Machine Learning Development Services

Our Machine Learning services

We offer three core groups of machine learning development services to transform your business with machine learning:

1. Consulting & discovery, where we assess your data and goals to craft a clear ML strategy;

2. Model engineering & intelligence design, where we build and fine-tune advanced models like neural networks and LLMs for performance and scale;

3. Productization & deployment, where we deliver fully integrated, production-ready ML systems with ongoing support.

Consulting & discovery

We help you make smart decisions before you invest in development. Our Machine learning consulting services allow your team to assess your data, explore opportunities, and define a clear ML strategy. With our Machine Learning services, you’ll know what to build, why to invest in ML development, and how to do it in the right way, before writing a single line of code.

ML consulting

We evaluate your current data, tech stack, workflows, and business goals to identify where machine learning will make a measurable difference. We strive to uncover real opportunities that fit your business. It could be automating manual processes, improving decision-making, or reducing operational costs.

By the end, you’ll know where ML fits, what it can do, what it can’t, and how to move forward without guesswork.

You’ll receive clear, jargon-free answers to questions like:

Is ML the right solution for this problem?

What type of models fit your case: regression, classification, clustering, or something custom?

What data do you need – and is your current data usable?

Strategy building

We help you build a practical, phased ML development and implementation strategy tailored to your business. These services include defining high-value use cases, mapping available data, setting clear success criteria, estimating timelines, and choosing the right technical approach.

This isn’t a theoretical roadmap for machine learning development – it’s a step-by-step execution plan that aligns with your business needs, technical reality, and budget.

Deliverables typically include:

prioritized list of use cases, with estimated ROI;

data readiness analysis and gap identification;

suggested model types and architecture;

development plan with effort, cost, and resources required.

Use-case validation

Before investing time and budget into development, we validate whether your ML idea can actually deliver value – using rapid prototyping, existing datasets, and small-scale experiments.

As a result of these ML services, you’ll receive a validation report that includes technical feasibility, business impact estimates, risks, and a go/no-go recommendation. If it’s viable, we will show you how to build it. If it’s not, we’ll explain why – saving you months of wasted effort.

We’ll answer:

Can this be solved with ML?

Will the data support it?

What level of accuracy or performance is realistically achievable?

How much will it cost to build and run?

ML consulting

By the end, you’ll know where ML fits, what it can do, what it can’t, and how to move forward without guesswork.

You’ll receive clear, jargon-free answers to questions like:

Is ML the right solution for this problem?

What type of models fit your case: regression, classification, clustering, or something custom?

What data do you need – and is your current data usable?

Strategy building

This isn’t a theoretical roadmap for machine learning development – it’s a step-by-step execution plan that aligns with your business needs, technical reality, and budget.

Deliverables typically include:

prioritized list of use cases, with estimated ROI;

data readiness analysis and gap identification;

suggested model types and architecture;

development plan with effort, cost, and resources required.

Use-case validation

Before investing time and budget into development, we validate whether your ML idea can actually deliver value – using rapid prototyping, existing datasets, and small-scale experiments.

We’ll answer:

Can this be solved with ML?

Will the data support it?

What level of accuracy or performance is realistically achievable?

How much will it cost to build and run?

Model Engineering & Intelligence Design

Once you’ve defined your ML strategy, we help you design and build models that work – fast, accurate, production-ready. We can adjust existing models or train new ones from scratch, or engineer intelligence that delivers on your business goals.

Deep learning / neural networks

We leverage the power of machine learning to build and train advanced deep learning models to solve complex problems with unstructured data – images, video, audio, and natural language. In our services, we work with computer vision, speech recognition, and NLP, designing architectures configured to work with your data and reach your objectives.

You get models built for production: optimized for speed, accuracy, and deployment at scale – with the full training pipeline, data processing, and monitoring included.

We work with:

Convolutional Neural Networks (CNNs) for image and video processing;

recurrent and attention-based networks for sequence data and time series;

transformers for NLP, classification, and generative tasks.

