When AI Products Can’t Find PMF, Build a Landing Client Instead
PMF isn’t always found in the product
Sometimes, it starts with one strategic client
early-stage-ai-monetization
Startup growth
AI product gtm strategy
PMF Doesn’t Always Start With Product
The Trap Most AI Founders Fall Into
AI founders often spend months (sometimes years) building a product with one goal in mind: achieve product-market fit.
The assumption is: if we keep building, the right users will come.
But for emerging technologies like LLM agents, custom AI pipelines or computer vision tools, this rarely works.
Because in most markets, especially smaller or undeveloped verticals, there is no ready market waiting.
There are only problems, messy, fragmented, and often under-defined.
And this is where the trap begins:
“We have a powerful tool, but no one is really buying it.”
It often starts with a client-specific solution.
In early-stage AI startups, the real “fit” doesn’t happen in your codebase. It happens in a room with one potential client,
where you co-design a solution that solves a real operational problem they’re willing to pay to fix.
This isn’t scaling, it’s calibrated solutioning.
It’s not about building for the masses.
It’s about building for one landing client that gives you directional signal.
Reverse-engineering your GTM strategy through high-context clients.
The Case for a Landing Client MVP
The Case for a Landing Client MVP
Two Types of Revenue Models: Exploration vs. Scale
Understanding this distinction helps you avoid premature scaling traps.
1. Exploratory Revenue Models
Built for learning
Involve direct collaboration with early clients
Pricing is bespoke or service-wrapped
Success is defined by insight, not margin
Used when:
You’re validating vertical fit
Your use case still requires human involvement
You need access to workflows/data not available in public
Example:
Using your AI workflow builder to help a freight logistics company reduce manual audits, manually integrating their CSVs and Slack workflows, not just building a dashboard.
2. Scale Revenue Models
Designed for repeatability
Pricing is standardized
GTM is automated or semi-automated
You’re optimizing margin, not learning
Used when:
You’ve proven a narrow use case
ICP is clear
You’ve seen repeat interest from similar profiles
But: Trying to scale before exploration almost always leads to wasted engineering and stalled go-to-market.
Left： Evaluate Client Engagement
Key Questions:
Did the client achieve measurable ROI (e.g., 30% efficiency gain)?
Are they willing to expand usage/refer peers?
Right：Assess Generalizability
Decision Criteria:
API/SaaS Path:
Low domain-specific logic (e.g., document OCR).
Multiple clients need similar functionality.
Vertical Path:
High regulatory/data specificity (e.g., radiology image analysis).
Client willing to be a launch partner.
Pricing & GTM Strategy
For API/SaaS:
Model: Usage-based pricing ($0.01/call) + tiered subscriptions.
GTM: Developer portals, platform integrations (Slack/Zapier).
For Vertical SaaS:
Model: Annual license ($50k+/year) + per-unit fees.
GTM: Industry events, compliance-focused sales.
40% Rule
Source: Borrowed from SaaS/consumer tech metrics, where:
<20% retention: Likely a failing product.
20-40%: Needs iteration.
>40%: Strong PMF signal (Andrew Chen, a16z).
For AI products: Adjusted downward because:
Early AI tools often solve "nice-to-have" problems (e.g., productivity boosters vs. mission-critical workflows).
User behavior is still adapting to AI interfaces (e.g., prompt engineering fatigue).
Why Early-Stage Can Use 40%
Lower baseline: Compared to mature SaaS (where 70%+ retention is expected), early AI products face:
Higher user skepticism ("Is this just a ChatGPT wrapper?").
Unstable performance (hallucinations, edge-case failures).
Focus on core users: 40% filters out "tourists" and identifies:
Power users who see enough value to return.
Use cases worth doubling down on (e.g., "Our 40% retained users are all freelance designers").
How to Improve Retention
If below 40%:
Segment users: Is retention low because of:
Poor onboarding? (Fix with guided tutorials).
Limited use cases? (Kill underused features).
Trigger habitual use:
Add daily/weekly automated reports (e.g., "Your AI-generated social posts got 50 clicks").
Let power users lead:
Identify what retained users do differently → Amplify those workflows.
Key Takeaway
40% is a starting threshold for early AI products to identify "what’s working."
It’s not the end goal, scale demands higher retention, but early on, it helps avoid building for imaginary users.
Adjust based on:
Industry standards (B2C AI vs. healthcare AI).
User segments
Business model (API tools need lower retention than standalone apps).
The Case for a Landing Client MVP
The Case for a Landing Client MVP
Instead of chasing PMF in the abstract, build your GTM through one high-context client.
What does this mean?
Find one strategic buyer (often not from your initial target market)
Co-create a narrow solution with them
Observe how workflows adapt, how users interact, and where friction shows up
From there, decide: Is this a use case worth generalizing? Or a niche worth owning?
What to Track Instead of "PMF"
For AI teams in the exploratory phase, track these signals instead:
Time-to-prototype: how fast can we test assumptions?
Internal champion behavior: are they pushing the project forward internally?
Cost-to-insight: how expensive is it to learn something meaningful?
Repeatability: can we see the same problem elsewhere?
Closing: Don’t Build for “Market” Build for Insight
In the earliest stages, AI teams don’t need scale.
