Synaptron India (P) Limited
Corporate Profile
Synaptron in a glance
Comprehensive Solutions & Expertise
Expertise in custom applications, ERP/HCM implementations, hardware, cloud services, staffing, data warehousing, and more – all tailored to your unique business needs.
Transformation & Modernization
Modernize legacy systems, optimize processes with cutting-edge technology, and unlock the full potential of your business through digital transformation
Partnership & Client-First Approach
Focus on understanding your challenges, delivering results, and ensuring your long-term success.
Meet Our Team
Dhrub
20+ Yrs in IT, Digital Transformation. ERP & AI Applications
Joyantt
22 Yrs as Solution Architect, Business Development
Pranav
25+ yrs in Business Development & Customer Success
Lalit
AI Leader with 18 yrs as Data Scientist & Technology Manager
Alex
22 Yrs in Digitalization & Document Management Systems
Vinayak
20+ Yrs in ERP powering Digital Transformations
How Can We Assist You
Actionable Data Intelligence
Insights that drive decisions
Integrated ERP/HCM
System
Streamlined business process management
Applications for Modernization
Custom software, updates, and integrations
Strategic Staffing Solutions
Expert talent for your team
Scalable Cloud & Hardware
Flexible IT infrastructure solutions
Applications of Deep Learning
And much more…
Sample (Azure) AI Model Deployment of Cloud
Performing data collection/understanding, modeling and deployment
SENSORS AND IOT (UNSTRUCTURED)
AZURE ML ML SERVER
AZURE DATABRICKS
(Spark ML)
SQL Server (In-database ML)
DATA SCIENCE VM
COSMOS DB
SQL DB
LOGS, FILES AND MEDIA (UNSTRUCTURED)
DATA LAKE STORE
AZURE STORAG E
COSMOS DB SQL DB
AML COMPUTE
AZURE DATABRICKS
HDINSIGHT
SQL DW
BUSINESS / CUSTOM APPS
AZURE KUBERNETES SQL SERVER
AZURE ANALYSIS SERVICES
DASHBOARDS
(STRUCTURED)
DATA FACTORY
SERVICE
(In-database ML)
Technologies
Information extraction
Extraction of unstructured text from contracts
Industry: Technology Technology: NLP, Deep learning Client: US based
Automatically extract information related in legal contracts such as Supplier name, customer name, date effective etc..
Deep learning information extraction algorithm. Algorithm is novel and IP will be owned by sponsor.
Algorithms have been deployed to the client system.
Language to SQL
Convert natural language query to SQL
Industry: Technology Technology: NLP, Deep learning Client: US based
Convert query entered or spoken by user in natural language to SQL query.
Deep reinforcement learning based system.
State of art technology, query includes co mplex connectors such as GROUPBY and ORDERBY.
Algorithms have been deployed to the client system.
Predicting psychological and blood age
– Measuring stress
Industry: Healthcare Technology: Deep Learning Client: UK based
Based on questions a Deep Learning model is trained to predict the psychological age.
Based on age and questions a
recommendation engine gives the todos to help reduce stress and reduce psychological age.
Blood age is computed using blood report parsed by automated pdf reader.
Deep learning model is used on blood parameters extracted from report to compute blood age.
Both aging clocks have been launched in the market.
Crop Price Forecasting
Predictive modelling
Crop price prediction helps farmers to
decide the right time to harvest the crop.
10 years of historical data was downloaded from the govt. websites.
A price prediction model was build for next 7 days, this has been deployed.
A model for next 1 month is in progress.
Client is well known world brand.
Industry: Agriculture Technology: Deep Learning Client: US/India based
Image Classification and Anomaly Detection
Classification of Chest X-ray Images and Nodule Detection
Interpretation of chest radiographs is a tedious job, which is further multiplied by the shortages of radiologists.
Built a triage system that sorts the cases based on severity.
Deep learning based tool discards the normal radiographs, which saves time and cost for the hospitals and insurance companies.
Further among abnormal radiograph,
Industry: Healthcare
Technology: Image analysis, Deep learning
Client: US based
nodule (precursor to cancer) was automatically detected.