Generative AI / LLMs

These services imply that we integrate and fine-tune generative AI models – from OpenAI’s GPT to open-source LLaMA and Claude – to power internal tools, Client-facing apps, and automated workflows. You get LLMs that are aligned with your data, your domain, and your business logic.

We handle the full stack: prompt engineering, embedding models, vector databases, API design, and secure cloud or on-prem hosting. You get GenAI that actually works, with guardrails, context awareness, and business value.

We build:

chatbots and virtual assistants that understand your Clients;

AI agents that summarize, draft, or translate documents;

Retrieval-Augmented Generation (RAG) pipelines using your data;

fine-tuned models with enterprise knowledge, ready for secure deployment.

AutoML

We set up AutoML frameworks that automate model selection, training, and tuning – accelerating development and reducing manual overhead. Ideal for fast prototyping, internal analytics tools, or scaling ML across non-expert teams.

You get faster time-to-first-result without sacrificing quality. As a part of our services, we also handle custom overrides and manual fine-tuning when AutoML reaches its limits – so you’re not locked into a black box.

We work with platforms like:

Google Vertex AI;

AWS SageMaker Autopilot;

H2O.ai;

MLflow Pipelines.

Model optimization & tuning

We fine-tune ML models for better performance, faster inference, and lower resource usage – without compromising accuracy. This includes hyperparameter tuning, pruning, quantization, batching strategies, and hardware-specific optimizations (GPU/TPU).

Used when:

inference is too slow for real-time apps;

costs spike with scale;

latency or memory footprint becomes a blocker;

you’re moving from prototype.

Deep learning / neural networks

You get models built for production: optimized for speed, accuracy, and deployment at scale – with the full training pipeline, data processing, and monitoring included.

We work with:

Convolutional Neural Networks (CNNs) for image and video processing;

recurrent and attention-based networks for sequence data and time series;

transformers for NLP, classification, and generative tasks.

Generative AI / LLMs

We build:

chatbots and virtual assistants that understand your Clients;

AI agents that summarize, draft, or translate documents;

Retrieval-Augmented Generation (RAG) pipelines using your data;

fine-tuned models with enterprise knowledge, ready for secure deployment.

AutoML

We work with platforms like:

Google Vertex AI;

AWS SageMaker Autopilot;

H2O.ai;

MLflow Pipelines.

Model optimization & tuning

Used when:

inference is too slow for real-time apps;

costs spike with scale;

latency or memory footprint becomes a blocker;

you’re moving from prototype.

Productization & deployment

We don’t stop at models in our ML development services. We deliver full machine learning systems – deployed, integrated, and built to scale. From early prototypes to production-grade solutions, we handle the tech, the stack, the ML development, and the transition from lab to business.

End-to-end ML development

We guide the full ML development process, turning raw business data into a deployed, production-ready product. You get a complete ML-powered system: model, backend, frontend, APIs, and infrastructure.

The result: a fully operational software solution, powered by Machine Learning.

That includes:

data pipelines and storage;

model development and training;

UX and user-facing features;

CI/CD pipelines for continuous improvement;

testing, deployment, and monitoring tools.

AI-driven product architecture

We design scalable product architectures that support ML from day one – optimized for data flow, model retraining, monitoring, and fault tolerance.

This ensures your ML product is not only functional – it’s reliable, maintainable, and built to evolve.

You get:

cloud-native or on-prem architecture diagrams;

component responsibilities and communication logic;

ML model deployment strategy (API-based, batch, streaming, edge);

data governance and observability best practices.

Pilot projects / MVP

We build and deliver ML-powered MVPs in 12-14 weeks. The result of these services is a working prototype connected to real data, tested with real users, and built with production-readiness in mind.

Ideal for:

validating ideas before a full-scale rollout;

getting stakeholder buy-in with tangible results;

testing data quality, model viability, and integration flows.

