Built to work in production, not just staging
From supervised learning to MLOps โ we build machine learning systems that run in production, not just demos.
Companies are hoarding the power to predict the future and save more than 80% in manual labor, predict demand in such a way that millions of dollars in storage costs can be saved. The difference between all this potential and tangible achievements almost always is in the engineering, not the data.
Prakash Software Solutions Pvt. Ltd (PSSPL) specializes in building production-quality Machine Learning Systems from end-to-end โ preparing data, developing models, integrating into existing environments, and maintaining accuracy via continuous learning pipelines. With over 25 years of enterprise software development experience and deep expertise in Azure ML Services, we are prepared for the harsh realities of production โ no clean benchmarks here!
We work with businesses in the US, UK, and Australia across healthcare, manufacturing, financial services, retail, and logistics.
25+
Years enterprise software delivery
Full lifecycle Ownership
From PoC through MLOps, one accountable team
200+
Projects delivered
Regulated Industry Experience
Healthcare, financial services, manufacturing
Startups to Fortune 500
Trusted by clients of every scale Worldwide
Microsoft Solutions Partner
Azure ML, Power Platform, SharePoint
ISO 9001:2015 & ISO 27001:2022
Certified quality & security standards
Global development centers
Offices in USA, UK & Australia
As a reputable ML development company, we offer complete machine learning development services that assist companies in utilizing AIโs potential to spur creativity, productivity, and expansion. Our industry-wide experience assuresโฏcustomized solutions that address your unique requirements.
Before development begins, we assess feasibility, validate data readiness, and define the right technical architecture for your ML initiative to create a practical and scalable development roadmap. Our approach includes evaluating business use cases, auditing data quality, selecting suitable model strategies, identifying compliance requirements, and planning phased delivery aligned with operational priorities.
When off-the-shelf solutions don’t meet your accuracy requirements, domain specificity, or data privacy constraints, we design and train models from scratch โ or fine-tune pre-trained ones on your data. We work across supervised learning, unsupervised learning, semi-supervised approaches, and reinforcement learning depending on what your data and business objective actually call for.
A trained model sitting outside your systems is worthless. We embed ML capabilities into your existing enterprise stack โ ERP, CRM, data warehouses, APIs โ and deploy to cloud (Azure ML, AWS SageMaker, GCP Vertex), on-premise, or edge environments. We have particular depth in Azure ML and Microsoft Fabric, which gives us an edge for clients already running on the Microsoft ecosystem.
The most underrated phase of any ML project. We build automated training-to-production pipelines, configure data drift monitoring, and establish retraining workflows so your models stay accurate as real-world conditions evolve.ย This isn’t an afterthought. We design for MLOps from the first sprint โ because retrofitting observability into a production model is expensive and fragile.
Machine learning systems can lose efficiency over time due to changing datasets, evolving business conditions, or infrastructure limitations. We analyze underperforming ML systems to identify issues affecting accuracy, scalability, inference speed, and production reliability. From model architecture and feature engineering to data quality and deployment pipelines, we optimize ML implementations to improve performance and operational stability.
Before a model touches production data โ or a regulator โ we audit it for accuracy, bias, robustness, and edge-case performance. This is particularly important for healthcare, financial services, and manufacturing, where model errors have real consequences.
Classification, regression, and ranking models trained on labelled data. Common applications: fraud detection, demand forecasting, credit scoring, medical diagnosis support, customer churn prediction, and pricing optimization.
Frameworks: Scikit-learn, XGBoost, LightGBM, TensorFlow, PyTorch.
Clustering, dimensionality reduction, anomaly detection, and association mining on unlabeled data. Common applications: customer segmentation, network intrusion detection, recommendation engines, and exploratory data analysis.
Agent-based models that learn through interaction with an environment. Common applications: process optimization, supply chain scheduling, dynamic pricing, and robotics control systems.
Document classification, named entity recognition, sentiment analysis, intent detection, summarization, and information extraction. We build NLP pipelines on transformer models (BERT, RoBERTa, domain-specific fine-tuned variants) as well as leaner solutions where compute cost matters.
Image classification, object detection, segmentation, OCR, and video analysis. Common applications: quality control on production lines, medical imaging analysis, document digitization, and visual inspection automation. Architectures: YOLO variants, ResNet, EfficientNet, Vision Transformers.
Time series forecasting using ARIMA, Prophet, LSTM, and Temporal Fusion Transformers. Common applications: inventory demand planning, energy load forecasting, predictive maintenance, and financial modelling.
