Unlock strategic advantage with self-learning, data-driven machine learning solutions that enhance decision-making and operational efficiency. From strategy to deployment, our ML consulting helps your organization operate smarter and grow with confidence.
Oleg Kalyta
Founder & AI Lead
Free Trial
Test our team, no commitmentProof of Concept
Working prototype deliveredProduction Ready
Full AI solution deployed
End-to-end ML consulting services covering the entire machine learning lifecycle, from strategy to production.
We assess your business goals, customer requirements, and existing data to define where machine learning can deliver measurable impact. The outcome is a clear ML strategy aligned with corporate objectives and realistic implementation paths.
We define dataset requirements, evaluate data quality, and prepare reliable data pipelines. This includes data cleaning, preprocessing, and validation to ensure your machine learning models are built on a solid, accurate foundation.
We design end-to-end ML solutions, from selecting suitable algorithms to defining system architecture and development stages (PoC, MVP, production). This ensures your ML initiative is technically sound, scalable, and aligned with business needs.
We develop, train, and fine-tune machine learning models tailored to your use cases. Through rigorous testing and optimization, we turn raw data into intelligent solutions that support automation, prediction, and decision-making.
Your ML models are integrated into existing infrastructure and business workflows. We support deployment, CI/CD pipelines, version control, monitoring, and scalability to ensure reliable performance in production environments.
After deployment, we continuously monitor model performance, retrain when needed, and provide ongoing support. This keeps your machine learning solutions accurate, stable, and aligned with evolving business requirements over time.
Building the model is just the beginning. Our MLOps consulting services ensure your machine learning solutions run reliably in production with proper monitoring, governance, and automated retraining.
Automated pipelines that test, validate, and deploy ML models with the same rigor as traditional software. Version control for models, data, and experiments means you can reproduce results and roll back when something breaks.
Production models need constant attention. We set up drift detection, performance tracking, and alerting systems that catch degradation before it hits your bottom line.
Data changes, models get stale. Our MLOps consulting includes building retraining workflows that kick in automatically when performance drops below your thresholds.
Centralized feature repositories keep training and serving in sync. We help teams avoid the training-serving skew that causes so many production failures.
Every model version tracked with full lineage and approval status. For regulated industries, we build audit trails that satisfy compliance requirements.
Infrastructure as code for training and serving. Terraform, Kubernetes, and cloud-native tools provision ML environments that scale with demand without manual intervention.
Real ML implementations. Real business impact. See how we've helped companies leverage machine learning.
AI-Powered Health Companion
We built an AI health app with multi-agent ML system for personalized wellness recommendations. The app integrates Apple HealthKit, processes data with OpenAI and LangChain, and uses vector databases for context-aware insights.
Intelligent Ticket Handling
ML-powered support agent that handles thousands of tickets daily with context-aware responses and automatic escalation.
Turn your data into a competitive advantage with machine learning solutions designed to achieve real business goals
Turn raw data into strategic intelligence.
We help you extract actionable insights from complex datasets, enabling smarter decisions across operations, finance, and customer engagement.
Automate decisions and workflows
Machine learning models automate repetitive processes, increasing speed, accuracy, and consistency while reducing manual effort.
Optimize operations with measurable impact.
From predictive analytics to intelligent automation, our ML solutions are designed to improve efficiency, reduce costs, and deliver clear business outcomes.
Built to scale with your business
Our custom machine learning solutions integrate seamlessly with existing systems and evolve as your data, processes, and business needs grow.
From custom models to production-ready MLOps, we cover the full spectrum of ML capabilities
We design and develop machine learning models for classification, regression, prediction, clustering, anomaly detection, and more. Using supervised, unsupervised, reinforcement, and hybrid learning approaches, our models are built to solve real business tasks and perform reliably in production.
Turn user and product data into personalized experiences. Our ML-powered recommendation systems analyze historical behavior and contextual signals to deliver accurate, relevant suggestions that improve engagement, conversion rates, and customer satisfaction.
We apply AI and ML to automate and enhance analytics across the entire data lifecycle. This enables teams to uncover patterns faster, generate actionable insights, and make data-driven decisions without relying solely on technical specialists.
