Visibility Only Counts If the Data Is Live
Visibility Only Counts If the Data Is Live
A daily batch export is not visibility. We build event-driven pipelines so inventory, orders, and shipments reflect what is true right now.
ML Services Built for Your Business
We work with companies that need practical machine learning solutions but don't have the in-house expertise. Whether you're starting your first ML project or scaling existing systems, we build what you actually need.
ML Strategy & Consulting
We identify where ML fits your business and build a realistic roadmap for first-time adopters.
Custom ML Development
We build custom ML systems from your data and requirements — ideal when off-the-shelf tools fall short.
Neural Network Solutions
Deep learning for image recognition, NLP, and pattern detection — built for complex, unstructured data.
ML Systems Engineering
We turn ML models into production-ready systems that reliably handle real-world data and traffic.
ML Integration Services
We connect ML models to your existing software, databases, and workflows — no rebuild required.
Managed ML Solutions
We host and maintain your ML systems so your team can focus on results, not infrastructure.
MLOps & Automation
We build automated pipelines for training, testing, and deploying models reliably at scale.
Why Us for Supply Chain?
In supply chain, a dashboard nobody trusts is worse than no dashboard. Here's what that conviction changes about how we build.
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We Know What Warehouse Teams Won't Use
We Know What Warehouse Teams Won't Use
Our design process is validated on the floor — scanner in hand, gloves on — so what we ship fits how pickers and planners actually work.
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Integration Is the Architecture
Integration Is the Architecture
ERP, WMS, TMS, and carrier APIs are decided during architecture — not bolted on after launch when the mappings already drifted.
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Built for Multi-Tier Supplier Networks
Built for Multi-Tier Supplier Networks
Your risk sits two tiers down. We model suppliers, sub-suppliers, and lanes so disruption surfaces before it reaches your line.
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We Know When Not to Use AI
We Know When Not to Use AI
We use ML for demand forecasting and route optimization — and push back when a reorder rule is cheaper and more auditable.
Our Technology Approach
We choose technologies based on what solves your specific problem, not what's trending. Each tool in our stack has a clear purpose for building reliable, maintainable ML systems that deliver business value.
Core ML Frameworks
We use TensorFlow, PyTorch, and scikit-learn because they offer proven reliability and extensive community support. This means your models are built on stable foundations with long-term maintenance.
Computer Vision Libraries
OpenCV and specialized vision frameworks help us process images and video efficiently. We choose these when you need quality control, facial recognition, or visual inspection systems.
NLP & Text Processing
For text analysis and language understanding, we use spaCy, NLTK, and transformer models. These work well for customer service automation, document analysis, and sentiment tracking.
Data Engineering Tools
Apache Spark and pandas help us clean and prepare your data properly. Good data preparation is what makes ML models accurate and reliable in production.
Deep Learning Platforms
Keras and custom neural network architectures let us handle complex patterns in data. We use these when traditional ML isn't enough for problems like fraud detection or medical diagnosis.
Automation Frameworks
We integrate with RPA tools when ML needs to trigger business processes. This connects your ML predictions to actual workflows in your existing systems.
Cloud Infrastructure
AWS SageMaker, Azure ML, and Google Cloud AI provide scalable computing power. We pick the platform that matches your existing infrastructure and compliance requirements.
Data Pipeline Tools
Airflow and Kubeflow help automate model training and deployment. This ensures your ML systems stay updated and continue performing well as your business changes.
Real Supply Chain Outcomes
Every number below comes from a supply chain system we designed, built, and shipped — measured in production, not projected in a pitch deck.
60%
AI & Analytics Visibility Platform — a global manufacturer flying blind past tier-1 suppliers, 60% increase in end-to-end supply chain visibility
40%
Route & Freight Optimization Engine — carrier spend set by spreadsheet and habit, 40% reduction in transportation and logistics costs
30%
Warehouse & Planning Automation — manual reconciliation across ERP and WMS, 30% increase in operational efficiency
Our Technology Partners
The platforms and tools we build supply chain products on — hover any logo to learn how we use it.
