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BigTree108

AI and machine learning development services

AI and machine learning development: predictive models and forecasting, fraud detection and risk scoring, recommendation systems, computer vision, natural language processing and speech recognition, trained on your data and kept in production with MLOps, with dedicated machine learning engineers for your team or a machine learning project delivered end to end.

What we build with AI and machine learning

  • Predictive models and forecasting

    Demand and sales forecasts, churn and lifetime-value predictions and lead scoring from your own history, with LightGBM, XGBoost or CatBoost on tabular data and statsforecast or pretrained time-series models such as Chronos and TimesFM, each measured against a simple baseline before it ships.

  • Fraud detection and risk scoring

    Transaction fraud, account takeover and anti-money-laundering alerts, and credit and payment-risk scores served in real time from a feature store, with thresholds tuned to what a false alarm costs your reviewers and each score explained feature by feature with SHAP.

  • Recommendation and search ranking

    Product, content and next-best-action recommendations with two-tower retrieval and a ranking model on top, search results re-ranked with learning to rank, and every change judged in an A/B test on clicks, conversion or revenue rather than offline scores alone.

  • Computer vision

    Classification, object detection, segmentation and OCR in PyTorch, fine-tuned from pretrained models such as Ultralytics YOLO, vision transformers and Segment Anything on your labelled images, and exported to ONNX Runtime, TensorRT or Core ML when the model has to run on a device.

  • Natural language processing

    Text classification, entity extraction, sentiment and semantic similarity with fine-tuned transformer models from Hugging Face, in English, Ukrainian and other languages, where a small model you host is faster and cheaper per document than a call to a large language model.

  • Speech and audio

    Speech recognition with Whisper or NVIDIA Parakeet adapted to your vocabulary and accents, speaker diarisation with pyannote, call transcription and analytics for contact centres, and text-to-speech, in batch or streaming.

  • Advertising and pricing models

    Click-through and conversion prediction, bid optimisation, uplift models that show which customers a campaign actually changes, customer segmentation and dynamic pricing, each tested in a controlled experiment before it steers spend.

  • Training pipelines and MLOps

    Training, evaluation and registration as a pipeline in MLflow 3, Kubeflow, Azure Machine Learning, SageMaker AI, Gemini Enterprise Agent Platform (the successor to Vertex AI) or Databricks, with data versioned in DVC or Delta tables, so every model in production can be traced to its code, data and parameters.

  • Model serving and monitoring

    Models served behind FastAPI, BentoML, NVIDIA Dynamo-Triton (formerly Triton Inference Server) or KServe, or run inside a .NET or Java application with ONNX Runtime, with drift and prediction quality watched in Evidently and a retraining run when accuracy falls below the line agreed with you.

Hire machine learning engineers

  • Dedicated machine learning engineers

    Machine learning engineers who join your team full time, work in your repositories, tracker and meetings, and report to your lead. You interview them; we carry the Ukrainian contract, payroll, invoicing and leave.

  • AI and machine learning project delivery

    A team that takes the AI and machine learning project from scope to release: estimate, build, tests and deployment, with a technical lead on our side who owns the plan and the quality.

  • AI and machine learning support and take-overs

    An existing AI and machine learning system taken over from another team or kept running: a read-only review and a written list of risks first, then fixes and new features in order of impact.

The dedicated team page explains how specialists join your team, and the outsourcing page covers project delivery, take-overs and how we charge.

Who works on your AI and machine learning project

  • Machine learning engineers

    Models, evaluation, serving and MLOps

  • Data scientists

    Problem framing, experiments and statistics

  • Data engineers

    Training data and feature pipelines

  • Python, .NET and Java engineers

    The services and applications that call the model

  • QA engineers

    Test automation for model-backed features

Trained model or language model

A model trained on your own history suits structured data with a clear target: a number to forecast, a class to predict, an outlier to catch, an item to recommend. It is cheap per prediction, its results can be explained feature by feature, and it improves as labelled data accumulates. A large language model suits text, documents and conversation, where labelled examples are scarce and the output is language. Many products use both, and we recommend one per problem.

Either way, the work starts with a baseline and an evaluation set agreed with you, so a model has to beat something measurable before it ships. Training runs are reproducible from code and data versions, tests run in the pipeline, and training data stays in your cloud account, with personal data pseudonymised before it is used.

Other data and AI services we provide

Data and AI overview

Questions about AI and machine learning development

Which industries do your machine learning engineers work in?

AI and data products, where the model is the product; fintech, for fraud detection, credit scoring and transaction monitoring; e-commerce and retail, for recommendations, search ranking, demand forecasting and pricing; healthcare, for medical image analysis, clinical text and patient risk models; advertising and marketing technology, for click prediction, bidding and audience models; and enterprise SaaS, for churn prediction and machine learning features inside the product.

How much data do we need to start?

It depends on the problem more than on a number of rows. The first step is a short assessment of what you hold: how much history, whether it is labelled and how clean it is. A baseline model built on that data shows whether the signal is there before you invest in a full build.

Do you train custom models or use ready-made AI services?

Both. Where a cloud vision, speech or document service already reaches the accuracy you need, we integrate it, because training your own would cost more for little gain. We train custom models when your data, your accuracy target or your cost per prediction goes beyond what those services offer.

Can you add a model to our existing .NET or Java application?

Yes. The model can run as its own API that your application calls, or be exported to ONNX and run inside the application with ONNX Runtime, which has .NET and Java libraries. The second avoids a network call and a separate service to operate.

How quickly can machine learning engineers start?

When the right machine learning engineer is available, the start is gated only by your interview and the NDA and IP assignment. Otherwise we run a search, which typically produces candidate profiles within two to three weeks, and nobody starts until you have said yes.

How do we hire machine learning engineers through BigTree108?

Tell us the work, the seniority and the hours you need. We propose one or two people with their profiles, you interview them the way you would interview your own hire, and you sign one agreement with BIG TREE 108 LLC and receive one invoice a month.

Who owns the work they produce?

You do. Every specialist has a signed contract with BigTree108 that assigns all work product to the company, and our agreement with you assigns it onward. Code, designs and documents are delivered into your own repositories and tools, not kept where only we can change them.

Have a machine learning problem to solve?

Tell us what you want to predict, detect, recommend or recognise and what data you hold. You get an answer within one business day: a plan for an assessment, candidate profiles, or a recommendation on where to start.

Email hello@bigtree108.com How we respond