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Supported by over 10+ years of experience as a machine learning development company, we build and launch ready-to-use machine learning models that change how companies work, creating measurable improvements and lasting value.
Our Core Capabilities
Machine learning systems built, scaled,
and sustained over time
ML-Powered Solutions Delivered
Custom ML Models Trained and Deployed
Bespoke LLMs Fine-Tuned
Strategic ML Partnerships
Industries Mastered
Enterprise ML Integrations Completed
As a trusted machine learning consulting company, we help enterprises define ML strategies that align with existing infrastructure, reduce decision bottlenecks, and deliver measurable returns on every initiative.
reduction in time-to-decision for an enterprise
that replaced manual analytics workflows with ML-driven advisory systems built on modular AI architecture and cloud-native data pipelines
We deliver custom machine learning development services using proven algorithms, strong data engineering, and industry-specific training pipelines designed for accuracy, scale, and long-term performance.
improvement in model accuracy
for a platform that transitioned from rule-based logic to custom-trained ML models with automated hyperparameter tuning and distributed training on GPU clusters
We offer deep learning development, with neural architecture including CNNs, RNNs, LSTMs, and Transformers for image recognition, language processing, and predictive analytics at enterprise scale.
faster inference time for a computer vision system
processing high-volume image data across distributed GPU environments using optimized CNN architectures and model compression techniques
We turn complex datasets into production-ready ML workflows, covering ETL pipelines, model management, Kubernetes orchestration, and CI/CD integration for reliable, long-term performance.
reduction in pipeline failure rates
for an enterprise ML system handling multi-source data ingestion, real-time model serving, and Kubernetes-orchestrated workload management across hybrid cloud infrastructure
We integrate ML models directly into your ERP, CRM, or custom platforms via REST APIs and serverless architectures, keeping existing operations intact while adding real-time intelligence.
increase in operational efficiency for a business
that embedded predictive ML models into core ERP and CRM systems via REST APIs and serverless architectures without disrupting live workflows
We build MLaaS solutions on AWS SageMaker, Azure ML, and Google Vertex AI, giving enterprises instant access to predictive models, anomaly detection, and personalization tools as plug-and-play APIs.
lower infrastructure overhead for an enterprise
that migrated from on-premise ML environments to GPU/TPU-backed cloud infrastructure on AWS SageMaker with auto-scaling inference endpoints
We implement end-to-end MLOps practices including automated CI/CD pipelines, model registry, drift detection, and live telemetry monitoring so your ML models stay accurate, compliant, and production-ready.
model uptime maintained for an enterprise ML ecosystem
running automated CI/CD pipelines, continuous drift detection, and rollback workflows across multi-cloud production environments
Strong capabilities across
Industry recognition
US government AI frameworks
Enterprise-grade implementation
MUDRA wanted to simplify personal finance by offering a smarter, interactive way for users to manage budgets. Traditional apps lacked real-time insights and conversational interfaces for better user engagement.
We built a chatbot-powered budget management tool using AI and NLP. It helps users monitor spending patterns, set savings goals, and receive personalized financial recommendations.
Finding quick employment for blue-collar workers was inefficient due to fragmented platforms and outdated application processes.
We developed JobGet, an AI-driven employment platform that connects job seekers and employers in real time. Its ML-based recommendation engine matches workers to the best-fit jobs instantly.
Vyrb aimed to redefine social interactions by enabling seamless audio messaging, especially for wearable devices, but needed an ML-driven, scalable backend.
As an experienced machine learning solutions company, we built Vyrb, an AI-powered social media app optimized for Bluetooth wearables, enabling users to exchange audio messages hands-free.
SMBs struggled to make sense of lengthy reports and lacked affordable access to high-level business consulting.
We built MyExec, an AI-powered platform that reads documents, identifies critical points, and delivers tailored recommendations instantly through a conversational interface.
Americana Group needed to streamline last-mile delivery operations across multiple countries and reduce inefficiencies in order handling.
We developed ALMP, a real-time logistics intelligence system that optimized order distribution, strengthened geofencing rules, and provided managers with live operational insights.
Flynas wanted a digital solution to modernize travel experiences, simplify booking processes, and increase customer retention.
We enhanced the app with a sleek UI and AI-driven chatbot, ensuring intuitive user journeys and building a foundation for scalability and sustained growth.
Founder & CEO
Epluribus LLC - Creators of MOXY
Builds scalable, business-ready ML infrastructures while making sure they connect well with current workflows, cloud platforms, and company systems.
Keeps data pipelines reliable and high quality by cleaning, changing, and organizing datasets for better model training and results.
Works on launching production-ready ML models, allowing for ongoing improvements, live performance checks, and quick problem fixing.
Ensures every ML solution follows regulatory rules, ethical AI guidelines, and company security requirements to maintain trust and openness.