Integration into business systems

We integrate ML models into your existing software, tools, and workflows – CRMs, ERPs, data lakes, analytics platforms, mobile apps, and more.

You get:

stable API connections or embedded ML modules;

real-time or batch predictions triggered by your systems;

clean data flow in both directions;

documentation and support for your internal teams.

ML systems are not “set and forget.” As a part of our Machine Learning app development services, we provide continuous support, performance monitoring, retraining, and infrastructure scaling – so your solution keeps delivering as your business grows.

You get a partner who stays with you post-launch – keeping your AI sharp, stable, and scalable.

We handle:

monitoring model accuracy and drift;

retraining and A/B testing strategies;

infrastructure upgrades and horizontal scaling;

SLA-based support and incident resolution.

End-to-end ML development

The result: a fully operational software solution, powered by Machine Learning.

That includes:

data pipelines and storage;

model development and training;

UX and user-facing features;

CI/CD pipelines for continuous improvement;

testing, deployment, and monitoring tools.

AI-driven product architecture

We design scalable product architectures that support ML from day one – optimized for data flow, model retraining, monitoring, and fault tolerance.

This ensures your ML product is not only functional – it’s reliable, maintainable, and built to evolve.

You get:

cloud-native or on-prem architecture diagrams;

component responsibilities and communication logic;

ML model deployment strategy (API-based, batch, streaming, edge);

data governance and observability best practices.

Pilot projects / MVP

We build and deliver ML-powered MVPs in 12-14 weeks. The result of these services is a working prototype connected to real data, tested with real users, and built with production-readiness in mind.

Ideal for:

validating ideas before a full-scale rollout;

getting stakeholder buy-in with tangible results;

testing data quality, model viability, and integration flows.

Integration into business systems

We integrate ML models into your existing software, tools, and workflows – CRMs, ERPs, data lakes, analytics platforms, mobile apps, and more.

You get:

stable API connections or embedded ML modules;

real-time or batch predictions triggered by your systems;

clean data flow in both directions;

documentation and support for your internal teams.

Long-term support & scaling

You get a partner who stays with you post-launch – keeping your AI sharp, stable, and scalable.

We handle:

monitoring model accuracy and drift;

retraining and A/B testing strategies;

infrastructure upgrades and horizontal scaling;

SLA-based support and incident resolution.

Transform Your Business with ML

Go beyond off-the-shelf solutions. We build custom machine learning models that solve your unique challenges and drive real results.

Get free consultation

Industries we serve with our custom ML development services

Finance & fintech

Financial systems run on trust, data, and risk management. ML uses explainable, compliant algorithms to strengthen fraud prevention, credit evaluation, and Client profiling. We design models that help fintechs move fast without breaking the rules, and help banks modernize without losing control.

credit risk evaluation;
fraud detection & anti-money laundering;
customer segmentation & scoring;
algorithmic trading support;
chatbots for banking.

Financial analysis report with key metrics

Healthcare & life sciences

Every decision in healthcare carries weight. ML helps make those decisions earlier, with more context and less guesswork. We support clinical teams with image analysis, patient risk prediction, and structured insight from messy records – without compromising data security or regulatory compliance.

medical image analysis;
predictive diagnostics;
patient outcome prediction;
hospital resource planning;
NLP for medical records.

A healthcare worker holding a prescription bottle

Retail & eCommerce

In retail, small margins reward precise decisions. ML improves targeting, pricing, and inventory moves – based on behavior, not assumptions. We help retailers increase conversions, reduce stockouts, and adapt fast to market shifts without relying on outdated rules.

product recommendations;
dynamic pricing;
inventory forecasting;
customer behavior prediction;
store layout optimization.

Logistics & supply chain

The cost of being late, wrong, or underprepared in logistics is steep. ML improves planning accuracy, delivery timing, and inventory movement. Our systems detect delays before they cascade, forecast demand with less overstock, and fine-tune operations at scale.

demand forecasting;
route optimization;
warehouse automation;
anomaly detection in logistics flows.

