FOR B2B SAAS — SEED TO SERIES B

Your board wants revenue outcomes — not model accuracy scores. Every AI feature we build is tied to a specific revenue metric before we write a single line of code. 95% uptime, or we fix it free.

Ship an AI feature that works in 60 days. Every model tied to a revenue metric.

95% of AI pilots in B2B SaaS fail to reach production. The failure mode is almost never the model — it’s scope, data readiness, and no business metric defined upfront. We fix all three.

Book a scoping call See which AI use case to build first →

Fixed-scope delivery · $7,997–$65K · Every model tied to a revenue metric · 95%+ uptime or we fix it free

Who this is for

→ CTO with a POC that works 80% of the time — stuck on the last 20%

→ Founder whose competitors are shipping AI features into their sales deck

→ Board asking for an AI roadmap you don’t have yet

Three ways to engage

Data Science Audit Assess readiness & plan
Model Development Build & train & validate
Full Production MLOps Design & deploy & monitor

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

Why AI features stall before they ship

✕
You’ve been promising an AI feature in your sales deck for six months. Engineering keeps de-prioritising it.
The feature stays on the roadmap because it’s technically complex and the spec is still vague. Meanwhile, your competitors are shipping it — and mentioning it in their pricing page.
✕
Your team built a POC. It works 70% of the time. You don’t know how to get it to production without it becoming a full-time maintenance job.
Production means data pipelines, monitoring, and fallback logic. Most teams build the demo and skip the infrastructure.
✕
Your board wants an AI roadmap. You’ve written three drafts and none of them sound credible enough to present.
“We’re exploring AI” doesn’t satisfy investors who’ve seen the same sentence in every Series B pitch. They want a specific feature, a timeline, and a metric it’s supposed to move.
✓  Shipped in 60 days

The AI feature that’s been on your roadmap for 6 months is live. Not a POC — production, with monitoring, fallback logic, and a metric it’s moving.

✓  Holds in production

95%+ uptime. Connected to your alerting. Automatically retrained on schedule. Not something that needs babysitting every time the data drifts.

✓  Revenue-attributed

Every model tied to a number your board cares about — churn rate, lead conversion, activation rate. Not an accuracy score on a spreadsheet nobody reads.

What We Build

The four AI problems B2B SaaS companies pay to solve.

We don’t build AI for AI’s sake. These are the four use cases where B2B SaaS companies consistently see measurable ROI — and where we’ve shipped production-grade solutions.

Churn Prediction

Identify at-risk accounts 30+ days before they cancel using usage patterns, support interactions, and billing signals. Intervention playbooks triggered automatically. Sales gets the list sorted by deal size × churn probability. Target: AUC > 0.85 within 30 days of deployment.

Product-Qualified Lead Scoring

Score every free trial user by likelihood to convert based on activation milestones, feature depth, and team size signals. Sales focuses on the 20% who will pay. Pipeline quality improves immediately. Target: 40%+ reduction in sales cycle from qualified lead to close.

AI Feature Development

Ship the AI feature your competitors are already selling. Semantic search. Smart recommendations. Automated summaries. AI onboarding flows. We scope it, spec it, build it, and hand it to your engineering team with full documentation. In 60 days, not 6 months.

Onboarding Intelligence

Predict which new users will activate and which will ghost in week one — from first-session signals. Trigger the right intervention at the right moment: a different onboarding flow, a human reach-out, or an in-product nudge. Target: 20%+ improvement in week-1 retention.

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.

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

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

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

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.

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

Document 1500% Year 1 ROI

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. Competitors change theirs daily. Demand shifts. You're leaving 13–16% revenue on the table.

Outcome

+13–16% revenue growth

Verified across 30+ e-commerce platforms

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

$100M+ in averted losses

For mid-size retail + logistics

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

Prevents $M+ in losses

Catch model decay in hours, not days

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

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, 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.

Book a scoping call Send an email instead

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