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.
less unplanned downtime
Built failure prediction models processing hundreds of TB of IoT data for a global Chemical Manufacturing Enterprise
better search quality + higher conversion
Replaced keyword search with semantic models trained on user interaction patterns in 11M+ audio catalog for Podimo
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 build ML systems that run in production, on your real data, within your infrastructure constraints and industry regulations. Every fixed-price phase delivers a 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, stramlit 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.
STX Next
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.
Enterprise
RAG
PoC to MVP to production: document processing, SSO, semantic search, audit logging
Chatbots &
AI Agents
Agents embedded in products, with MCP and API tools, connected to knowledge bases
Computer
Vision
Object recognition, logo detection, image classification, OCR
Predictive Analytics
& ML
Classification, regression, forecasting, credit scoring, fraud detection
Predictive
Maintenance
Failure forecasting, time-to-failure, anomaly detection on IoT
Recommendations & Intelligent Search
Sequential models, learning-to-rank, semantic search, personalization
NLP & Document Processing
Topic modelling, email classification, information extraction, document workflows
MLOps & Platform Engineering
ML pipelines, model serving, monitoring, CI/CD, experiment tracking
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.
read the full story
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.
read the full story
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.
read the full story
Why Regulated Businesses Work With Us
Complete pricing ladder
Fixed prices start at PoC and scale up to enterprise builds. This modular path allows you to start small, see value, and scale only what works, with first production value 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 in manufacturing & energy
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.
Let's talk
Schedule a chat with our AI Director and one of our senior engineers to discuss your AI development needs.
FAQ
How long does AI implementation take?
It depends on the entry point. An Agentic AI Workspace goes live in 2 weeks. A RAG PoC on your documents runs 2 to 4 weeks. A Quick Prediction Sprint is 3 weeks. An AI Agent MVP is 4 weeks. Full enterprise RAG and custom builds run 10 weeks and up, depending on scope and integrations.
How much does AI development cost?
Entry-point pricing is transparent and fixed. The point of starting with a fixed price is to give you a working artefact and a real decision before you commit to a larger budget. If you're interested in a specific solution, please contact us for a detailed quote!
What is an AI agent, and how is it different from a chatbot?
A chatbot answers questions. An AI agent reads incoming data, decides what to do against defined rules, and takes action across your systems: creating tickets, sending alerts, updating records, with or without a human in the loop. Our AI Agent MVP gives you a working end-to-end agent in 4 weeks, not a slideshow.
What is RAG, and how do you know it will work on our documents?
RAG (Retrieval-Augmented Generation) is a pattern where a language model answers questions grounded in your documents instead of its training data. The honest answer is that RAG quality depends entirely on your documents. That is why we run a 2 to 4 week fixed-price RAG Mini PoC on your real content, benchmark answer accuracy with named failure cases, and give you an objective go or no-go recommendation before any production commitment.
Do you offer on-premise or hybrid LLM deployment?
Yes. Our OCR & Document Intelligence service runs in three modes: full on-prem (data never leaves your infrastructure), hybrid (raw documents stay inside, only anonymised text is sent to cloud LLMs), or fully managed. This is built for banking, insurance, healthcare, and public administration, where data sovereignty is non-negotiable.
What is the difference between AI consulting and AI development services?
Consulting helps you decide what to build. Development builds it. We do both, and we deliberately blend them: every engagement starts with a discovery and a fixed-price PoC that answers feasibility before any full build. You get strategy, code, and a working prototype from the same team. No vendor handoffs.
Which industries do you specialise in?
We have shipped AI references in banking and finance, insurance, manufacturing, oil and gas, energy, healthcare, AdTech and MarTech.
What does your AI development process look like?
Five steps. Discovery and AI readiness assessment (up to 3 sessions, free entry point). Architecture and data review with a written assessment. Fixed-price PoC on your real data with a benchmark report. Production build, Scrum-based, with CI/CD and full code ownership delivered to your repo. Handoff, MLOps, and optional ongoing support. First production value typically lands in 2 to 4 weeks regardless of which path you start on.
