Visual Sales Tools That Accelerate Buyer Decisions

Who This Is For

Built for B2B software companies and agencies who serve B2B clients.

Good fit

✓Founders and CTOs evaluating AI feasibility before committing budget
✓Product teams that shipped an AI feature but haven’t measured its revenue impact
✓Engineering leads inheriting an AI roadmap with no ROI case attached
✓Growth teams whose personalisation or prediction model is underperforming
✓Agencies who sell AI capabilities to B2B clients and need the build run end-to-end

Not the right fit

—Companies that want AI for the press release, not the product
—Teams that haven’t identified a specific business problem yet
—Anyone looking for a chatbot wrapper or GPT-4 integration in a weekend
—Projects with no historical data to train a model on
—Businesses looking for the cheapest vendor, not the accountable one

What We Build

Have a specific ML problem? Start there.

Every engagement is fixed-scope with a clear, measurable outcome. Pick the category where the problem is most live right now — or book a call and we’ll map it together.

Predict & Score

Forecast outcomes and rank priorities

When decisions are made on gut feel, lagging reports, or static plans — ML models give you a ranked, probability-weighted view of what’s coming.

You get a production model tied to a specific business metric — with monitoring, retraining logic, and a clear output your team acts on.

Demand Forecasting

Predict inventory needs, staffing, or resource allocation across seasonality and disruptions.

Equipment Failure Prediction

Flag machines and systems likely to fail before they do — manufacturing, utilities, fleets.

Lead & Customer Scoring

Rank accounts, applicants, or prospects by likelihood to convert, churn, or expand.

Risk & Fraud Scoring

Real-time risk flags on transactions, claims, or applications — before damage is done.

Price Optimisation

Model that finds the revenue-maximising price by segment, demand signal, and context.

Read & Extract

Turn unstructured documents into structured data

When high-volume intake — contracts, invoices, applications, forms — is processed by hand and the data never makes it into your systems reliably.

Documents processed at scale, classified, and pushed into your workflow without manual touch.

Contract & Document Intelligence

Extract clauses, dates, and obligations from legal and financial documents at scale.

Invoice & Receipt Processing

Automate data entry from PDFs and images — zero manual keying, error rates near zero.

Form & Application Processing

Classify and extract fields from high-volume intake forms — insurance, onboarding, compliance.

Search & Retrieve

Make internal knowledge instantly queryable

When the answer is somewhere in your docs, manuals, or support history — but no one can find it without digging through folders or asking a colleague.

Anyone on the team gets an accurate answer from your own data in seconds — without switching tools or waiting on someone who knows.

RAG Knowledge Base

AI search over your docs, manuals, support history, or product data — internal or customer-facing.

Regulatory & Compliance Search

Search across policy libraries, case law, or compliance documentation — instant, cited answers.

See & Detect

Vision models on images and video

When quality control, inspection, or classification relies on human eyes — and the volume makes it inconsistent, slow, or expensive to scale.

Consistent, high-speed visual inspection that catches what humans miss — and scales without adding headcount.

Defect Detection

Visual QC on production lines or physical assets — flag defects before they leave the floor.

Document Scanning & Classification

Classify scanned documents without manual sorting — mail rooms, archives, mixed-format inboxes.

Recommend & Personalise

Surface the right thing at the right moment

When every user, customer, or agent sees the same thing regardless of context — and personalisation would directly move revenue or engagement.

Every user gets a relevant next step — the right product, content, or action — based on their actual behaviour, not generic rules.

Product & Content Recommendations

Next best item, article, or offer personalised to each user’s behaviour — e-commerce, media, SaaS.

Next Best Action

Route customers to the right offer, agent, or response at the right moment in the journey.

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

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.

	Data Science Audit $5K–$8K 1–2 weeks · scoping call included	Most Popular Model Development $18K–$28K 6–8 weeks · 20 hrs/week	Full Production MLOps $40K–$70K 10–14 weeks · dedicated team
Best for	Validating ML feasibility before committing budget — or building a credible board roadmap.	You have a defined problem and need a production model tied to a business metric.	End-to-end ML system with multiple models, owned and maintained by your team.
Data readiness & ROI validation	✓	✓	✓
Architecture recommendation	✓	✓	✓
Written diagnostic report	✓	✓	✓
Prototype on your data	—	✓	✓
Production model + validation	—	✓	✓
Performance guarantees (MAPE / AUC / precision)	—	✓	✓
Documentation + team handover	Written report	Full docs	Full docs + training
Multiple models	—	—	✓
MLOps infrastructure (pipelines, CI/CD)	—	—	✓
Monitoring & automated retraining	—	Initial setup	Fully automated
Post-engagement support	—	30 days	Optional retainer
Outcome guarantee	Honest go/no-go call	Hit metric or we iterate free	95%+ uptime guaranteed
	Book audit →	Start a model →	Scope MLOps →

Still not sure which engagement type fits? Let's talk.

