Machine Learning Services
Production-Grade Machine Learning Systems Built for Regulated Industries
Your data is already generating signals. Equipment leaves traces hours before it fails. Customers behave differently right before they churn. Invoices, claims forms, and technical documents contain structured data that your team is still extracting by hand.
Our Machine Learning services turn these patterns into clear decisions, and decisions into measurable results.
- What we do not do: Run a six-month research project, hand over a Jupyter notebook, and call it a delivery.
- What we do: We build ML systems that run in production, on your real data, within your infrastructure constraints and industry regulations.
STX Next’s impact in numbers
less unplanned downtime
Built failure prediction models for a global Chemical Manufacturing Enterprise's furnaces, processing hundreds of TB of IoT data.
better search quality and higher conversion
Replaced keyword search with semantic models trained on user interaction patterns for Podimo’s 11M+ audio catalog.
down from 4 hours
Replaced a manual QA process with computer vision pipelines running font, logo, and object detection at scale for Wunderman Thompson.
Our services
We structured our engagement models so you can start with a business question and scale only what proves its value. Every fixed-price phase delivers a real, working tool and a clear decision – not a slide deck. All code goes straight to your repository so you don’t have to worry about vendor lock-in.
1. Quick Prediction Sprint
You have historical data and a specific question: can we predict churn, equipment failure, demand, fraud, or price? Your team has been debating it for months. You want an answer before committing to a full budget.
A 3-week, fixed-price PoC using classic ML. We take your data, build and compare 2 to 3 model types against a baseline, and run SHAP and LIME explainability to show exactly what drives the predictions. You get an experimental notebook and a clear go/no-go recommendation with specific failure cases identified. Covers classification, regression, and time-series forecasting.
Python with SOTA ML tools: PyTorch, SKLearn, xAI with SHAP. Visualisation with plotly, streamlit dashboard, Jupyter study.
Fixed price, delivery in 3 weeks, and a clear answer backed by objective measurement.
2. Predictive Maintenance
The most expensive sound is the silence of an unplanned shutdown. Missed production targets, overtime, emergency procurement, and unhappy customers. Planned maintenance is easy to manage, but you need to predict failure accurately enough to plan around it.
We build failure prediction models on your IoT and time-series data. We use physics-derived features alongside raw sensor readings because context matters. We deploy an interface that gives engineers two clear views: a high-probability maintenance window and a countdown-to-failure for each asset, leaving room to tune schedules and avoid overlaps.
Azure ML, PyTorch, SHAP.
20% reduction in unplanned downtime in production at a global chemical manufacturing enterprise.
3. Computer Vision
Human intelligence is too valuable to waste on mechanical data extraction and pixel checking. This repetitive work doesn't scale and leads to human error.
We build end-to-end computer vision pipelines for your specific task (object detection, logo identification, image classification, or document OCR). We train on your data, not generic datasets, and deploy as auto-scaling Kubernetes microservices.
YOLO, ResNet50, DenseNet121, PyTorch, OpenCV, PySpark, Kubernetes.
88.19% F1-Score and 93.2% mAP on logo detection for Sponsoring Insight. Campaign asset analysis cut from 4 hours to 15 minutes for Wunderman Thompson.
4. Recommendations & Intelligent Search
If your search engine matches literal words instead of actual intent, you are just training your users to browse the exit. That departure happens even faster when your recommendation system relies on rigid, static rules instead of adapting to live user behavior.
We replace keyword search with semantic vector search using dense embeddings. Adding learning-to-rank models trained on real user signals (clicks, listens, purchases) allows for reranking results in real time and layering a conversational search interface on top.
Elasticsearch, BigTable, BigQuery, FastAPI, Kubernetes, Pub/Sub.
27% better search quality and a 3.5% platform conversion lift for Podimo.
5. Predictive Analytics & Forecasting
Decisions on inventory, pricing, or credit are still based on last year's averages because your data isn't connected to a forward-looking model.
We build supervised ML models for classification (churn, fraud), regression, and time-series forecasting (demand, price, energy). You get clean data preprocessing, feature engineering, SHAP explainability outputs, full documentation, and a monitoring plan.
Python, Darts or Nixtla, Databricks, MLFlow.
Live models for a chemical manufacturing enterprise (daily price forecasting), Bgenerous (credit scoring), and Pricenow (demand forecasting).
6. MLOps & Platform Engineering
Models do not stay smart on their own. If engineering cannot integrate what data science ships to staging, your project stalls. Deploying without drift monitoring means shifting input data will quietly erode your investment.
We design and build ML pipelines that cover the full lifecycle: data ingestion, feature engineering, model training, serving, drift monitoring, and automated retraining triggers. Includes experiment tracking, rollback mechanisms, and CI/CD built for your stack.
MLflow, Databricks, Snowflake, Airflow, AWS SageMaker, GCP Vertex AI, Docker, Kubernetes.
Live pipelines for EssenceMediacom, Pricenow, and a chemical manufacturing enterprise.
Competence Mapping
The difference between a model that works and one that delivers business value comes down to what happens around it: the pipeline keeping it fed with clean data, the monitoring catching drift before it costs you, the explainability layer making the output trusted enough to act on. Across our capability areas, we’ve shipped all of it.
