Generative AI, machine learning,
and computer vision.
Built into products people actually use.

AI that works,
not just demos

Schedule a consultation
14+  Years
150+  Projects
GenAI · ML · NLP 

Where AI projects
tend to break

Most AI projects don’t fail at the idea stage.
They do later—when real data hits the model,
and nobody has planned for what comes next.

Your data isn’t ready, and no one has told you

Your data isn’t ready, and no one has told you

Different sources, formats, and missing data can affect model output. Data preparation and integration help the model work with the right inputs.

The proof of concept never became a product

The proof of concept never became a product

Real inputs, integrations, traffic, and latency can change AI performance. Testing under expected conditions shows how the feature will behave after release.

It was launched, then it slowly stopped working

It was launched, then it slowly stopped working

New data and usage patterns can affect output over time. Regular checks show when the model, prompts, or data need an update.

AI and ML services
we cover

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Generative AI & LLMs

AI assistants, RAG systems, and content generation connected to company data and workflows

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Conversational AI & NLP

Chatbots, search, and document processing for teams that work with large amounts of text

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Predictive ML & Data

Forecasting, recommendations, and anomaly detection based on business data

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MLOps & Deployment

Pipelines, model deployment, and evaluation to keep AI features running after release

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AI Integration & Consulting

Not sure where AI fits? We help you scope what’s realistic and build it into your product

From proof of concept
to production

You don’t need a finished AI plan. Start with a business task or idea, and we’ll help turn it into a working solution.

01 — Data & Discovery

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We define the task, review the available data and systems, and agree on what the AI feature needs to achieve

02 — Proof of Concept

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An initial build—call it a pilot version—shows how the idea performs with real data before larger development starts

03 — Production Build

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Once value is proven, we develop the idea into a fully-functional AI solution suitable for daily use

04 — Monitor & Improve

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After release, we track how the feature performs and update it as data, usage, and requirements change

Vilmate
Have a use case in mind? Let’s pressure-test it.
Book a call

Platforms
& tools

LLM & GenAI
ML & Data
MLOps & Cloud
GPT-icon
GPT
Claude-icon
Claude
Llama-icon
Llama
LangChain-icon
LangChain
Hugging Face-icon
Hugging Face
Pinecone-icon
Pinecone
PyTorch-icon
PyTorch
TensorFlow-icon
TensorFlow
scikit-learn-icon
scikit-learn
pandas-icon
pandas
XGBoost-icon
XGBoost
NumPy-icon
NumPy
Docker-icon
Docker
Kubernetes-icon
Kubernetes
MLflow-icon
MLflow
AWS SageMaker-icon
AWS SageMaker
Azure ML-icon
Azure ML
Vertex AI-icon
Vertex AI

What we build
with AI

AI assistants & chatbots
AI assistants & chatbots

LLM-powered assistants that answer customer questions, help users find information, and understand follow-up questions

Document intelligence
Document intelligence

Tools that extract, classify, and summarize contracts, invoices, and forms, turning piles of paperwork into structured data

Recommendation engines
Recommendation engines

Systems that personalize product, content, and search results for users based on how their preferences and behaviors

Forecasting & prediction
Forecasting & prediction

Models that understand patterns and predict demand, churn, or risk coming, so that decisions get made on data instead of gut feel

Enterprise search
Enterprise search

Tools that understand user questions in plain language and return the right answer, extracting it from internal documents

Why Vilmate

Your Team, Not a Rotation
Speed Without Shortcuts
ISO-Certified Quality
Nearshore Advantage
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You work with the same developers start to finish. The team can handle AI features, application logic, integrations, cloud setup, and data flows.
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We move fast, but not at the expense of code quality or architecture. “Fast” and “high-quality” aren’t mutually exclusive.
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Your data is handled with controlled access and security practices: structured code reviews, clear documentation, and predictable delivery.
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Based in Eastern Europe, we overlap with US and EU working hours. Get real-time collaboration without the timezone headache.

Selected work

A few AI projects that made it past the demo and into production

See all cases
Awarebee
#ai
#saas
#tech
#wordpress
USA

Awarebee

Monitor website changes with AI
We worked on Awarebee’s SaaS platform, covering UX/UI, dashboards, subscriptions, branding, and a marketing website built around its existing monitoring functionality.
View case study
AI-Powered Video Ad Platform
#adtech
#ai
#nest.js
#node.js
#postgresql
#react
USA

AI-Powered Video Ad Platform

A web platform for AI-powered video post-production
The client already had the AI technology aimed to simplify the work with 3D elements. Our team built the web architecture, core workflows, and user experience that brought it into everyday use.
View case study
Voicea
#ai
#saas
#android
#ios
#ruby
#vue.js
USA

Voicea

AI-powered voice collaboration platform for more productive meetings
We joined Voicea’s engineering team to build native iOS and Android apps alongside frontend, backend, and machine learning development.
View case study

Questions clients ask
before they start

How much does an AI/ML project cost?
It depends on the scope: data readiness, model complexity, integrations, and how far you want to take it. A focused proof of concept is a much smaller commitment than a full production system. We scope it properly on a discovery call before quoting anything, so you see clearly what you’re signing up for.
How long before we see something working?
A proof of concept usually takes a few weeks and gives you a working version to evaluate before moving further. The timeline depends on the features, integrations, and work required for the project.
What if our data isn’t ready?
That’s the most common starting point, honestly. Messy, scattered, or unlabeled data is normal at the start. A part of our job is getting it into shape before any modelling happens. If the data genuinely can’t support what you're after, we’ll tell you early what and how much of data you need to drive value to prevent from burning budget finding out later.
Do we need a huge dataset to start?
Not always. Some problems need a lot of labeled data; others can lean on pre-trained models and a smaller, well-prepared set. It depends on what you're solving. We’ll assess what you have and tell you whether it’s enough, or what it’d take to get there.
What happens after the model goes live?
Models drift as real-world data shifts, so launch isn’t the finish line. We set up monitoring and retraining to keep accuracy from sliding over time. Some clients want us running that long-term; others prefer we hand it off with a setup their own team can manage.

Let’s build AI
that drives value

Whether you’ve got a use case in mind or just a problem worth solving, let’s pressure-test it and figure out together what’s real
Prefer a call?
Pick a time that works for you.
Book a call




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