AWS
Cloud infrastructure and managed services powering scalable, resilient supply chain workloads.
Azure
Microsoft's cloud platform for enterprise-grade logistics and ERP deployments.
Google Cloud Premier
Premier-tier Google Cloud for demand forecasting, data, and AI.
ServiceNow
Workflow automation for operations, IT, and supplier service management.
Adobe
Experience and content tooling for customer-facing commerce products.
Magento
Commerce platform for B2B ordering and distributor storefronts.
Databricks
Lakehouse platform for large-scale inventory and logistics analytics.
AWS Sagemaker
Managed ML for demand planning, routing, and forecasting models.
AWS Bedrock
Managed foundation models for generative-AI supply chain features.
MuleSoft
API-led integration across ERP, WMS, TMS, and carrier systems.
OneStream
Unified finance and operational performance management.
Oracle
Enterprise database and cloud apps, including Oracle SCM.
Salesforce
CRM and service cloud for distributor and customer engagement.
SAP
Enterprise resource planning for procurement, production, and logistics.
Snowflake
Cloud data platform for analytics on governed supply chain data.
BigCommerce
Headless commerce for wholesale and DTC channels.
RedHat
Open-source enterprise platforms and Kubernetes (OpenShift).
HubSpot
Marketing, CRM, and lead-acquisition automation.
Stripe
Payments infrastructure for billing and order transactions.
Boomi
Low-code integration and data management across systems.
Cloudinary
Media optimization and delivery for catalog-heavy apps.
MongoDB
Flexible document database for modern logistics applications.
Docker
Containerization for portable, reproducible deployments.
Datadog
Observability and monitoring for production reliability.
Snowplow
Behavioral data pipelines for product analytics.
MoEngage
Cross-channel customer engagement and messaging.
Who This Is For
Operations teams that need their supply chain to run on live data instead of yesterday's spreadsheet.
Shippers, 3PLs, and Freight Forwarders
Moving volume across carriers and lanes, and need visibility and cost control that holds at scale.
Manufacturers and Distributors
Running production against multi-tier suppliers, and need disruption to surface before it hits the line.
Supply Chain and Operations Leaders
Reconciling ERP, WMS, and TMS by hand, and need one planning view your team actually trusts.
Retail and E-Commerce Operators
Balancing inventory across channels and warehouses, and need demand forecasting that reflects real sell-through.
Unlock the Power of Robust Supply Chain Solutions
We connect your ERP, WMS, and TMS data into one planning view, then add demand forecasting and exception alerts on top of it. Fewer manual reconciliations, tighter inventory, and decisions your operations team can act on the same day.
Contact our Experts
Award-Winning AI Development & Consulting
100 Fastest Growth Companies
Global Spring Winner
Top App Development Company
AWS Partner Network
Google Cloud Partner
Highly Rated on Trustpilot
Verified Agency
Top App Development Company
ASSOCHAM Member
Common Questions
[ 1 ]
How do you determine if AI is right for our business?
A short discovery maps your goals, then shows where AI adds real value — and where it will not.
[ 2 ]
What does the consulting process look like?
Discovery, plan, build and test with regular check-ins, then deployment and ongoing support.
[ 3 ]
How long does a typical AI project take?
Simple AI projects run 2-3 months; complex ones 6-12. We confirm timelines after discovery.
[ 4 ]
Can you work with our existing systems?
Yes. We integrate AI with your existing systems, APIs, and workflows without disruption.
[ 5 ]
How do you handle data security and privacy?
Security is built in: data protection best practices, access controls, and compliance.
[ 6 ]
What kind of ongoing support do you provide?
We monitor performance, ship improvements, give technical support, and train your team.
[ 7 ]
What types of businesses do you work with?
Startups to enterprises, across industries — anyone ready to put AI to work.
Global presence