Builds advanced neural network designs that handle complicated data processing jobs with high accuracy, making smart automation possible at a large scale.
Makes the whole ML process automated, keeping CI/CD workflows smooth, repeatable, and scalable for enterprise-level ML launches.
Changes raw data into useful insights by using statistical modeling, testing, and feature engineering to support data-based decisions.
Ensures ML model launches are secure and scalable on AWS, Azure, or Google Cloud, thereby improving computing resources for cost savings and enhanced performance.
Focuses on creating natural language models that understand, read, and produce human language for uses like chatbots, sentiment analysis, and virtual assistants.
Is Your Enterprise Ready to Scale
With Machine Learning?
We bring together advanced algorithms, cloud-native architecture, and compliance-ready workflows to transform how enterprises operate
CCPA (California Consumer Privacy Act)
SOC 2 (Service Organization Control 2)
ISO/IEC 27001 (Information Security Management)
ISO/IEC 23894 (AI Risk Management)
OECD AI Principles (Trustworthy and Ethical AI Practices)
NIST AI Risk Management Framework (Model Transparency and Reliability)
HIPAA (Health Insurance Portability and Accountability Act)
Federal Trade Commission (FTC) AI Guidelines on fairness and transparency
AI Bill of Rights (U.S. White House blueprint for responsible AI use)
ISO/IEC 42001 (AI Management System Standard – published in 2023)
MLOps Best Practices (Continuous Integration & Deployment for ML Models)
ONNX (Open Neural Network Exchange for Model Interoperability)
Responsible AI Guidelines (Bias Detection and Fairness Audits)
Explainable AI (XAI) (Frameworks like SHAP and LIME)
Cloud AI Standards (AWS SageMaker, Azure ML, GCP Vertex AI)
Model Governance Standards for Versioning & Auditability
IEEE 7000 Series (Ethical Concerns in AI – includes bias, privacy, transparency)
ISO/IEC TR 24028 (AI Trustworthiness Overview)
ISO/IEC TR 24027 (Bias in AI Systems and ML)
ISO/IEC TR 24029 ISO/IEC TR 24029 (Robustness and Accuracy of AI Models)
Federated Learning Standards (Privacy-preserving ML collaboration)
Edge AI Standards (AI processing on devices with limited compute resources)
Synthetic Data Standards (for testing and bias reduction in ML models)
Every ML solution we deliver is audit-ready from day one.
CCPA (California Consumer Privacy Act)
SOC 2 (Service Organization Control 2)
ISO/IEC 27001 (Information Security Management)
ISO/IEC 23894 (AI Risk Management)
OECD AI Principles (Trustworthy and Ethical AI Practices)
NIST AI Risk Management Framework (Model Transparency and Reliability)
HIPAA (Health Insurance Portability and Accountability Act)
Federal Trade Commission (FTC) AI Guidelines on fairness and transparency
AI Bill of Rights (U.S. White House blueprint for responsible AI use)
ISO/IEC 42001 (AI Management System Standard – published in 2023)
MLOps Best Practices (Continuous Integration & Deployment for ML Models)
ONNX (Open Neural Network Exchange for Model Interoperability)
Responsible AI Guidelines (Bias Detection and Fairness Audits)
Explainable AI (XAI) (Frameworks like SHAP and LIME)
Cloud AI Standards (AWS SageMaker, Azure ML, GCP Vertex AI)
Model Governance Standards for Versioning & Auditability
IEEE 7000 Series (Ethical Concerns in AI – includes bias, privacy, transparency)
ISO/IEC TR 24028 (AI Trustworthiness Overview)
ISO/IEC TR 24027 (Bias in AI Systems and ML)
ISO/IEC TR 24029 ISO/IEC TR 24029 (Robustness and Accuracy of AI Models)
Federated Learning Standards (Privacy-preserving ML collaboration)
Edge AI Standards (AI processing on devices with limited compute resources)
Synthetic Data Standards (for testing and bias reduction in ML models)
Every ML solution we deliver is audit-ready from day one.
We build ML systems that naturally meet tough data protection and AI regulations, including GDPR, CCPA, HIPAA, ISO/IEC 42001, and the coming EU AI Act. By weaving compliance checks into every part of the development process, we draft ML-powered solutions that stay both groundbreaking and legally robust.
Our team incorporates production-quality MLOPs skills into every project, seamlessly integrating ongoing training, validation, deployment, and monitoring. From automated CI/CD pipelines to expandable monitoring systems, we keep your machine learning setup running smoothly at an enterprise level.
We focus on responsible AI development by integrating fairness checks and bias-detection tools directly into our workflow. Our machine learning solutions development process focuses on transparent AI methods like SHAP and LIME; keeping model decisions interpretable and easy to track.