Statistical and deep learning approaches to identifying outliers in structured and sequential data. Common applications: fraud detection, manufacturing defect identification, cybersecurity threat detection, and medical signal monitoring.
Collaborative filtering, content-based, and hybrid recommendation systems. Common applications: product recommendations, content personalization, and next-best-action modelling for sales and marketing.
Partner with PSSPLโthe machine learning development company that blends innovation, data science, and business strategy. Our end-to-end ML development services can help you achieve your goals, whether they are smarterโฏautomation, greater insights, or customized experiences.
Start Your ML Journey with PSSPL Today!
The gap between a model that works in a notebook and a model that delivers business value at scale is called MLOps. It’s where most ML projects either mature or die. We build production ML infrastructure that covers:
Automated testing, validation, and deployment workflows so your team ships model updates without manual intervention
Centralized, versioned, and reusable feature pipelines that eliminate the inconsistency between training and serving
Statistical tests and alerting when input data or model predictions shift beyond acceptable thresholds
Scheduled or trigger-based retraining workflows with human-in-the-loop approval gates where needed
MLflow or Weights & Biases integration so every training run is reproducible
Versioned model management with rollback capability and A/B testing infrastructure
Industry-Specific Machine Learning Solutions
We develop ML systems for medical imaging analysis, patient risk prediction, hospital workflow optimization, disease detection, and healthcare data intelligence.
We build predictive maintenance systems, quality inspection models, defect detection solutions, and production optimization platforms for manufacturing environments.
Our recommendation engines, demand forecasting systems, and customer behavior analytics platforms help retailers improve conversions, inventory planning, and personalization.
Our ML engineers develop fraud detection systems, risk scoring platforms, credit assessment models, algorithmic forecasting systems, and compliance monitoring tools.
We help logistics businesses optimize routing, warehouse operations, delivery forecasting, fleet monitoring, and supply chain planning using machine learning algorithms.
We developed a scalable MLOps system for a video analytics platform with live video streams and large-scale visual data analysis using artificial intelligence technology.
The system simplified the processes of training, deploying, monitoring, and re-training of models in the cloud environment.
The solution allowed us to help our client automate machine learning processes, speed up deployment times, and increase the stability of video processing pipelines.
Moreover, the solution made it easier to monitor the quality of machine learning algorithms, detect potential issues, and optimize the AI models.
We developed a machine learning solution for demand forecasting to solve overstocking issues during the off-seasons, as well as inaccurate inventory planning, for our customer who runs a retail business in the UK.
Our forecasting solution leveraged multivariate models augmented with external factors such as weather conditions, local happenings, promotions, etc.
Based on Azure ML, the automated forecasting tool enabled the client to make better inventory-related decisions more quickly due to accurate predictions. The forecasting accuracy increased from 67% to 89%, resulting in a decrease in the inventory surplus, which saved the company ยฃ240K per quarter.
We’re direct about what we work with because vague claims about “cutting-edge technology” don’t help you evaluate whether we’re the right fit.
Temporal models purpose-built for sequential patterns outperform LLM-based forecasters on most industry benchmarks
Statistical and autoencoder approaches are interpretable, fast, and don’t hallucinate
Classic ML gives you exact probability outputs with calibration; LLMs give you stochastic text
SHAP values and decision trees are defensible in audits; LLM reasoning is not
When GenAI / LLMs make sense: Unstructured text understanding at scale, conversational interfaces, complex reasoning over documents, code generation, and content creation workflows.
Our job is to recommend the right approach โ not the most impressive-sounding one. If classic ML solves your problem, that’s what we’ll propose.
At PSSPL, we provide ML development services customized according to your business needs as a reputed machine learning development company. We ensure you that we use intelligent approaches to solve industry-specific problems to create tangible business benefits for you. We have a professional team that guarantees your ML development is both effective and valuable, irrespective of whether it’s healthcare, banking, or logistics.
Fraud detection, credit risk scores, anti-money laundering solutions, trading signal generation, and lifetime customer value predictions. We appreciate the compliance requirements โ SHAP values, LIME explanations, audit logs, and bias checks are integral to the delivery process in regulated environments.
Analysis of medical imaging, decision-making support for clinicians, readmission predictions for patients, drug discovery pipelines, and medical device data pipelines. We operate in HIPAA-aware environments and develop documentation to assist with regulatory filings.
Use of predictive maintenance based on sensor data, quality inspection, demand forecast, supply chain optimization, and production planning. With our IoT and edge computing expertise, models can be run near the origin of the data โ not only in the cloud.