Leverage AI technologies that work with unstructured data such as text, images, audio, and video. Using computer vision, natural language processing (NLP), speech recognition, and text analytics, we build cognitive solutions that support automation, insight discovery, and innovation.
We develop deep learning models that recognize complex patterns in large-scale datasets, including images, text, and audio. These models improve over time and support advanced use cases such as image recognition, predictive analytics, and natural language understanding, with cloud-based training for faster iteration.
Ensure consistent performance and governance of ML models in production. Our ModelOps approach supports automated monitoring, validation, compliance, and risk management, helping you maintain accuracy, reliability, and business value across all deployed models.
Beyond traditional ML: generative AI, explainable models, edge deployment, and privacy-preserving techniques for enterprises pushing the boundaries of what machine learning can do.
Custom LLM applications built on GPT-4, Claude, and open-source models. We handle fine-tuning, RAG architectures, and prompt engineering to make AI that actually understands your business.
Black-box models are a liability when regulators come knocking. We add interpretability layers that explain why a model made each prediction—in terms business stakeholders understand.
Image classification, object detection, and video analytics for manufacturing quality control, retail analytics, medical imaging, and security. Built to work in real conditions, not just on clean test datasets.
Text classification, entity extraction, sentiment analysis, and semantic search at scale. We process documents, support tickets, contracts, and customer feedback to surface actionable insights.
ML models compressed and quantized for edge devices, IoT sensors, and mobile apps. When cloud latency is too slow or connectivity is unreliable, we bring the model to the data.
Train models across distributed data sources without moving sensitive information. Essential for healthcare, finance, and any multi-party collaboration where data privacy is non-negotiable.
We select technologies that ensure ML solutions are accurate, scalable, and ready for real-world business use
OpenAI API
LangChain
Pinecone
LangGraph
Hugging Face
TensorFlow
Node.js
NestJS
Python
FastAPI
PostgreSQL
Redis
ReactReact Native
Next.js
Expo
TypeScript
AWS
Google Cloud
Docker
Kubernetes
GitHub ActionsTurn your data into strategic insight and automation that scales. Let's discuss your goals and build solutions that deliver real value.
Understand your business questions, available data, and operational context to define where ML can deliver the most value.
Rank potential ML projects based on ROI, feasibility, and alignment with strategic goals to focus efforts where they matter most.
Engineering work begins with feature selection, model training, validation, and refinement using your data.
Models are embedded into workflows, APIs, dashboards, or automation pipelines for production use.
Performance tracking, retraining, and incremental updates keep models relevant and effective over time.
Our machine learning consulting services are adaptable to the needs of different industries, helping organizations turn data into better decisions, efficient operations, and real business outcomes.
Predict patient outcomes, personalize treatment plans, and optimize clinical workflows. From diagnostics assistance to drug discovery and preventive care analytics, ML helps healthcare providers deliver smarter, data-driven services while meeting HIPAA requirements. See how we built Healify.
Predictive maintenance that prevents downtime before it happens. Automated quality control that catches defects human inspectors miss. Production optimization that maximizes throughput. ML is transforming factory floors worldwide.
Demand forecasting that accounts for seasonality, promotions, and market shifts. Inventory optimization that balances carrying costs against stockouts. Route planning that adapts to real-time conditions. ML brings clarity to complex supply networks. See how we built EvLuv.
Fraud detection that catches sophisticated attacks in milliseconds. Credit scoring that goes beyond traditional factors. Algorithmic trading, risk modeling, and regulatory compliance. ML helps financial institutions make better decisions faster. See how we built Finsu.
Recommendation engines that drive conversion. Dynamic pricing that maximizes margins. Customer segmentation for targeted marketing. Demand forecasting for inventory planning. ML powers the personalized shopping experiences customers now expect. See how we built Beauty Advisor.
Content recommendation that keeps viewers engaged. Audience segmentation for targeted advertising. Content moderation at scale. Viewership prediction for programming decisions. ML personalizes entertainment experiences.
Transparent pricing based on project scope and complexity. Here's what typical ML initiatives cost based on projects we've delivered.
$15,000 – $30,000
4–6 weeks
Validate feasibility before committing to full development. We build a working prototype with your data to demonstrate exactly what ML can achieve for your specific use case.
