform. This ensured clean, cookieless compatible data collection across all digital touch points significantly improving the accuracy of events being fed to ad platforms and CRMs.

We then set up automated ETL pipelines and webhook listeners that moved user behavior data in real time into the client’s reporting and sales systems. In parallel, we deployed a lightweight DevOps layer using containerized CI/CD pipelines, giving the internal dev team the ability to ship updates faster and with less risk.

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Backend Infrastructure: Google Cloud Hosted GTM server container with event enrichment and deduplication

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System Integrations: Segment, Salesforce, Stripe, and GA4 stitched into a unified lead intelligence loop

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Automation Layer: Custom webhooks, ETL pipelines, and CI/CD deployment to minimize human error

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Outcome:

  • 42% improvement in attributed leads across paid channels
  • Time to deploy feature changes reduced by 60%
  • Full visibility into the lead funnel from first touch to revenue recognition
  • Significantly cleaner data for ad optimization, reporting, and lifecycle marketing
  • The system now acts as the client’s central nervous system for demand generation, enabling their marketing and sales teams to act faster, smarter, and with total confidence in their data.