Content Engine

Your category. Dominated. In search. In AI. In your buyer's mind.

Not content marketing. A compounding market presence that makes you the obvious choice. 8-12 posts/month, distributed everywhere buyers research.

What you get:
8-12 blog posts/month (AI-augmented)
Email newsletter distribution
AI citation optimization
50% traffic lift ● Cited by ChatGPT, Claude, Perplexity
See Your Category Opportunity See How It Works
What's Included
Content Audit
$5K–$8K
Content Engine
$12K–$18K/mo
Content + GEO
$20K–$30K/mo
All tiers include monthly performance reporting
Jake McMahon
Jake McMahon
PLG & GTM Growth Strategist
LinkedIn

Your board keeps asking about AI strategy. You don't have one.

Your engineering team is researching AI. Nothing has shipped in 6 months.

They're evaluating models, testing APIs, building prototypes in staging. But nothing is live. Nothing is generating revenue.

Competitors announced AI features. Yours are still "in development".

Every week you wait, the gap widens. They're closing deals on AI roadmaps. You're still in "evaluation mode".

You don't know which AI features would actually move revenue.

Everyone has opinions. Nobody has data. You're guessing which AI use cases your users would actually adopt.

6 months from now: 3-5 AI features that users actually adopt.

Your board sees AI adoption metrics in every meeting. Your sales team closes deals faster because you have AI capabilities competitors don't.

Your engineering team has a repeatable playbook for shipping AI features. No more "we need to research this." No more 6-month "evaluations." They ship AI the way they ship any other feature — with confidence, with instrumentation, with adoption targets.

You're not asking "should we do AI?" You're asking "which AI feature next?"

What changes after 6 weeks

Before After
AI strategy is a slide deck AI strategy is 3-5 shipped features with adoption data
Prototypes dying in staging Production features with 10%+ adoption
Engineering researching "which model to use" Engineering shipping features on a repeatable playbook
Board asks "what's our AI strategy?" Board sees AI adoption, retention lift, expansion revenue
Competitors announce AI first You're known as "the AI company" in your category

How It Works

Two frameworks. One outcome: AI that ships.

A.I.M. runs the engagement. D.A.T.A. ensures your foundation is ready. Together: production AI in 6-8 weeks.

A
Phase 1

Assess

Week 1-2. We identify which AI features would actually move revenue — not which models are coolest.

  • AI opportunity map (5-8 use cases ranked)
  • Build vs. buy recommendation
  • Revenue potential for each use case
  • D.A.T.A. readiness assessment
I
Phase 2

Implement

Week 3-6. We build and deploy one production AI feature — not a prototype, not a proof of concept.

  • Production AI feature (deployed)
  • Model selection + fine-tuning
  • AI UX patterns (onboarding, explainability)
  • Adoption instrumentation
M
Phase 3

Measure

Week 7-8. We track adoption, iterate based on usage data, and hand off a playbook your team can use.

  • Success dashboard (real-time adoption)
  • Iteration backlog (based on 30-day usage)
  • AI playbook (your team's internal guide)
  • 30-day adoption report

Your AI is only as good as your data.

Before we build anything, we run D.A.T.A. — a readiness assessment that tells you if your data can actually support AI. No surprises in week 4.

D

Depth

Do you have enough historical data for model training?

  • Volume assessment by use case
  • Time range adequacy check
  • Minimum viable dataset identified
A

Accuracy

Is your data clean, labeled, and reliable?

  • Data quality scoring
  • Label consistency audit
  • Missing value analysis
T

Taxonomy

Is your event tracking structured for ML consumption?

  • Event schema review
  • Feature engineering readiness
  • Real-time vs. batch assessment
A

Access

Can models query your data in real-time, or is it siloed?

  • Data pipeline audit
  • API accessibility check
  • Integration complexity score

Why this matters: We've seen teams spend $200K on AI features only to discover their data couldn't support them. D.A.T.A. catches this in week 1. If your data scores low, we fix that first — before writing a single model training script.

Pricing

Three ways to engage

AI Opportunity Audit

One-time · 3 weeks

$7,997
  • 5-8 AI use cases ranked by revenue impact
  • Data readiness assessment
  • Competitive AI feature analysis
  • Build vs. buy recommendation
  • Implementation roadmap
Book Strategy Call

AI Feature Build Sprint

One-time · 6-8 weeks

$35K–$65K
  • 1 production AI feature shipped
  • Model fine-tuning or API integration
  • AI UX patterns + onboarding
  • Adoption instrumentation
  • Success dashboard + iteration plan
Book Strategy Call

AI Growth OS

Ongoing · 3-month minimum

$18K–$28K/mo
  • 1-2 AI features shipped per month
  • Adoption dashboard (updated weekly)
  • Model performance monitoring
  • Competitive AI feature tracking
  • Monthly AI strategy review
Book Strategy Call

What's included (AI Feature Sprint — $50K example)

AI Opportunity Assessment $15,000
Data Pipeline Build $12,000
Model Development $18,000
AI UX Design $10,000
Production Deployment $8,000
Adoption Instrumentation $7,000
Total itemized value $70,000
AI Feature Sprint price $50,000

Adoption Guarantee

If your AI feature doesn't hit 10% adoption among active users within 60 days of launch, we iterate free until it does. We're incentivized to build something your users actually want — not just something that works technically.

Common questions

Everything you need to know before booking a call.