Manufacturing & industrial

Unplanned downtime, defects, and bottlenecks kill efficiency. ML helps production teams maintain uptime, catch quality issues early, and make processes more adaptive. Our models are trained on real operational data – not lab simulations – so they hold up in live environments.

predictive maintenance;
quality inspection;
equipment failure detection;
process optimization;
robotics and machine coordination.

Real estate & PropTech

Markets shift fast. ML helps real estate platforms and investors respond with accurate pricing, risk scoring, and demand models. We work with data from listings, transactions, foot traffic, and demographics to create sharper forecasts and smarter decisions.

property price prediction;
investment risk analysis;
demand forecasting.

Business impact of Machine Learning

Done right, Machine Learning doesn’t just run in notebooks. It works in your systems, supports your team, and improves your bottom line. That’s what we focus on: practical, measurable impact.

Here’s what machine learning should deliver:

Save 1,000+ hours by automating repetitive tasks across operations, support, and analytics.
Handle over 60% of Tier-1 support requests with ML-powered virtual assistants.
Cut churn by 20% using models that predict when and why customers are about to leave.
Boost conversions by up to 20% with personalized offers, recommendations, and content.
Reduce fraud losses by 30–40% through real-time anomaly detection and behavioral risk scoring.
Increase forecasting accuracy for sales, demand, and risk, helping teams act early, not late.
Deploy models 3× faster with structured MLOps workflows.

From Idea to Intelligent Application

Have a great idea for an AI / ML product? Our experts will guide you through the entire process, from concept to deployment.

Book discovery call

7 Cs of a trustworthy ML development services partner

Machine Learning development projects aren’t just about code. They’re about trust, clarity, and shared goals. Here’s how we work – and why Clients stay with us after the first release.

Clarity

We don’t complicate things. You’ll always know what we’re building, why it matters, and how it works – in clear, honest language.

Context

We don’t solve abstract problems. We dive into your business goals first – then shape the solution around real needs, not tech for tech’s sake.

Competence

We use proven tools, best practices, and architectures that scale. We know where ML shines – and where it doesn’t. That means fewer risks and better decisions.

Curiosity

We explore, test, and innovate – but only when it’s justified by business value. No R&D theater. Just smart experiments, grounded in purpose.

Caution

We think about risks. Every project considers data security, model bias, ethics, and regulatory impact – from day one, not post-factum.

Collaboration

We don’t work for you – we work with you. Fast feedback loops, shared ownership, and open communication are built into our process.

Consistency

We do what we say. Timelines, deliverables, quality, post-launch support – no dropped balls, no vanishing teams.

Our tech expertise that powers ML solutions

We work at the intersection of data, software, and machine learning, building solutions that operate reliably in real-world conditions. When providing AI and Machine Learning development services, our team combines research-level understanding with practical development skills, allowing us to design systems that are not only smart, but stable, explainable, and scalable.

Here’s a breakdown of our core engineering capabilities.

Machine learning algorithms

In our custom machine learning development services, we implement a wide range of ML models, selected based on task type, data constraints, interpretability requirements, and infrastructure. This includes:

supervised learning models (logistic regression, decision trees, XGBoost, SVMs) for classification, scoring, and forecasting;
unsupervised models (clustering, anomaly detection) for pattern discovery and segmentation;
time series models for demand, sales, and risk forecasting (ARIMA, Prophet, ML ensembles);
hybrid pipelines combining rules, heuristics, and ML models to handle edge cases and fallback logic.

All models are selected based on empirical benchmarks – not buzz – and validated on your business-specific KPIs.

Deep learning

When task complexity or unstructured data demands more, we build and train deep neural networks using:

CNNs for visual input (quality inspection, OCR, medical imaging, object tracking);
RNNs, LSTMs, and Transformers for time series and NLP (document analysis, event prediction, chat models);
autoencoders for noise reduction, dimensionality reduction, and anomaly detection;
custom architectures for multimodal inputs or hybrid systems (e.g., text + tabular + visual).