Best for: Companies exploring ML, validating use cases, or seeking investor demos
$30,000 – $70,000
8–14 weeks
Production-ready machine learning solution with core functionality. Ideal for startups seeking funding or enterprises piloting AI in a specific business unit.
Best for: Startups heading into funding rounds, enterprises piloting AI
$70,000 – $150,000+
3–9 months
Comprehensive machine learning systems with multiple models, data pipelines, and deep enterprise integrations. Includes full MLOps infrastructure.
Best for: Large organizations, regulated industries, mission-critical ML systems
Clean, structured data speeds up development. Messy or sparse data means more time on preparation. Multiple data sources, real-time streaming, or unstructured formats (images, text, audio) add complexity.
A basic classification model costs less than a custom deep learning architecture. If you need 99%+ accuracy or the model must handle edge cases gracefully, expect more iteration and testing.
Connecting to a single API is straightforward. Integrating with legacy ERPs, multiple data warehouses, and real-time systems requires more engineering. Each integration adds scope.
If you need automated retraining, drift detection, model versioning, and governance workflows, the infrastructure investment is higher. Essential for production systems, optional for PoCs.
Healthcare, finance, and government projects come with HIPAA, SOC 2, or FedRAMP requirements. Compliance adds 15-25% to project costs but is non-negotiable in regulated industries.
Plan for 15-25% of initial build cost annually. Models need retraining as data drifts, integrations need updates when APIs change, and new requirements emerge as the business evolves.
We focus on machine learning customized solutions that deliver real business value, not experiments that stall after the pilot phase.
Our machine learning consultants work directly with ML models, data pipelines, and production systems. This means faster, more reliable results based on proven algorithms, tools, and real implementation experience, not trial and error.
We adapt machine learning models to your existing business processes, data sources, and technical environment. Every solution is designed around your operational, management, and growth goals, not generic templates.
From data quality issues to biased models and fragile deployments, we identify and mitigate ML risks early. This reduces costly rework and ensures stable, trustworthy outcomes once models move into production.
Using proven frameworks, workflows, and team enablement practices, we help you implement ML initiatives faster (often in weeks or months instead of years) without the need to hire or scale an in-house ML team.
What most impressed me about ProductCrafters was their dedication to my project and understanding of our goals. They were very honest and transparent throughout the entire process.
They were flexible, and it was easy to work with them on a day-to-day basis. Their brilliant ideas were critical to the project success.
Out of over 40 applicants, we selected ProductCrafters based on their experience, technical expertise, and cost estimate. The team showed deep technical expertise, a strong work ethic, and honesty.
The team has honest billing practices and creates incredible value for the cost. Working with ProductCrafters has saved us hundreds of thousands of dollars compared to domestic firms.
The quality of their code makes them a valuable partner. They thought holistically about solutions and brought up all-encompassing ideas.
Their insightful advice has maximized the application's performance. We're actually learning things from ProductCrafters that we can adapt and use in other applications.
Customer stories
View on Clutch
Free Trial
Test our team, no commitmentProof of Concept
Working prototype deliveredProduction Ready
Full AI solution deployedOleg Kalyta
Founder & AI LeadOur machine learning consulting company embeds ML models directly into your workflows and systems, delivering actionable insights without disrupting day-to-day operations.
Connect ML models to your existing databases, CRMs, ERPs, and IoT systems. We ensure clean, structured data flows seamlessly into models for consistent and reliable predictions.
Deploy ML models that trigger automated actions — like routing support tickets, adjusting inventory levels, or flagging anomalies — directly within your operational processes.
Implement ML models in production with CI/CD pipelines, automated testing, and monitoring dashboards to track performance, detect drift, and update models as conditions change.
Design solutions that scale with your business, supporting additional data sources, increasing request volumes, and new requirements without disrupting operations.
Enable ongoing retraining, performance tuning, and model versioning to ensure predictions stay accurate and workflows remain optimized as your business evolves.
Learn how to build AI models from scratch, covering data preparation, algorithm selection, training, and deployment for real-world applications.
Read article
Explore how deep learning models process complex data, recognize patterns, and drive intelligent automation across industries.
Read article
Practical guide to adding AI technology into applications, from selecting the right approach to deployment and optimization.
Read article