Machine Learning Development Services

We build machine learning models that ship inside real products — not proofs of concept that never leave a notebook. As a machine learning development company, Purrweb takes your idea from a data audit and feasibility check through model training, integration, and post-launch monitoring, so the ML actually powers a feature your users touch. Quick answer: Machine learning development services cover the end-to-end work of turning data into a working model inside your product — ML consulting and feasibility, data engineering, custom model development and training, deployment, and ongoing MLOps. The goal is a maintained model in production, not a one-off experiment.

Free estimate in 48 hoursFixed quote after feasibilityTensorFlow / PyTorch / Scikit-learn

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Our machine learning development services

ML consulting & feasibility

Before any code, we assess whether machine learning is the right tool for the job, what data you need, and where the risk sits. You get an honest read on scope — including when an off-the-shelf model is the smarter call.

The output is a plain-language recommendation: the problem framed as an ML task, the data you'll need, a realistic accuracy target, and the risks worth knowing before you spend.

Custom model development

Supervised, unsupervised, and deep learning models trained on your data — from predictive analytics and churn or fraud detection to recommendation systems and forecasting.

We choose the model that fits the problem and your data volume, not the most complex one available, and evaluate it against a metric that maps to a business outcome you care about.

Computer vision

Image classification, object detection, and OCR features built into your product — from quality inspection to document parsing and in-app scanning.

For dedicated vision work, see our computer vision development services.

Natural language processing

Text classification, entity extraction, sentiment analysis, and LLM fine-tuning for domain-specific language tasks.

When a general-purpose language model isn't accurate enough on your terminology or documents, we fine-tune or build a focused model that understands your domain.

MLOps & model monitoring

Retraining pipelines, versioning, drift detection, and monitoring so your model keeps performing after launch instead of quietly degrading.

Models decay as real-world data shifts. We put the plumbing in place to catch that early and retrain on schedule, so accuracy doesn't slide without anyone noticing.

AI integration

Already have a model or want to use a third-party one? We wire external and custom models into your product, including the API, data, and infrastructure work around them.

If a ready-made model is the right answer, that's our AI integration services rather than a custom build.

Products we've shipped

We've shipped data-heavy features across 100+ products — dashboards, analytics, real-time pipelines, and integrations that hold up under real usage. See the range of what we've built in our portfolio.

Not sure if your idea needs a custom model?

Get a free ML feasibility assessment — we'll tell you what's realistic, what data you need, and whether machine learning is worth it before you commit a budget.

Get a feasibility assessment

Industries we build machine learning into

B2B SaaS

Predictive scoring, usage analytics, and in-product recommendations — for example, flagging accounts likely to churn so customer success can reach them first.

Fintech

Fraud detection, credit and risk models, and transaction anomaly detection that learn the patterns specific to your users rather than a generic rule set.

Healthtech

Medical imaging support and predictive monitoring, built HIPAA-aware with the data handling and validation that regulated products need.

E-commerce & retail

Recommendation engines tuned to your catalogue, demand forecasting, and personalization that responds to real browsing and purchase behaviour.

Logistics

Route and demand forecasting, inventory optimization, and ETA prediction that turn historical operational data into decisions people can act on.

Our machine learning tech stack

Frameworks: TensorFlow, PyTorch, Scikit-learn, Keras

Cloud ML platforms: AWS SageMaker, Google Vertex AI, Azure ML

LLMs & foundation models: OpenAI, Anthropic, Hugging Face (via our AI integration services)

Data & pipelines: Apache Spark, Airflow, vector databases

MLOps: MLflow, Docker, Kubernetes

We pick the stack around your product and infrastructure, not the other way around — the model has to run and be maintainable inside the system you already have.

Getting started is as simple as 1-2-3

Tell us the problem

Share the outcome you want and the data you have.

Get a feasibility read

We assess data readiness, approach, and a realistic scope.

We build and ship it

Model development, integration into your product, and monitoring after launch.

Our machine learning development process

Discovery & data audit

We map the business goal to an ML problem and audit your data — volume, quality, labelling, and gaps. If the data isn't ready, we scope the data engineering first.

Feasibility & PoC

A time-boxed proof of concept validates whether a model can hit useful accuracy on your data before you invest in a full build. This is where we de-risk — many ideas are reshaped or stopped here, and that's the point.

Model development & training

Feature engineering, model selection, training, and evaluation against metrics that map to your business outcome — not just accuracy for its own sake.

Integration & deployment

We ship the model into your product — API, mobile, or web — as a feature users actually reach. This is where our full-stack product engineering matters: the model and the product are built by one team.

Monitoring, retraining & MLOps

Post-launch, we monitor for drift, retrain on fresh data, and version models so performance holds over time.