We support distributed training, hardware acceleration (GPU/TPU), and model versioning, ensuring DL models are production-ready – not stuck in experimentation.

AutoML

We use AutoML tools to reduce time-to-first-model in rapid prototyping and internal systems. Tools include:

Google Cloud AutoML / Vertex AI;
AWS SageMaker Autopilot;
H2O.ai;
MLJAR and internal AutoML wrappers for light tasks.

Unlike black-box automation, we audit every model generated, tune key parameters manually when needed, and benchmark against custom-built alternatives. AutoML is a tool – not a shortcut.

Big data processing

High-volume data needs systems that can scale and recover. We design and implement:

distributed data pipelines using Apache Spark, Hadoop, and Airflow;
real-time data streaming via Apache Kafka and Flink;
cloud-native event processing on AWS (Kinesis), GCP (Pub/Sub), and Azure Event Hub;
ETL and ELT pipelines capable of processing terabytes/day – for training and real-time inference.

We optimize data flow to reduce lag, memory footprint, and runtime – so your models learn from the freshest, richest signals available.

Services	Tools samples
ML & AI frameworks/libraries	TensorFlow, PyTorch, Scikit-learn, Keras, XGBoost, LightGBM, OpenCV, Hugging Face Transformers, spaCy, NLTK, FastText, LangChain, MLlib (Apache Spark).
Programming languages	Python, R, Java, C++, JavaScript / TypeScript (for frontend/backend integration), Go, Scala.
Data & pipeline tools	Apache Airflow, Apache Kafka, Apache Spark, Pandas, NumPy, Dask, dbt (for data transformation).
Cloud platforms & infrastructure	AWS (SageMaker, EC2, S3, Lambda), Microsoft Azure (Machine Learning, Blob Storage), Google Cloud Platform (Vertex AI, BigQuery, AutoML), IBM Cloud, DigitalOcean (for small-scale deployments), Snowflake.
DevOps & MLOps	Docker, Kubernetes, MLflow, DVC, Kubeflow, Jenkins, GitHub Actions, Terraform, Prometheus + Grafana (for monitoring).
Databases & storages	PostgreSQL, MySQL, MongoDB, Cassandra, Redis, ElasticSearch, Amazon Redshift, BigQuery, MinIO (S3-compatible object storage).
Visualization & dashboarding	Power BI, Tableau, Looker, Grafana, Streamlit, Dash by Plotly, Superset.

Services

ML & AI frameworks/libraries

Programming languages

Data & pipeline tools

Cloud platforms & infrastructure

DevOps & MLOps

Databases & storages

Visualization & dashboarding

Tools samples

TensorFlow, PyTorch, Scikit-learn, Keras, XGBoost, LightGBM, OpenCV, Hugging Face Transformers, spaCy, NLTK, FastText, LangChain, MLlib (Apache Spark).

Python, R, Java, C++, JavaScript / TypeScript (for frontend/backend integration), Go, Scala.

Apache Airflow, Apache Kafka, Apache Spark, Pandas, NumPy, Dask, dbt (for data transformation).

AWS (SageMaker, EC2, S3, Lambda), Microsoft Azure (Machine Learning, Blob Storage), Google Cloud Platform (Vertex AI, BigQuery, AutoML), IBM Cloud, DigitalOcean (for small-scale deployments), Snowflake.

Docker, Kubernetes, MLflow, DVC, Kubeflow, Jenkins, GitHub Actions, Terraform, Prometheus + Grafana (for monitoring).

PostgreSQL, MySQL, MongoDB, Cassandra, Redis, ElasticSearch, Amazon Redshift, BigQuery, MinIO (S3-compatible object storage).

Power BI, Tableau, Looker, Grafana, Streamlit, Dash by Plotly, Superset.

Solve Complex Challenges with ML

Tackle your toughest challenges with the power of custom machine learning. We develop robust, scalable solutions for even the most complex business problems.