Solution deployed at 1000- bed hospital.
Prediction and Recommendation Modeling
– Predict Risk of Hospitalization in Patients with Chronic Kidney Disease
Age, Gender, History of disease, calcium, ….
Risk of hospitalization
Industry: Healthcare
Technology: Data analysis, Machine learning
Client: US based
Early warning to patients and ca regivers
Machine learning based platform
developed to reduce insurance burden and improve care for C KD patients.
Based on patient history and lab values system predicts the risk of hospitalization for the CKD patient in next 1-5 years with sensitivity accuracy of above 85%
Mobile app based AIsolution
Recommended for trials
Predicting Risk and Enhancing Safety
Heavy Industry Task Safety Analysis and Prediction
AI
Industry: Oil and gas industry Technology: NLP, Machine learning Client: US based
Injury is c onsidered as the most critical event for any heavy industry.
Developed a solution that predicts and alerts the user about the risk involved for a given task based on description.
Risk mitigation techniques are also suggested.
Solution offers significant cost savings
and has been successfu ly deployed by the client.
Image Segmentation and Analytics
Segmentation of Retinal Layers from OCT Images
Industry: Healthcare
Technology: Image analysis, Deep learning
Client: India based
Segmentation of retina layers is critical for diagnosis of several eye related diseases.
Image segmentation technology based on deep learning was delivered for inhouse OCT scanner product.
Algorithms have been deployed and will be part of the product after FDA clearance.
Agritech Solutions
Challenges
Lack of knowledge about modern techniques; No / limited availability of Soil data; Middleman / agents takes the major chunk of profit
Poor Seed Quality is directly impacting the crop yielding
Inability to detect crop diseases early in the growth cycle leads to reduced productivity and uncontrolled use of chemicals which gets accumulated in soil, water, and sediment
Expensive physical lab-based tests, time taking and not easily accessible/available for growers
High Cost of IoT and Satellite based Solutions inhibits rapid digital transformation in Agriculture
Communication gap and response time lag are major source of delays in providing help to the farmers
How Can AI Help in Smart Manufacturing
How Can AI Help Manufacturing? Use Case 1 : Smart Maintenance
Reduce Unplanned downtime cost plants and factories in upwards of $50 billion annually, 42% of which can be attributed to asset failure.
AI algorithms (is a set of well-defined steps to be followed to arrive at a desired solution) like neural networks and Machine Learning can generate trustworthy predictions regarding the status of assets and machinery for predictive maintenance
Use Case 2 : Better Product Development
Using Generative allows putting a detailed brief created by humans into an AI algorithm.
The algorithm analyses the brief based on parameters like available production resources, performance, quality and time.
The algorithm examines all possible variations and generates a few
optimal solutions for e.g. Engine Development
IoT integrated use cases in lifecare
Area
Value
Smart Hospitals
Hospital Navigation and improved patient and provider experience. Use IoT for everything from parking to inventory tracking
Smart Diagnostics Data
Pool data from multiple diagnostic sources and analyze for disease trends and density for better diagnostics supply chain management
Personalized Medicine
Leverage wearables to collect real time patient critical parameters and store them in Databases. Run rule based analytics and narrow down on personalized treatments
Chronic Disease Management
Reduced medical center admissions, shorter hospital stays with the aid of home-monitoring systems, and adoption of standardized treatments that conform to best-practices; Overall a $300B value prop annually
Connected Learning
Estimated 40% improvement in knowledge utilization through recorded lessons and a 50% reduction in instructional supplies
Air Quality Monitoring
Improved quality of life and life-expectancies
IoT integrated use cases for people productivity & safety
Factory Sheds
Guard Post
Uploading Rack
Rest Area
Processing Area
Auxiliary Gate
IoT integrated use cases for people productivity & safety
360° overlapping coverage
Computer Vision AI based Solutions Conceptual Dashboard UI
Software Development Capabilities