Book a free diagnostic call

No sales pitch. Just honest assessment of your situation and the right next step.

Fast-Track Opportunities

Don't know where to start? Here are four high-ROI problems we solve in 2–4 weeks.

These are narrow, specific problems where ML delivers immediate measurable value. No lengthy architecture design. Fixed scope. Clear outcomes.

Churn Root Cause Diagnosis

The problem: Your churn prediction model says who will leave, but not WHY. You can't act on a prediction if you don't know the root cause.

Outcome

+5–15% retention lift

Delivers measurable retention lift

Deliverables:

✓ Root cause model (usage + support + billing)
✓ SHAP explainability per at-risk segment
✓ Integration with your existing churn model
✓ Retention action playbook

Timeline: 2–3 weeks

Budget: $5,997

Dynamic Pricing Engine

The problem: Your pricing is static. Demand shifts and your revenue doesn’t capture it. A dynamic pricing model responds in real time — adjusting for demand signals, inventory, and buying patterns.

Outcome

Increase revenue with dynamic pricing

Real-time price optimisation tied to your demand signals

Deliverables:

✓ Price elasticity models per segment
✓ A/B test framework for safe rollout
✓ Real-time pricing rules engine
✓ Monitoring dashboard + alert setup

Timeline: 3–4 weeks

Budget: $6,997

Disruption-Proof Forecasting

The problem: Your forecasts break when supply chains get disrupted, seasonality shifts, or markets change. You're planning for yesterday, not tomorrow.

Outcome

Forecasts that adapt to market shifts

Improve planning accuracy despite disruptions

Deliverables:

✓ Anomaly-resistant forecast models
✓ External data integration (weather, supply)
✓ Automated feature engineering
✓ Real-time replanning triggers

Timeline: 2–3 weeks

Budget: $6,997

Model Health Dashboard

The problem: Your demand or churn model is silently degrading. You don't know until revenue drops 30 days later. You need 24/7 monitoring.

Outcome

Monitor model health and catch decay early

Get alerts before prediction quality degrades

Deliverables:

✓ Data drift detection
✓ Prediction quality monitoring
✓ Automated alerting (Slack/email)
✓ Retraining trigger recommendations

Timeline: 2–3 weeks

Budget: $4,997

All four are fixed-scope. No surprises. Book a call to map your problem to one of these, or explore something custom.

Pick Your Fast-Track Path

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

Founder-led delivery. Accountable for the outcome, not just the output.

Every engagement runs with a dedicated technical lead from scoping through production handover — backed by specialist ML engineers when scope demands. No account-manager middle layer.

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.

How much data do we need before ML makes sense?

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.

What if our data is messy or incomplete?

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.

Do you build custom models or use off-the-shelf solutions?

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.

How long does a typical ML project take?

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.

What happens if the model doesn’t perform well enough?

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.

Can our team maintain the model after handover?

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.

How do you handle sensitive or regulated data?

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.

What’s the difference between this and your automation services?

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.

Visual Sales Tools That Accelerate Buyer Decisions

Built for B2B software companies and agencies who serve B2B clients.

Good fit

Not the right fit

Have a specific ML problem? Start there.

ML problems worth solving depend on where you operate.

The technologies we deploy in production.

Time Series

Computer Vision

NLP

LLMs

RAG

Generative AI

MLOps

AutoML

Bayesian & Statistical Models

Synthetic Data

Deep Learning

Data Engineering

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

Choose the engagement model that matches your starting point.

Don't know where to start? Here are four high-ROI problems we solve in 2–4 weeks.

Churn Root Cause Diagnosis

Dynamic Pricing Engine

Disruption-Proof Forecasting

Model Health Dashboard

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

Scoping — define the problem and the metric

Prototype — prove the model works on your data

Production — deploy with proper engineering

Monitoring — catch drift before it costs you

Founder-led delivery. Accountable for the outcome, not just the output.

What most people ask before starting an ML project.

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