Get a quote

Projects we recently released

The system has produced a significant competitive advantage in the industry thanks to SumatoSoft’s well-thought opinions.

They shouldered the burden of constantly updating a project management tool with a high level of detail and were committed to producing the best possible solution.

Alexander McCaig

Co-Founder & CEO, Tartle

I was impressed by SumatoSoft’s prices, especially for the project I wanted to do and in comparison to the quotes I received from a lot of other companies.

Also, their communication skills were great; it never felt like a long-distance project. It felt like SumatoSoft was working next door because their project manager was always keeping me updated. Initially.

Benjamin Dorsinvil

Founder, SellBig

We tried another company that one of our partners had used but they didn’t work out. I feel that SumatoSoft does a better investigation of what we’re asking for. They tell us how they plan to do a task and ask if that works for us. We chose them because their method worked with us.

Damian Gevertz

Founder & CEO, Widgety

SumatoSoft is great in every regard including costs, professionalism, transparency, and willingness to guide. I think they were great advisors early on when we weren’t ready with a fully fleshed idea that could go to market.

They know the business and startup scene as well globally.

David Logan

Founder, Umergence

SumatoSoft is the firm to work with if you want to keep up to high standards. The professional workflows they stick to result in exceptional quality.

Important, they help you think with the business logic of your application and they don’t blindly follow what you are saying. Which is super important. Overall, great skills, good communication, and happy with the results so far.

Domien Van Eynde

Team Lead, Daiokan.com

They are very sharp and have a high-quality team. I expect quality from people, and they have the kind of team I can work with. They were upfront about everything that needed to be done.

I appreciated that the cost of the project turned out to be smaller than what we expected because they made some very good suggestions. They are very pleasant to work with.

Michael Karbushev

Senior Director of Engineering, Evolv

The Rivalfox had the pleasure to work with SumatoSoft in building out core portions of our product, and the results really couldn’t have been better.

SumatoSoft provided us with engineering expertise, enthusiasm and great people that were focused on creating quality features quickly.

Paul S. Chun

CTO, Rivalfox GmbH

SumatoSoft succeeded in building a more manageable solution that is much easier to maintain.

Yevgeniy Rozenblat

Program Manager, TL Nika

Thanks to SumatoSoft can-do attitude, amazing work ethic and willingness to tackle client’s problems as their own, they’ve become an integral part of our team. We’ve been truly impressed with their professionalism and performance and continue to work with a team on developing new applications.

We are completely satisfied with the results of our cooperation and will be happy to recommend SumatoSoft as a reliable and competent partner for development of web-based solutions

Yury Haverman

Founder, BoxForward

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ML solutions that solve real business challenges

We deliver practical machine learning solutions that drive measurable results – higher Client retention, faster decisions, lower costs, and smarter operations. Below are the areas where we use the power of Machine Learning and our decade-plus expertise to bring real, visible value across industries.

Customer experience & personalization

Make every Client interaction smarter, faster, and more relevant with our services. We help companies use data to understand users better, predict behavior, and personalize experiences at scale – with systems that learn and adapt continuously.

Recommender systems

Suggest the right product, service, or content based on each user’s behavior, context, and preferences. Used in e-commerce, media, e-learning, and more.

Personalized content/offers

Deliver tailored promotions, emails, and messages that convert better than one-size-fits-all campaigns – powered by user segmentation and predictive models in our Machine Learning products.

AI chatbots & assistants

Automate support and engagement with bots that understand intent, resolve requests, and escalate when needed – trained on your real conversations.

Customer retention prediction

Spot churn risks early. We build models that flag users likely to leave – so your team can act before it happens.

Operational efficiency & automation

We build ML-powered systems that cut manual work, reduce costs, and improve accuracy – without disrupting your workflows. These solutions help teams do more with less by automating decisions, optimizing schedules, and preventing issues before they escalate.

Intelligent process automation

We automate decision-making inside your workflows – not just simple rules, but logic that adapts to data. From approvals to routing, Machine Learning makes the flow smarter.