Full-Spectrum Applications: Web, mobile, desktop, cloud-native, embedded systems
Robust Tech Stack: Expertise in leading languages (Python, Java, JavaScript, .NET), frameworks (React, Angular, Node.js, Spring), and databases (SQL, NoSQL)
Solutions-Oriented Development: Custom builds, product development, SaaS platforms, legacy modernizations
Agile & Collaborative: Tailored methodologies (Scrum, Kanban) for seamless partnerships
Data-Driven Emphasis: AI/ML integration, analytics, and robust security practices
Enterprise Offerings
CONSULTING HR TECH
HYPER AUTOMATION & INTEGRATION
ENTERPRISE PLATFORMS & ECOSYSTEM SERVICES
Domain, Process & Solution Experts
20+ Projects | 10 Locations
35 Experts | 2 CoEs
15 Projects | 50+ Bots | 30+
Reengineered Apps | 6 Locations
10 E2E Programs | 5 Global Sites
30 Experts | Flexible Models
Cloud Services
Cloud Strategy
Analytics & Intelligence
Automation
Collaboration Platforms
DevOps
Empowering Business
Highlights
& Bots
Application Modernization
Orchestration
API
Management
Assessment, Design, Planning
Cloud Migrations
Mobile
Micro Services
Compliance &
Focus on RoI, Cost of Operations &
Applications
Cost
Transforming Experiences
Capabilities for Joint Value Creation
Our Kronos Portfolio
Consulting | Implementation | Upgrades | Integrations | Analytics & Reporting | SLA based Support/ DevOps
Capabilities for Joint Value Creation
Our SuccessFactors Portfolio
Consulting | Implementation | Upgrades | Integrations | Analytics & Reporting | SLA based Support/ DevOps
Our Integration Practice
Integration
Connect and Integrate applications and
data. e.g., Oracle, SAP, SuccessFactors,
Kronos etc.
API Management
Design, Develop, Manage and Secure
APIs.
e. g., expose data to external applications
or parties
Process Flows
Build Efficient Workflows. e.g., Employee Onboarding, Customer Support Portal, Approval Workflows,
Requisition portal etc.
EDI
Connect and Automate Transactions with Partners and Supplier.
e.g., Purchase order, Invoice, Shipping Notifications etc.
Data Management
Data extraction, cleansing and synchronization across Enterprise applications like Oracle, JD Edwards, SAP, SuccessFactors, Workday and databases
Managed Services
Maintenance, Monitoring and Support of Boomi Infrastructure and Integration Processes
Synaptron ERP Solution built on Open Source ERPNext system is the right fit for all your needs.
While our ERP solution offers several modules and sub-modules that cater to any industry functions right from Accounting to Sales to Reporting, mentioned here are some of our core modules that can help you automate your business critical processes effectively and effortlessly.
Manufacturing Sector Specific Modules
Bill of Materials
The BOM is a list of all materials (either bought or made) and operations that go into a finished product or sub-Item.
Work Orders
A Work Order is a document that is given to the manufacturing shop floor by the Production Planner as a signal to produce a certain quantity of a certain Item.
BOM Comparison Tool
Using BOM Comparison Tool, you can compare two BOMs and see what changed between their iterations.
Workstation
Workstation stores information regarding the place where the workstation operations is carried out
Item Alternative
If a raw material defined in the BOM is not available during the production process then their respective available alternative item used to complete the production process.
Capacity Planning
Capacity Planning functionality helps you in tracking production jobs allocated on each Workstation.
Operation
Stores a list of all Manufacturing Operations, its description and the Default Workstation for the Operation
Sub-Contracting
Subcontracting is a type of job contract that seeks to outsource certain types of work to other companies.
Open Work Orders
We can easily identify the progress of manufacturing of certain items in our organizations using Open Work Orders feature
Delivery Model
Pro p o rt io n o f Re s o u rc e s
20%
Enterprise Data Management, Data Engineering, and Data Modelling
En g a g e m e n t Mo d e ls
64%
Digital Transformation Solutions
16%
AI/ML and Data Analytics
Our Value Proposition
Advantage of End-to-End Application Services (Full Lifecycle Support)
Personalized
It is tailored to yourunique needs with a futurefocus
Identify your unique current and future need together
Evaluate modern tools whether open source or Microsoft for yourunique environment.