Expertise Built On +50 AI/ML Projects
Our teams help global corporations adopt AI solutions responsibly, securely, and cost-effectively. How do we do it? Let our work speak for itself.
The search engine that learned how users think
Podimo is an audio platform. Their original search system matched literal keywords. Users who searched for "true crime podcast" found exactly that phrase, but missed relevant shows titled under "murder mysteries" or "unsolved cases." It forced users to browse instead of search, which hurts engagement.
- The Solution: We built a semantic vector store using Elasticsearch and BigTable, generated and indexed dense audio embeddings for 11M+ records in BigQuery, and enabled real-time indexing via Pub/Sub. On top of that, we added a learning-to-rank model trained on live user interaction signals (clicks, listens, returns) to rerank results in real time.
- The ROI: A 27% improvement in search quality, a 3.5% lift in platform conversion, and a 1.3% increase in meaningful listens.
Unplanned downtime reduced by 20% with Predictive Maintenance
A major chemical manufacturing enterprise came to us with a single question: can we predict when our Olefin furnaces are going to fail? Two years later, that relationship spans four production systems:
- Predictive Maintenance: Because raw telemetry lacks thermodynamic context, we built models using physics-based features alongside sensor readings. Running on Azure ML, PyTorch, Neo4j, and SHAP, the system delivered a 20% reduction in unplanned downtime across billions of records from two factories.
- Scheduling Assistant: We built a tool for production planners that shows a high-probability maintenance window and a countdown-to-failure for each furnace, allowing them to prevent overlapping downtime during critical production runs.
- Document Automation: We automated the extraction and reconciliation of supplier delivery PDFs against internal order data using FastAPI and Pandas, removing a manual step that was causing entry errors.
- Energy Trading Analytics: Built an engine to predict day-ahead and real-time electricity prices in ERCOT using a Temporal Fusion Transformer model on Databricks and Spark, complete with automated daily inference pipelines.
Replacing a four-hour manual review with a 15-minute pipeline
Wunderman Thompson’s Brand Guardian platform checks marketing assets for brand consistency, compliance, and creative standards before they go live. At their scale, a human review queue couldn't keep up with the volume, and inconsistencies between reviewers created compliance risks.
- The Solution: We rebuilt the platform on a Kubernetes-based ML microservices architecture with auto-scaling compute and Celery workers for task distribution. Computer vision pipelines run YOLO, ResNet50, and DenseNet121 for logo, font, and object detection. A messaging system manages task queuing so the pipeline handles massive volume spikes smoothly.
- The ROI: Cut manual review from 4 hours per asset batch to 15 minutes, allowing the system to handle 100% volume growth without adding headcount.
Why Regulated Businesses Work With Us
Clear Pricing Ladder
Fixed prices start at the PoC phase and scale up to enterprise builds. You can start small, see real value, and deploy your first production features in 2 to 4 weeks.
Real Metrics, Not Estimates
Our code delivers: 20% less unplanned downtime, 27% better search quality, content QA cut from 4 hours to 15 minutes, and 88% logo detection accuracy.
Deep Sector Experience
More than 20 ML/AI projects backed by in-house domain experts who understand process physics, engineering workflows, and operations beyond the raw code.
Data Sovereignty
Three flexible deployment modes to protect your data: Full On-Premise (data never leaves your servers), Hybrid (only anonymized text leaves the perimeter), or Fully Managed Cloud.
Agents Shipped, Not Pitched
Production-grade agent systems, including IT operations tools that correlate real-time logs with open tickets, automated training script generators, and secure, self-hosted workspaces.
By the numbers
Let's talk
Schedule a chat with our AI Director and one of our senior engineers to discuss your AI development needs.
FAQ
How do we know whether ML will actually work on our data?
That is what our 3-week Quick Prediction Sprint is for. On your real data, we build and compare models against a baseline, run explainability, and give you an objective verdict with named failure cases. You get a working notebook and a board-ready recommendation deck. No blind six-month investments.
How long does a full ML project take?
A predictive maintenance PoC runs 4 to 6 weeks. A full production system integrated with your MLOps infrastructure typically takes 3 to 12 months, depending on data readiness, integration scope, and regulatory requirements. We design the path so each stage produces a working tool before the next begins.
How much does ML development cost?
Our entry points are fixed and transparent. Full enterprise builds are priced individually.
Our data is messy. Does that rule us out?
No. Most of our projects involve data that was never designed for ML: noisy sensor streams, inconsistent PDFs, unstructured clinical text, and audio embeddings at scale. Data preprocessing and feature engineering are a core part of what we do. If your data genuinely can't support the prediction task, we will tell you honestly at the end of the initial sprint.
Can you work alongside our in-house data science team?
Yes, this is a very common model. We typically bring the MLOps, scalable infrastructure, and production integration engineering, while your internal team focuses on core data models. We adapt to your workflow and tooling.
What about model drift after deployment?
Drift is why ML projects look great at launch but fail six months later. Every production deployment we build includes drift monitoring, automated retraining triggers, and rollback mechanisms. We treat monitoring as a core engineering task from day one.
Can you deploy on our infrastructure, not the cloud?
Yes. For clients in banking, insurance, healthcare, and manufacturing where data cannot leave the perimeter, we offer three modes: full on-premise, managed cloud, or a hybrid mode where data stays inside your infrastructure and only anonymized text is processed by external tools.