Our engineers work well with major AI platforms like AWS SageMaker, Azure ML, and Google Vertex AI, and also create custom solutions for private infrastructure. Whether you need large-scale cloud ML systems or instant processing at the edge, we build setups that integrate with your current technology and deliver reliable results.
Mudra, MyExec, JobGet, and ALMP trusted us with high-impact ML platforms where
accuracy, governance, and uptime were non-negotiable. Put the same standards
behind
your project.
The India's Fastest-Growing Companies 2026 Recognised by TIME and Statista
The Financial Times - High-Growth Companies Asia-Pacific 2026
Statista High Growth Companies
APAC
2025 & 2024
Statista India's Growth Champions 2023
AI is used to automate routine tasks, as well as make better predictions using ML models. The systems adapt to changes in business data over time.
Generative models are used in our teams to accelerate product development, including virtual assistants and synthetic datasets in ML training.
Agent-based systems are developed using learning models. They are self-based, reduce manual work, and remain dependable on a regular basis.
Using ML and deep learning, we build computer vision features to detect objects, perform inspections, and enable medical imaging and live monitoring.
We put NLP into practice with chatbots, sentiment analysis and search, allowing teams to make sense of text and speech data with the help of ML.
We apply data mining based on ML techniques to identify trends in large data. Such trends are commonly used to make pragmatic business choices.
As a deep learning development company, we create deep learning models for complex recognition tasks, including speech-to-text systems, predictive analysis, and personalized recommendations.
We put RPA to work automating repetitive, rule-based tasks in finance, HR, and operations. This cuts down on manual mistakes, reduces costs, and lets teams spend time on strategic work.
We run ML applications on cloud platforms like AWS, Azure, and Google Cloud to support growth and security, keeping processes running smoothly, and accelerating model training.
We combine big data infrastructure with ML analytics to process real-time data, predict demand, and make better business decisions.
Bring the power of automation and advanced
analytics to your core systems
Machine learning solutions have numerous applications, starting with a simple chatbot and including sophisticated language models such as ChatGPT or SORA. Therefore, the machine learning app development cost may greatly vary depending on the features implemented.
The average cost of ML development services is between $30,000 and $300,000 (or more). This is, however, an estimated figure. This cost estimation may be reduced or increased with several factors, including the complexity of the project, features, choice of platform, where the development team will be based, and so on.
We will be able to assist you in getting more accurate approximations of your bespoke ML application development. Contact us to get the precise cost estimates!
The duration required to develop an ML model is dependent on several aspects, such as the complexity of the product, the features required, the availability of data, and the level of expertise of the team building. Generally, after going through the process of building a machine learning model, a simple ML application with minimum functionalities can be developed within 4 to 6 months. In contrast, an elaborate and advanced solution with sophisticated functionalities might take 6 months to 1 year or even longer to develop.
A more accurate timeline or schedule can be obtained by contacting a reputable machine learning solutions company like ours.
Machine learning development services offer several significant benefits to businesses across industries:
Yes. Appinventiv provides custom machine learning solutions for business optimization, including demand forecasting, process automation, customer behavior analysis, and operational efficiency improvements. Our ML solutions are tailored to meet specific business goals and drive measurable ROI.
Appinventiv offers personalized machine learning development, designing models and algorithms that cater to the unique needs of your industry, business model, and data architecture. We ensure the ML solutions are scalable, accurate, and aligned with your business objectives.
Yes, our machine learning development services will comprise all the stages of development of ML, as well as the process of support and maintenance.
We counter the machine learning model challenges with a thorough systematic analysis of data, a wide range of datasets, and fairness algorithms. We maintain a constant monitoring and validation process to ensure that the custom-built solutions are unbiased, non-discriminatory, and consistent with our ethical values.
The tools & frameworks used in ML development include coding languages such as Python, R, and Java, or even strong ML libraries and systems. Popular model-building and training frameworks are Tensorflow, PyTorch, Scikit-learn, and Keras.
Apache Spark, Pandas, and NumPy are popular data processing libraries, and MLOps frameworks such as MLflow, Kubeflow, and SageMaker make it easier to deploy and monitor models. There are also cloud-based services, such as AWS SageMaker, Azure ML, and Google Vertex AI, which help to scale ML development.
To integrate ML into an existing application, start by identifying a clear use case, such as predictive analytics, recommendation engines, or NLP-powered chatbots. Next, develop the ML model and connect it to the application via APIs or microservices. The model can then be hosted in the cloud and scaled as needed, allowing seamless integration without disrupting current workflows.
Appinventiv follows a full-cycle ML development approach, from data collection and preprocessing, model selection, training, and validation to deployment, monitoring, and continuous improvement. This ensures that ML features are not only accurate but also scalable, secure, and aligned with business objectives.
