Machine Learning · Deep Learning · Computer Vision

Production ML that solves real business problems. Not science projects.

We build, deploy, and maintain machine learning systems — from demand forecasting to computer vision pipelines. Every model ships with monitoring, retraining logic, and a clear business metric it’s accountable to.

Book a scoping call See our automation services →

Hourly or project-based · No minimum commitment · Scale up or down as needed

Three service tiers

ML Consulting Strategy + architecture
Model Development Build + train + validate
Production MLOps Deploy + monitor + retrain

Every model tied to a business metric
Not accuracy scores — revenue impact

Built with industry-standard ML infrastructure
PyTorch TensorFlow OpenAI Anthropic AWS Google Cloud Hugging Face Python MLflow Weights & Biases Pinecone Apache Spark Apache Airflow

What We Build

ML solutions for problems that actually cost you money.

Every model we build is tied to a business outcome you can measure. If you can’t put a dollar value on the problem, we’ll help you find one that qualifies — or tell you ML isn’t the right tool.

Demand Forecasting

Predict inventory needs, staffing requirements, and revenue trajectories with time series models trained on your historical data. Reduce overstock, prevent stockouts, and plan with confidence.

Computer Vision

Quality inspection, document processing, object detection, and OCR pipelines. From manufacturing defect detection to automated document extraction at scale.

Recommendation Engines

Product, content, and next-best-action recommendations built on collaborative filtering and deep learning. Increase basket size, engagement, and conversion rates.

Churn Prediction

Identify at-risk accounts weeks before cancellation using usage patterns, support interactions, and billing signals. Intervene early with targeted retention.

Fraud Detection

Transaction anomaly scoring and real-time flagging using ensemble models. Catch fraudulent activity before it clears while minimising false positives.

Dynamic Pricing

Price optimisation based on demand curves, competitive positioning, and elasticity models. Maximise revenue per transaction without eroding customer trust.

NLP & Document Processing

Text classification, entity extraction, and summarisation at scale. Process contracts, support tickets, reviews, and internal documents automatically.

Predictive Maintenance

Equipment failure prediction from sensor and telemetry data. Reduce unplanned downtime and shift from reactive repairs to scheduled interventions.

Customer Segmentation

Behavioural clustering for marketing, product, and pricing decisions. Move beyond demographics to segments that reflect how customers actually behave.

Voice & Conversational AI

Voice bots, IVR automation, and speech-to-text pipelines. Handle inbound calls, route conversations, and extract structured data from voice interactions.

Knowledge Base & RAG

Retrieval-augmented generation over your internal documentation. Ask questions in natural language and get accurate answers grounded in your own data.

Automated Content Analysis

Sentiment analysis, review mining, and social listening at scale. Understand what customers are saying across channels without reading every comment manually.

Solutions by Industry

ML problems worth solving depend on where you operate.

Different industries generate different data and face different prediction challenges. Pick yours to see the ML applications that deliver measurable ROI.

SaaS companies generate dense behavioural data — login frequency, feature adoption curves, support ticket patterns, billing events. The ML opportunity is turning that signal into automated predictions: which accounts will churn, which leads will convert, and which onboarding paths produce the highest LTV.

  • Churn prediction models trained on product usage + billing signals
  • Lead scoring using product-qualified signals, not just demographics
  • Feature adoption forecasting to guide roadmap prioritisation
  • Automated customer health scoring across usage, support, and payment data
  • Expansion revenue prediction — which accounts are ready to upsell
  • NLP-powered support ticket classification and routing

Your product data predicts revenue outcomes before they show up in a dashboard

SaaS businesses sit on more predictive signal per customer than any other model. The gap is turning that signal into decisions that happen automatically.

Healthcare generates massive volumes of unstructured data — clinical notes, imaging, lab results, patient communications. ML applications in healthcare focus on pattern recognition at scale: diagnostic support, patient risk stratification, document processing, and resource allocation optimisation.

  • Medical image analysis — radiology, pathology, and dermatology support
  • Patient risk stratification using EHR data and clinical markers
  • Clinical document NLP — extraction and summarisation of unstructured notes
  • Readmission prediction models for post-discharge monitoring
  • Resource demand forecasting for staffing and capacity planning
  • Drug interaction detection and pharmacovigilance signal mining

Clinical decisions supported by pattern recognition at a scale humans cannot match

ML in healthcare works when it augments clinical judgment with data-driven risk signals — not when it tries to replace the clinician.

Fintech operates at transaction velocities where manual review is impossible. ML models in this space handle fraud detection, credit risk scoring, transaction monitoring, and regulatory compliance — all in real time, with audit trails that satisfy regulators.