Evaluate the need for automation (RPA) or AI to reduce your costs
Leverage ITIL and Modern agile management
practices todeliver
value.
leverage our own Innovation lab ofdata scientists for
ideas and solutions throughout the lifecycle.
Focus on composable architecture to enable SOA
Ownership
You own it,you decide how it should be designed,implemented
Flexible and Scalable
Future integrations baked intodesign
Lower Costs
Offshore Model is great at lowering custom development costs
Efficient
Unnecessary demands on operating system eliminated
Leverage low code acrossapplication
development layers
Consumer-Grade UI
Responsive, conversational experience
Multi channelability
Develop multichannel Responsive apps
API-Driven Integration
Architect for Microservices and RESTAPIs
Agile DevOps
Automate, Iterate and deliverrapidly
Cloud-native model
Adopt cloud native developmentmodel for continuous refresh
Delivery Framework
Createdby Annette
Sptihoven fromthe Noun Project
In line with our Standard Delivery Practice, we identify your unique needs to leverage the best suited project methodology from Waterfall toAgile.
*This is our standard deliveryframework for development and support of custom applications,thatcan be change per your unique needs
Understanding AI Concepts
AI Backgrounder
The Data Science Process with examples of frameworks
Answers
Let the machine figure out the rules based on history!
The new ML based programming paradigm
Deep Learning Vs Traditional ML
ML
manual feature extraction
classification algorithm
Cat Dog Other
DL
feature learning + classification
Cat Dog Other
Fundamental Concepts – Visualizing a Neural Network
Understand the data and the goal
Classification or Regression
We have two classes of data (orange and blue).
We want to train a model that given some input data, it can classify it as orange (-1) or
blue (+1).
Define a network architecture
Decide on the network architecture, basically:
how many layers
how many neurons in each layer
how the neurons in each layer are connected (fully or partially)
Define shape of input data
Define how the input data (the features X1 and X2) are provided to the model.
Here we choose to multiply the features and provide the result as the input.
Set parameters – learning rate
Set parameters – activation function
Set parameters – Regularization
Training – The forward pass
Training – Evaluate loss
Given that we know the correct class and the class predicted by the model, we can assess the model’s performance at the end of each epoch.
The measured error is called the loss.
Training – The backwards pass
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The strength of the connection between the neurons is initially randomly set. This strength is real number and called a weight.
At the end of the epoch, a calculation is performed that proceeds in reverse thru the neural network, adjusting the weights to minimize the loss.
This is called the backwards pass.
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Training – Run lots of epochs
Training – Model complete!
Now we have a trained model that can classify the training data reasonably well.
But how well does it perform against data it has not seen?
Model Evaluation
That’s were the test data set comes in.
We run all of the test data thru the model and measure the result.
The Test Loss tells us the error of the model against this unseen data.
Concept:
Overfitting & Underfitting
With Neural Networks, you aim to overfit by having a complex model and then take steps to generalize it (reduce the overfitting).
But that’s the ideal – in reality overfitting often comes with a hefty time and computational cost price.
Best Practice
Solving the overfitting problem
Solution:
Use a simpler model
or
Use regularization
(constraints on parameters)
or
Get more training data
Monitoring Progress – The Learning curve
Pick a small subset of data
Fit model and calculate training and validation/test scores
Repeat for a larger subset of data
Choosing the right last layer activation & loss function (Keras)
Problem Type
Last Layer Activation
Loss Function
Binary classification
Sigmoid
binary_crossentropy
Multiclass, single-label classification
Softmax
categorical_crossentropy
Multiclass, multi-label classification
Sigmoid
binary_crossentropy
Regression to arbitrary value
[None]
mse
Regression to values between 0 and 1
Sigmoid
mse or binary_crossentropy
Model Packaging – Ship it!
Once the model is performing well you typically:
Store the model in a model repository (you version models like you version source code)
Write code to integrate the model in an application
Deploy the model from the repository along with the application that will consume it.