RPA bots

ML-enhanced RPA bots that don’t just click and copy – they recognize patterns, extract insights, and adapt to changes in structure or behavior.

Staff scheduling

Predict demand, allocate shifts, and reduce overstaffing. Our models learn from historical data, seasonality, and constraints to create optimal schedules.

Fraud detection

Real-time models that catch anomalies, flag suspicious transactions, and reduce false positives – constantly updated to match evolving fraud tactics.

Workflow integration

We embed ML directly into your tools and systems – no need to switch platforms. Models trigger actions where the work already happens.

Strategic insights & decision support

We build ML solutions that don’t just describe the past – they forecast the future. Our models turn raw data into clear recommendations, helping teams make faster, smarter, and more confident decisions at every level.

Predictive analytics & forecasting

Anticipate what’s next – from Client behavior to operational trends. We use historical data to build models and develop Machine Learning systems that predict outcomes with precision and speed.

Business intelligence (BI)

Go beyond dashboards. We integrate ML into your BI stack to surface hidden patterns, automate insights, and deliver proactive alerts – not just reports.

Pricing optimization

Dynamic pricing models that adapt to demand, competitor moves, and Client segments – driving higher margins without manual guesswork.

Sales & demand forecasting

Predict what will sell, when, and how much. Our models learn from seasonality, promotions, and external factors to support accurate planning and inventory control.

Risk prediction

Spot risks early – from credit defaults to system failures. We help teams act before problems grow, with models trained on the signals that precede loss.

Foundational AI components

Our services include the development of foundational AI components, which power more intelligent products and internal tools. These components are the engines behind smarter automation, better search, richer analytics, and seamless human-machine interaction.

Computer vision solutions

Analyze images and video at scale. We build systems for object detection, quality control, facial recognition, scene understanding, and more – with real-time performance and pixel-level accuracy.

NLP & document processing

Extract meaning from text, emails, PDFs, forms, and chat logs. From classification to entity recognition and semantic search – we turn messy documents into structured, searchable data.

Speech-to-text transcription

Convert voice into clean, timestamped, searchable text. Ideal for call centers, internal recordings, compliance logs, and accessibility features – with support for noisy audio and multiple languages.

AI agents

Systems that act autonomously: trigger actions, retrieve data, assist users, or orchestrate multi-step workflows – powered by language models, planning logic, and API integrations.

Create Custom ML for Your Business

Off-the-shelf AI isn’t enough. We design and build machine learning solutions specifically for your needs, goals, and data.

Get in touch

Our ML development process

Ensuring a successful machine learning software requires a disciplined process. We follow an agile, transparent development process to deliver high-quality Machine Learning solutions efficiently:

Explore & define

We start by understanding your business goals and data. In this phase, our ML engineers and business analysts work with you to identify high-impact use cases and assess the data you have (or need) for those use cases. We conduct discovery workshops to define the project scope, success metrics, and requirements for the ML solution.

By thoroughly exploring your domain and data, we set a strong foundation and realistic plan before development begins.

Model development & training

Once requirements are clear, we move into iterative development. Our data scientists select or build appropriate ML models (from regression and classification models to complex neural networks) and train them using your data.

We experiment with different algorithms and hyperparameters to find the best-performing model. Each model is validated rigorously – we test for accuracy, precision/recall, and other relevant metrics using cross-validation and real-world test cases.

We iterate until we have a model that meets the target performance and reliability for deployment.

Integration & deployment

Our software engineers integrate the ML model into your solution’s architecture after a successful model training and approval process.

We establish secure and scalable deployment environments for model inference through API integration with cloud applications and on-premise system connections. The end result is a fully functional ML-powered application or feature ready for end-users.

Our team takes care of all deployment procedures by performing application refactoring when needed and establishing MLOps best practice CI/CD pipelines and cloud infrastructure setup to ensure a seamless transition from development to production.