  • Real-time fraud detection with adaptive scoring models
  • Credit risk assessment using alternative data signals
  • Anti-money laundering transaction pattern detection
  • Customer lifetime value prediction for lending and pricing
  • Algorithmic trading signal generation and backtesting
  • Regulatory reporting automation with model explainability

Risk decisions made in milliseconds with full auditability

Financial ML models need to be fast, explainable, and auditable. We build with those constraints from day one — not as an afterthought.

E-commerce businesses generate rich behavioural data at every touchpoint — browse patterns, cart behaviour, purchase history, return rates, search queries. ML turns that data into personalised experiences, demand-aware inventory decisions, and pricing strategies that adapt in real time.

  • Product recommendation engines using collaborative and content-based filtering
  • Demand forecasting tied to inventory management and procurement
  • Dynamic pricing models based on elasticity, competition, and demand
  • Customer segmentation for personalised marketing and retention
  • Search relevance optimisation using learning-to-rank models
  • Return prediction models to flag high-risk orders pre-shipment

Every customer interaction gets smarter without adding headcount

The data already exists in your platform. We build the models that connect it to the decision points — product pages, pricing, email sequences, inventory orders.

Professional services firms — agencies, consultancies, legal, accounting — operate on expertise and client relationships. ML applications here focus on knowledge management, resource optimisation, and automating the document-heavy workflows that eat billable hours.

  • Document classification and extraction for contracts, filings, and briefs
  • Resource allocation optimisation — matching skills to project needs
  • RAG-powered knowledge bases over internal expertise and past work
  • Client outcome prediction based on engagement patterns
  • Proposal generation assistance using historical SOW analysis
  • Time entry classification and billing anomaly detection

Institutional knowledge becomes queryable and reusable

The biggest asset in professional services is accumulated expertise. ML makes it searchable, reusable, and available to every team member.

Logistics operations deal with high-dimensional optimisation problems — routing, scheduling, inventory positioning, demand planning. ML models handle the combinatorial complexity that humans and spreadsheets cannot, while adapting to real-time disruptions.

  • Route optimisation with real-time constraint handling
  • Demand forecasting for warehouse positioning and inventory allocation
  • Predictive maintenance for fleet and equipment management
  • Computer vision for package sorting, damage detection, and compliance
  • Delivery time estimation using traffic, weather, and operational data
  • Supply chain disruption prediction and alternative sourcing models

Operational decisions optimised at a speed and scale that manual planning cannot achieve

Logistics is fundamentally an optimisation problem. ML handles the variables, constraints, and real-time adjustments that static planning breaks on.

Our Stack

The technologies we deploy in production.

We pick the right tool for the problem, not the trendiest framework. Every technology below has been used in production systems we have built and maintained.

Time Series

Prophet · ARIMA · LSTM

Forecasting demand, revenue, and operational load from historical patterns

Computer Vision

PyTorch · OpenCV · YOLO

Object detection, quality inspection, OCR, and image classification

NLP

Transformers · spaCy · NLTK

Text classification, entity extraction, sentiment, and summarisation

LLMs

GPT-4 · Claude · Llama

Fine-tuning, prompt engineering, and production deployment

RAG

Pinecone · Weaviate · pgvector

Vector DBs, embedding pipelines, and retrieval-augmented generation

Generative AI

Stable Diffusion · DALL-E · Codex

Image generation, code generation, and content synthesis

MLOps

MLflow · W&B · Sagemaker

Model versioning, experiment tracking, and production monitoring

AutoML

AutoGluon · H2O · Optuna

Automated feature engineering, model selection, and hyperparameter tuning

Federated Learning

PySyft · Flower · TFF

Privacy-preserving ML across distributed data without centralising it

Synthetic Data

CTGAN · Gretel · SDV

Training data generation, augmentation, and privacy-safe dataset creation

Deep Learning

PyTorch · TensorFlow · JAX

Neural architecture design, training, and optimisation at scale

Data Engineering

Spark · Airflow · dbt

Data pipelines, ETL, and warehouse architecture for ML workloads

Hire AI Specialists

ML engineers, data scientists, and AI architects — embedded in your team or running the project independently.

Hourly or project-based. No minimum commitment. Scale up or down as the work requires.

LAUNCH PRICING
Role Junior Mid Senior Lead
ML / Data Science Engineer $35/hr $55/hr $85/hr $110/hr
AI/ML Architect — — $110/hr $130/hr
ML Project Manager — $55/hr $70/hr $85/hr
Business Analyst (AI) — $45/hr $65/hr —
Backend Python Engineer $30/hr $45/hr $55/hr $75/hr
Computer Vision Engineer — $60/hr $90/hr $115/hr

Looking for process automation, workflow optimisation, and AI agents instead?

See our Automation & Process Intelligence services →

How an Engagement Runs

From scoping to production monitoring — four stages, no surprises.

01

Scoping — define the problem and the metric

We identify the business problem, validate whether ML is the right approach, define the target metric, and map the data you have versus the data you need. You leave the scoping call knowing exactly what we’d build, what data is required, and what the realistic accuracy and timeline look like.