What if our data isn't ready for AI? +
We run a D.A.T.A. assessment in week 1 — Depth, Accuracy, Taxonomy, Access. If your data scores low, we fix that first. Sometimes that means 2 weeks of data pipeline work before model training. Better to fix data upfront than build a model on garbage.
Do we need ML infrastructure already set up? +
No. We use whatever gets the job done fastest. Sometimes that's Hugging Face + FastAPI. Sometimes it's your existing cloud infrastructure. We're tool-agnostic — we optimize for speed to production, not technical purity.
What happens after the sprint? +
You have a production AI feature with adoption tracking. Most clients continue into AI Growth OS ($18K-$28K/mo) to ship 1-2 more features per month. Some take the playbook and run it themselves. Both are fine — you own everything we build.
Can you work with our existing stack? +
Yes. We've deployed on AWS, GCP, Azure, Vercel, and everything in between. We integrate with your existing auth, databases, and APIs. The goal is to ship AI that feels native to your product — not a bolted-on demo.
How is this different from an AI consultancy? +
Most AI consultancies deliver a prototype and leave. We deliver production features with adoption guarantees. They measure accuracy. We measure adoption + revenue. They hand you a Jupyter notebook. We hand you shipped code with instrumentation.
Trusted by B2B SaaS teams dominating categories
FormDR
Scale Insights
QForm
Net Atelier
Hacking HR
You don't have a content problem. You have a systems problem.

Most SaaS companies that struggle with content don't lack writers or ideas. They lack a system that connects content production to actual business growth.

They publish inconsistently. They have blogs but no distribution. They have traffic but no conversions. They have a board asking "what's our content ROI?" and no answer.

The problem isn't the writing. The problem is that there's no content engine underneath it.

What Changes

After 6 weeks, you're not guessing anymore

Before
  • 0-4 posts/month, inconsistent
  • Not in AI recommendations
  • Sales educates every prospect from scratch
  • CEO asks "is content working?"
After
  • 8-12 posts/month, every month, compounding
  • Cited by ChatGPT, Claude, Perplexity for 10+ queries
  • Prospects come pre-sold, closes 30% faster
  • Dashboard shows rankings, citations, SQLs attributed
Month 1
  • Keyword strategy (50-100 keywords)
  • First 8 posts published
  • Email distribution live
Month 2
  • 50% traffic lift achieved
  • First AI citations appearing
  • Monthly reporting dashboard live
Honest Comparison

ProductQuant vs. Other Options

We're not the right fit for everyone. Here's how we compare.

ProductQuant Growth Agency Fractional CPO
What you get Operating system + shipped features Recommendations + slide decks Strategy + guidance
Time to value 6-8 weeks 3-6 months 2-4 months
Team required None (we execute) Your PM + eng team Your entire team
Cost (3 months) $35K–$65K $150K–$300K $60K–$120K
What happens after You own the system Dependency continues They leave, system stays

Note: If you have a strong internal growth team and just need strategic guidance, a fractional CPO might be a better fit. If you want someone to own growth end-to-end long-term, an agency might work. If you want to ship fast and own the system afterwards, we're your best option.

The cost of waiting

Let's do the math. If you're at $2M ARR with 20% activation and the benchmark for your product type is 35%:

You're converting 15% fewer signups to revenue every month.

At 500 signups/month with $100 ACV: that's 75 users who paid for acquisition and never became customers.

That's $7,500 MRR lost. Every month. $90K ARR burning while you read this.

And that's just activation. We haven't talked about churn, expansion, or competitive positioning.

6-8 weeks from now, this could be fixed. Or you could keep burning $90K/month. Your call.

Who You're Working With

Not an agency. Not a hire. A partner.

Jake McMahon
Jake McMahon — PLG & GTM Growth Strategist
I'm Jake, the founder of ProductQuant. I've spent the last 8 years as a product operator — not a consultant, not an agency owner. I've been the person responsible for growth at B2B SaaS companies from $1M to $50M ARR.
I started ProductQuant because I kept seeing the same pattern: companies hiring growth agencies that delivered slide decks, or hiring fractional CPOs who delivered strategy but no execution. Meanwhile, their engineers were building features nobody used, their designers were redesigning based on opinions, and their boards were asking "what's the growth plan?" with no answer.
What I won't do:
  • Deliver a 100-page strategy deck and leave
  • Recommend you hire 5 people before we've shipped anything
  • Charge you $50K/mo to manage a team you already have
  • Tell you what you want to hear instead of what you need to hear
What I will do:
Ship production features in 6-8 weeks. Instrument everything. Show you what's working and what's not. Give you a system you can run yourself. And if I can't find $100K+ in addressable revenue opportunity in the first 6 weeks, I'll tell you — and we won't continue.
Risk Reversal

Four guarantees. Zero risk.

We're so confident in our system that we put our money where our mouth is.

Adoption Guarantee

If your AI feature doesn't hit 10% adoption among active users within 60 days of launch, we iterate free until it does.

Timeline Guarantee

If we don't ship production features in 6-8 weeks, you pay 10% less per week of delay. If we miss by 4+ weeks, the final 4 weeks are free.

Revenue Guarantee

If we can't find $100K+ in addressable revenue opportunity in the first 6 weeks, we won't continue the engagement. No hard feelings.

Ownership Guarantee

Everything we build is yours. Code, dashboards, playbooks, documentation. We don't rent you a system. We install one you own.

Ready to ship your first AI feature?

6-8 weeks. Production-ready. 10% adoption guaranteed.

Book AI Strategy Call See Sample AI Roadmap