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.
Fixed-scope delivery · $5K–$70K depending on scope · 95%+ uptime guarantee on production models · No surprises
Three ways to engage
Data Science AuditAssess readiness & plan
Model DevelopmentBuild & train & validate
Full Production MLOpsDesign & deploy & monitor
Every model tied to a business metric Not accuracy scores — revenue impact
Built with industry-standard ML infrastructure
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. Target: MAPE < 15% accuracy.
Computer Vision
Quality inspection, document processing, object detection, and OCR pipelines. From manufacturing defect detection to automated document extraction at scale. Target: 95%+ precision on defect detection.
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. Target: AUC > 0.85 within 30 days.
Fraud Detection
Transaction anomaly scoring and real-time flagging using ensemble models. Catch fraudulent activity before it clears while minimising false positives. Target: < 1% false positive rate at 95% fraud capture.
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
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
Bayesian & Statistical Models
PyMC · Stan · statsmodels
Causal inference, A/B analysis, survival models, and uncertainty quantification
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
How to Engage
Choose the engagement model that matches your starting point.
Whether you need help assessing data readiness, building your first model, or designing a complete production system, we have a clear fixed-scope offer for each stage.
ENGAGEMENT TYPE 01
Data Science Audit
Not sure if ML is the right move or where to start? We assess your data maturity, validate the ROI, define the target metric, and map what you have versus what you need. You leave with a clear architecture recommendation and realistic timeline.
Regular Price
$5K–$8K
1-2 weeks, scoping call included
Launch Partner Price (50% Off)
$2.5K–$4K
In exchange for: Case study + written testimonial + video interview (sent after you pay)
Includes
Data quality audit & readiness assessment
ROI validation (what problem to solve first)
Architecture recommendation & tech stack
Realistic timeline + resource estimate
Written diagnostic report
Your outcome: You know whether ML makes sense and exactly what to build. You can move forward with confidence or decide it's not the right tool.
ENGAGEMENT TYPE 02 (MOST POPULAR)
Model Development
You know what problem to solve. You need a working model that runs on your data and actually improves decisions. We scope, prototype, train, and validate. You get a production-ready model with documented performance guarantees.
Regular Price
$18K–$28K
6-8 week engagement, 20 hrs/week
Launch Partner Price (50% Off)
$9K–$14K
In exchange for: Case study + written testimonial + video interview (sent after you pay)
Includes
Problem scoping & metric definition
Prototype development on your data
Production model training & validation
Performance guarantees (MAPE, AUC, precision)
Model documentation & handover
30-day support + initial monitoring setup
Your outcome: A working model in production. Your team knows how to retrain it. You hit your target metrics or we iterate until you do.
ENGAGEMENT TYPE 03
Full Production MLOps
You need an end-to-end ML system. Architecture, multiple models, deployment pipelines, monitoring, retraining logic, documentation. We design, build, deploy, and hand it over ready to scale. Your team owns it and maintains it.
Regular Price
$40K–$70K
10-14 week engagement, dedicated team
Launch Partner Price (50% Off)
$20K–$35K
In exchange for: Case study + written testimonial + video interview (sent after you pay)
Includes
Full system architecture design
Multiple model development & training
Infrastructure setup (Python, cloud, databases)
Deployment pipelines & CI/CD
Monitoring & performance tracking
Automated retraining & alerting
Team training + production handover
Your outcome: A complete ML system running in production. 95%+ uptime guaranteed. Your team can maintain, monitor, and retrain independently.
Still not sure which engagement type fits? Let's talk.
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 — 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, or build on anonymised and 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.
