Custom ML Development

Custom ML Model Development & Deployment

Neuralex Labs provides custom ML model development for businesses worldwide — fine-tuned large language models on proprietary data, predictive analytics engines, computer vision systems, and NLP pipelines. We handle the full lifecycle: data preparation, training, evaluation, deployment, and monitoring, so you get a production model, not a research notebook.

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What we build

  • Fine-tuned & private LLMs

    Language models adapted to your domain data and terminology — deployable in your cloud for privacy-sensitive workloads.

  • Predictive analytics

    Forecasting models for demand, churn, revenue, and risk — trained on your historical data with measurable accuracy targets.

  • Computer vision

    Detection, classification, and OCR systems for documents, products, and quality-control imagery.

  • NLP pipelines

    Classification, extraction, summarization, and search over your documents, tickets, and communications.

  • MLOps included

    Versioning, evaluation harnesses, drift monitoring, and retraining pipelines — models stay accurate after launch.

Our ML development process

Custom ML development at Neuralex Labs follows five steps: a feasibility assessment of your data and target metric, data preparation, model training with rigorous evaluation, production deployment behind an API, and ongoing monitoring with retraining. You approve accuracy benchmarks before deployment — no black-box hand-offs.

  1. Feasibility & data audit

    We assess whether your data supports the target prediction and define the success metric before any commitment.

  2. Data preparation

    Cleaning, labeling strategy, and feature engineering — the unglamorous 60% of ML work done properly.

  3. Training & evaluation

    Model training with held-out evaluation sets; you see precision, recall, and error analysis against the agreed benchmark.

  4. Production deployment

    The model ships behind a documented API in our cloud, your VPC, or on-premises — with load and latency testing.

  5. Monitoring & retraining

    Drift detection and scheduled retraining keep accuracy stable as your data changes.

What you get

  • A production ML model meeting an agreed accuracy benchmark
  • A documented inference API integrated with your systems
  • Evaluation report: metrics, error analysis, and known limitations
  • Deployment in our cloud, your VPC, or on-premises
  • Monitoring, drift detection, and retraining pipeline

Frequently asked questions

How much does custom ML model development cost?

Custom ML development typically starts in the low five figures (USD) for a scoped single-model project and scales with data complexity and integration depth. Neuralex Labs quotes a fixed price after a feasibility assessment, so you know cost and target accuracy before committing.

Do we need big data to build a custom ML model?

No — modern techniques work with modest datasets. Fine-tuning LLMs can succeed with hundreds to a few thousand quality examples, and classic predictive models often perform well on a few years of business records. The feasibility audit tells you honestly whether your data is sufficient.

Should we fine-tune an LLM or use RAG?

Use RAG (retrieval-augmented generation) when the model needs current, changing knowledge from your documents; fine-tune when it must learn tone, format, or domain-specific reasoning. Many production systems combine both. We recommend the cheaper sufficient option in the feasibility phase — not the fanciest one.

Can the model run inside our own infrastructure?

Yes. Neuralex Labs deploys models in your VPC (AWS, GCP, Azure) or fully on-premises for regulated and privacy-sensitive workloads. Default deployment is our managed cloud with scoped access and written data agreements.

How long does a custom ML project take?

A scoped custom model typically takes 4–10 weeks from data audit to production deployment, depending on data readiness and integration complexity. Fine-tuning projects on prepared data land at the shorter end; computer-vision systems with labeling needs take longer.

What happens after deployment — who maintains the model?

We do. Every engagement includes monitoring, drift detection, and retraining under the monthly subscription. Model accuracy degrades as real-world data shifts; maintenance is part of the service, not a separate contract.

Related resources

Tell us the role. We build & deploy.

A 30-minute discovery call is enough to scope your first deployment — fixed quote, flat monthly pricing, done-for-you delivery.

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