Support, monitoring & improvement

Our commitment doesn’t end at launch. We continuously monitor the performance of the ML solution in production – tracking key metrics to ensure the model remains accurate and beneficial over time. If the model’s performance drifts (e.g. due to new data patterns), we proactively update and retrain it.

We also provide ongoing maintenance for the surrounding software (bug fixes, updates) and support your team with user training, documentation, and any adjustments needed. This continuous improvement approach means your machine learning solution keeps getting better and stays aligned with your evolving business needs.

Ongoing communication & agility

The project requires complete transparency and open communication throughout all development stages. The project delivers detailed progress reports throughout each stage while we maintain flexibility to adjust to new findings and changes because ML projects transform based on data discoveries.

Our agile methodology allows you to achieve early benefits through quick proof-of-concept delivery, followed by expansion while maintaining transparency about timeline and outcome expectations.

Benefits of choosing SumatoSoft as ML development company

Build on

Proven workflows;
Dedicated QA specialists

Lessons learnt after 36000 hours of custom software development

Openness to share knowledge and experience with your teams

Go faster with

Agile methodology

100% transparency

Dedicated Technical
PO / PM / BA

Stay in control

Clear and detailed estimates

Sprint / monthly reports,
custom reports

Regular communications: calls, emails, chats, personal meetings

Awards & Recognitions

Let’s start

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1 Share your idea

2 Discuss it with our expert

3 Get an estimation of a project

4 Start the project

If you have any questions, email us [email protected]

Elizabeth Khrushchynskaya

Account Manager

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Frequently asked questions

What are machine learning services?

The services span the entire ML lifecycle by including data preparation and model development and deployment and monitoring and support. These services convert unprocessed data into software applications that predict outcomes or automate choices or detect patterns for particular business needs.

What is machine learning as a service (MLaaS), when should you use it, and what are the best tools?

MLaaS provides cloud-based machine learning infrastructure that enables quick prototyping and supports organizations with limited resources and low-stakes models. The system manages storage functions together with training operations and deployment processes and API management. The top MLaaS tools consist of AWS SageMaker, Google Vertex AI and Azure Machine Learning and IBM Watson.

How much does it cost to develop a machine learning model?

The total cost depends on the project’s scope. A proof of concept may cost $20K–$50K. A production-grade ML system – with data pipelines, UI, and ongoing support – typically ranges from $80K to $250K+. The final cost depends on data quality and use case complexity and integration depth.

What are the 4 types of machine learning?

Supervised learning requires labeled data to function (e.g., spam detection).
Unsupervised learning finds structure in unlabeled data (e.g., segmentation).
Semi-supervised learning works with partially labeled data.
Reinforcement learning learns via trial and error (e.g., robotics, games).

What does a machine learning developer do?

They build systems that learn from data. The process involves data preprocessing and model selection followed by training and evaluation and deployment and monitoring. The developer’s responsibilities include model integration into software products and performance optimization as well as maintaining model accuracy throughout time.

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Consulting & discovery

ML consulting

Strategy building

Use-case validation

Model Engineering & Intelligence Design

Deep learning / neural networks

Generative AI / LLMs

AutoML

Model optimization & tuning

Productization & deployment

End-to-end ML development

AI-driven product architecture

Pilot projects / MVP

Integration into business systems

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Finance & fintech

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Retail & eCommerce

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Business impact of Machine Learning

From Idea to Intelligent Application

7 Cs of a trustworthy ML development services partner

Clarity

Context

Competence

Curiosity

Caution

Collaboration

Consistency

Our tech expertise that powers ML solutions

Machine learning algorithms

Deep learning

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Big data processing

Services

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Projects we recently released

Adaptive health monitoring mobile app for personalized wellness programs

IoT application with sensors for industrial fridge monitoring

Innovative big data trading platform

ML solutions that solve real business challenges

Customer experience & personalization

Operational efficiency & automation

Strategic insights & decision support

Foundational AI components

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Frequently asked questions