Recommendation engines, dynamic pricing, inventory demand forecasting, customer churn prediction, and personalization models. We've built systems handling millions of daily predictions at sub-100ms latency.
Optimization of delivery routes, fleet maintenance using predictive analytics, demand-supply analysis, slot optimization in warehouses, and delivery time estimations.
Document analysis, contract analysis, advanced search algorithms, and churn modeling for clients in software companies. Natural language processing and classification algorithms for understanding unstructured corporate data.
One of the most common questions enterprise buyers ask โ and one of the least-answered pages in the ML services space. Machine learning development cost varies significantly based on five factors:
4โ8 weeks. Validates whether ML can solve your problem with your data before full commitment.
3โ5 months. One well-defined problem, clean data, baseline MLOps.
6โ18 months. Multiple models, feature store, automated pipelines, monitoring.
Ongoing. Model monitoring, retraining, performance tuning, and new feature development.
We provide a free scoping call and fixed-scope proposals for clearly defined projects. For complex, multi-phase ML programmes, we work in time-and-materials sprints with transparent milestone-based billing.
We've been building production software since before "machine learning" was a marketing term. That means we understand data architecture, enterprise integration, and what it actually takes to keep a system running in production โ not just to demo it.
Deep expertise in the Microsoft ecosystem. Weโre experts in Azure ML and Power Platform from day one. This will greatly minimize integration risks and reduce time-to-delivery for organizations using Microsoft technologies.
From use case validation, all the way through model training, integration, deployment, and maintenance in an MLOps pipeline โ there wonโt be any handoffs. Youโll have the same team responsible for the whole process.
We work across time zones with structured communication rhythms. No 48-hour delays. No handoff confusion.
We've turned away ML projects that weren't ready for ML. We'd rather help you get your data infrastructure right first and come back in six months than take your budget for a project that was going to fail anyway.
Not a generic inquiry form. A real conversation with someone who’s worked on ML projects in production.
Tell us what you’re trying to solve. We’ll tell you honestly whether ML is the right approach, roughly what it would take to get there, and whether we’re a good fit for it.
Machine learning development refers to the full development cycle of systems that learn from input data to predict outcomes, classify data, and make decisions without having to be explicitly coded for every scenario. This involves the entire cycle, starting from data engineering up to monitoring the model in production environments.
Estimates run between ยฃ15,000โยฃ35,000 for a targeted proof-of-concept solution, and up to ยฃ80,000โยฃ300,000+ for a complete ML implementation with MLOps support. The major factors determining price include data preparation needs, model sophistication, integration, and maintenance considerations. Get your no-cost project scoping session.
A targeted proof of concept (PoC) will take about 4 to 8 weeks. A production-ready machine learning model, complete with integration and MLOps, will take between 3 to 6 months to implement for a problem that is clearly understood.
Machine Learning is the larger field of study. Deep Learning is a subset that employs neural nets with multiple layers. In structured or tabular data applications, traditional ML algorithms such as XGBoost, Random Forest, and Support Vector Machines generally do better than Deep Learning and cost significantly less to implement.
When faced with structured data, requirements for interpretability, latency or cost limitations, or regulations within an industry, opt for traditional machine learning techniques. For tasks involving large-scale unstructured text processing, developing conversational agents, or reasoning on complex documents, choose LLMs/GenAI. We will provide an unbiased suggestion during the scoping process.
Yes. We run structured ML feasibility assessments โ typically 2โ3 weeks โ that validate your use case, audit your data, and produce a recommended architecture and phased development plan. This can be done as a standalone engagement before committing to full development.
Financial Services (Fraud detection, Credit scoring), Healthcare (Clinical Decision Support, Medical Imaging), Manufacturing (Predictive Maintenance, Quality Inspection), Retail (Demand Forecasting, Recommendation Engines), Logistics, and Professional Services. For more information on some use cases, look at our case studies below.
Indeed โ and this is one of our most powerful value propositions. Over 25 yearsโ experience building on the Microsoft platform. Azure ML, Azure AI Foundry, Microsoft Fabric, Power Platform, SharePoint โ getting your Microsoft stack to work with ML is second nature to us.
Yes, we consider MLOps to be mandatory for production implementations. We develop training pipelines, drift monitoring, automated retraining, and model registry capabilities โ all built into our first sprint, not added after implementation.
We are able to develop and train our models in your own cloud infrastructure (no data will be exported from your tenancy), use differential privacy methods, and develop data processing protocols that meet GDPR, HIPAA, and industry-specific standards.