02

Prototype — prove the model works on your data

We build a working prototype on a subset of your data. This validates the approach before committing to a full production build. You see real predictions on real data — not a demo on a public dataset. If the prototype doesn’t hit the target metric, we stop and reassess before spending more.

03

Production — deploy with proper engineering

The validated model gets production infrastructure: API endpoints, data pipelines, error handling, logging, and integration with your existing systems. This is the difference between a notebook that runs on a laptop and a system your business depends on.

04

Monitoring — catch drift before it costs you

Every production model ships with monitoring for data drift, prediction quality, and business metric tracking. Retraining triggers are defined upfront. You know when the model needs attention — not when someone notices the predictions stopped making sense three months ago.

Who You’re Working With

One person. Accountable for the outcome, not just the output.

No account managers, no junior handoffs. Jake runs every engagement directly — from the first scoping call through to production handover.

Jake McMahon
Jake McMahon — ML & AI Strategist
I’m Jake, the founder of ProductQuant. I’ve spent 8+ years in B2B SaaS product and growth — building data infrastructure, deploying ML models, and learning the hard way that the technology is never the bottleneck. The data quality is. The problem definition is. The gap between what the model predicts and what the business actually needs to decide — that’s where projects fail.
I started ProductQuant because I kept seeing the same pattern: teams that spent months training models that never made it to production. Not because the models were bad, but because nobody had defined what “good” meant in business terms, built the data pipeline to support it, or set up monitoring to catch when it degraded.
What I won’t do:
  • Build a model without a defined business metric it’s accountable to
  • Deploy to production without monitoring and retraining logic
  • Promise accuracy numbers before seeing your actual data
  • Recommend deep learning when a gradient-boosted tree will do the job
  • Hand over a Jupyter notebook and call it a deliverable
What I will do:
Build ML systems that run in production, not in notebooks. Every model ships with data pipelines, monitoring, documentation, and a clear retraining schedule. If ML isn’t the right solution for your problem, I’ll tell you that in the scoping call — before you spend anything.

Common Questions

What most people ask before starting an ML project.

It depends on the problem. Some classification tasks work with a few thousand labelled examples. Time series forecasting typically needs 2+ years of historical data to capture seasonality. Computer vision projects can start with hundreds of annotated images if transfer learning applies. The scoping call is where we assess whether your data volume and quality are sufficient — and if not, what it would take to get there.
Most data is. Data cleaning and feature engineering are a standard part of every ML project — typically consuming more time than the model training itself. We scope this work explicitly so there are no surprises. If the data quality issues are fundamental (e.g. the signal you need was never captured), we’ll identify that early and help you set up the data collection before attempting to model.
Whichever solves the problem best. If a pre-trained model with fine-tuning meets your accuracy requirements, there’s no reason to train from scratch. If your problem requires custom architecture or domain-specific training data, we build that. The decision is based on your data, your accuracy requirements, and what’s maintainable long-term — not on what sounds more impressive.
A prototype on clean data can be ready in 2–4 weeks. Production deployment with pipelines, monitoring, and integration typically adds another 4–8 weeks depending on complexity. Computer vision and NLP projects that require annotation tend to run longer. The scoping call produces a realistic timeline based on your specific data and requirements.
That’s what the prototype stage is for. We validate on your actual data before committing to a full production build. If the prototype doesn’t meet the target metric, we diagnose why — insufficient data, wrong features, wrong problem framing — and either adjust the approach or recommend stopping. You never pay for a production build on a model that hasn’t proved itself first.
Every engagement includes documentation, a handover session, and monitoring setup. Retraining triggers and procedures are defined before we close out. If your team has Python/ML experience, they can maintain and retrain independently. If not, we offer ongoing monitoring and retraining as a separate engagement. The goal is always for you to own the system — we build it so that’s realistic.
Data security and compliance requirements are defined in the scoping stage. We can work within your infrastructure (no data leaves your environment), implement differential privacy techniques, use federated learning for distributed data, or build on anonymised/synthetic datasets. For regulated industries (healthcare, finance), model explainability and audit trails are built in from the start, not added after the fact.
Our automation services focus on process improvement, workflow automation, and AI agents built on top of clean operational infrastructure. This page is about machine learning — building predictive models, training on your data, deploying inference pipelines. Sometimes the two overlap (e.g., a churn prediction model feeding an automated retention workflow), and we scope those as a combined engagement.

Thirty minutes. You leave knowing whether ML is the right tool for your problem.

Tell us the business problem. We’ll assess whether ML can solve it, what data you need, and what realistic accuracy and timeline look like — before you commit to anything.

Book a scoping call Send an email instead

No pitch. No questionnaire. A technical assessment of your ML opportunity.