“The biggest misconception is that a working PoC means you’re most of the way there. A PoC exists to answer one question — is this feasible on your data — and the real engineering starts once the answer is yes. We’re deliberate about that boundary so clients don’t pay to productionize something that was never going to hold up.”

— Sergey Ponomarev, CTO, Purrweb

Custom ML model or an off-the-shelf AI API?

Not every problem needs a custom model. A ready-made API (OpenAI, a cloud vision endpoint) is faster and cheaper when your task is generic — general text generation, standard image labelling, transcription.

You need a custom machine learning model when the task is specific to your data and your edge cases: fraud patterns unique to your users, a recommendation system tuned to your catalogue, predictions that depend on your historical data. Off-the-shelf APIs can't learn your domain, and you don't control accuracy, cost at scale, or data privacy.

We'll help you make that call in the feasibility phase — and if integrating an existing model is the right answer, that's our AI integration services, not a custom build you don't need.

Machine learning is one specialism within our wider AI development services.

How much does machine learning development cost?

Data readiness

Clean, labelled, sufficient data is the biggest lever. Poor or missing data means data engineering before any modelling.

Model complexity

A logistic-regression churn model and a deep-learning vision system are different orders of effort.

Integration depth

A standalone prediction endpoint is cheaper than a model woven into an existing product with real-time constraints.

Compliance

HIPAA, GDPR, or financial-data handling add validation and security work.

There's no honest flat price for ML — the number moves with your data and scope, which is why most agencies just say “contact us.” We give a fixed quote after the feasibility assessment — once we've seen the data, the number is real instead of a rate-card guess made before anyone has looked at your project.

Get a fixed quote

Why teams pick Purrweb for machine learning

We ship the product, not just the model

Most ML shops hand you a model and a notebook. We're a full-stack product team — mobile, web, and backend — so the model lands as a working feature, not a file your engineers still have to integrate.

That means someone owns the whole path from data to the screen a user sees, and the model is built against the constraints of a real product from day one.

We de-risk before you spend

The feasibility phase exists to kill or reshape bad ideas early. We'd rather tell you ML isn't worth it than bill you for a model that won't ship.

You find out whether the idea is viable on a small budget, before committing to a full build — which is where most of the cost and most of the risk actually live.

One team, one accountability line

The people who train the model and the people who build the product are the same team. No handoff gaps between the AI vendor and the app team.

When something needs to change after launch — a retrain, a new feature, a fix — you talk to the people who built it, not a chain of vendors pointing at each other.

Some facts about Purrweb

550+

Project released

Clients from all around the world

Working with customers from USA, UAE, Japan, and Germany

10 years

Of making apps for startups and businesses

>50К

Positive reviews collected by our customers' apps in the app stores

>7 years

Longest partnership with the client

200+

People on the team

Machine learning development FAQ

What are machine learning development services?

They're the end-to-end services for building a machine learning model into a product: consulting and feasibility, data engineering, custom model development and training, deployment, and ongoing MLOps. The deliverable is a maintained model running in production, not a one-off experiment.

How is ML development different from AI integration?

ML development builds a custom model trained on your data. AI integration wires an existing model — yours or a third party's like OpenAI — into your product. If your problem is generic, integration is faster; if it's specific to your data, you need a custom model.

How long does it take to build a production-ready ML model?

A feasibility PoC is usually a few weeks. A production-ready model — validated, integrated, and monitored — typically takes a few months, depending on data readiness and integration depth. The honest gap most teams underestimate is PoC-to-production: a promising demo is not a maintained model.

What data do we need before starting an ML project?

Enough relevant, reasonably clean, and (for supervised learning) labelled data tied to the outcome you want to predict. In the discovery phase we audit exactly this — and if the data isn't ready, we scope the data engineering first rather than pretend a model can work without it.

How much does custom ML development cost?

It depends on data readiness, model complexity, integration depth, and compliance. We don't publish a flat rate because it would be a guess; we give a fixed quote after a feasibility assessment.

Can you integrate ML into an existing product instead of building from scratch?

Yes. We integrate custom and third-party models into existing web and mobile products, including real-time constraints and existing infrastructure. That's a core part of how we work — the model has to live inside the product you already have.

What industries benefit most from custom ML solutions?

Fintech (fraud, risk), B2B SaaS (scoring, recommendations), e-commerce (personalization, forecasting), healthtech (imaging, monitoring), and logistics (forecasting, routing) — anywhere decisions depend on patterns in your own historical data.

Do you offer MLOps and post-launch model monitoring?

Yes. We set up retraining pipelines, drift detection, model versioning, and monitoring so the model keeps performing after launch instead of degrading silently.