They need signal.
Signal doesn’t come from dashboards or traffic.
It comes from client conversations, context-rich experiments, and messy pilot projects.
Your first real PMF might not come from your product.
It might come from your first paying client.
Anchor Articles and Updates
Why Growth Marketing Is Not Digital Marketing and Why This Distinction Matters — It’s not that your marketing strategy is flawed. You might just be addressing the wrong problem.
Content as a Revenue Tool: Shortening Time-to-Close in Startup Sales — Content that shortens sales cycles, Not just builds traffic
Building Revenue Systems When Scale Isn’t an Option — Profitability First: How Startup Teams Can Drive Revenue in Constrained Markets
Case Studies
Mountain Gentleman — They knew they needed to go digital but had no idea how to start.So we saw things through the rider’s eyes.It wasn’t just about buying gear because it felt like building out your dream GTR.Every part of the journey was designed to match that thrill.
CoinRank — CoinRank needed a fresh way to stand out in crypto. We created a short video strategy that turns complex info into quick, engaging clips that grab attention fast.
Latest Updates
(GQ® — 02)
©2025
Latest Updates
(GQ® — 02)
©2025
FAQ
FAQ
01
What does a project look like?
02
How is the pricing structure?
03
Are all projects fixed scope?
04
Can I adjust the project scope after we start?
05
How do we measure success?
06
Do you offer ongoing support after project completion?
07
How long does a typical project last?
08
Is there a minimum commitment?
01
What does a project look like?
02
How is the pricing structure?
03
Are all projects fixed scope?
04
Can I adjust the project scope after we start?
05
How do we measure success?
06
Do you offer ongoing support after project completion?
07
How long does a typical project last?
08
Is there a minimum commitment?
When AI Products Can’t Find PMF, Build a Landing Client Instead
PMF isn’t always found in the product
Sometimes, it starts with one strategic client
early-stage-ai-monetization
Startup growth
AI product gtm strategy
PMF Doesn’t Always Start With Product
The Trap Most AI Founders Fall Into
AI founders often spend months (sometimes years) building a product with one goal in mind: achieve product-market fit.
The assumption is: if we keep building, the right users will come.
But for emerging technologies like LLM agents, custom AI pipelines or computer vision tools, this rarely works.
Because in most markets, especially smaller or undeveloped verticals, there is no ready market waiting.
There are only problems, messy, fragmented, and often under-defined.
And this is where the trap begins:
“We have a powerful tool, but no one is really buying it.”
It often starts with a client-specific solution.
In early-stage AI startups, the real “fit” doesn’t happen in your codebase. It happens in a room with one potential client,
where you co-design a solution that solves a real operational problem they’re willing to pay to fix.
This isn’t scaling, it’s calibrated solutioning.
It’s not about building for the masses.
It’s about building for one landing client that gives you directional signal.
Reverse-engineering your GTM strategy through high-context clients.
The Case for a Landing Client MVP
Two Types of Revenue Models: Exploration vs. Scale
Understanding this distinction helps you avoid premature scaling traps.
1. Exploratory Revenue Models
Built for learning
Involve direct collaboration with early clients
Pricing is bespoke or service-wrapped
Success is defined by insight, not margin
Used when:
You’re validating vertical fit
Your use case still requires human involvement
You need access to workflows/data not available in public
Example:
Using your AI workflow builder to help a freight logistics company reduce manual audits, manually integrating their CSVs and Slack workflows, not just building a dashboard.
2. Scale Revenue Models
Designed for repeatability
Pricing is standardized
GTM is automated or semi-automated
You’re optimizing margin, not learning
Used when:
You’ve proven a narrow use case
ICP is clear
You’ve seen repeat interest from similar profiles
But: Trying to scale before exploration almost always leads to wasted engineering and stalled go-to-market.
Left： Evaluate Client Engagement
Key Questions:
Did the client achieve measurable ROI (e.g., 30% efficiency gain)?
Are they willing to expand usage/refer peers?
Right：Assess Generalizability
Decision Criteria:
API/SaaS Path:
Low domain-specific logic (e.g., document OCR).
Multiple clients need similar functionality.
Vertical Path:
High regulatory/data specificity (e.g., radiology image analysis).
Client willing to be a launch partner.
Pricing & GTM Strategy
For API/SaaS:
Model: Usage-based pricing ($0.01/call) + tiered subscriptions.
GTM: Developer portals, platform integrations (Slack/Zapier).
For Vertical SaaS:
Model: Annual license ($50k+/year) + per-unit fees.
GTM: Industry events, compliance-focused sales.
40% Rule
Source: Borrowed from SaaS/consumer tech metrics, where:
<20% retention: Likely a failing product.
20-40%: Needs iteration.
>40%: Strong PMF signal (Andrew Chen, a16z).
For AI products: Adjusted downward because:
Early AI tools often solve "nice-to-have" problems (e.g., productivity boosters vs. mission-critical workflows).
User behavior is still adapting to AI interfaces (e.g., prompt engineering fatigue).
Why Early-Stage Can Use 40%
Lower baseline: Compared to mature SaaS (where 70%+ retention is expected), early AI products face:
Higher user skepticism ("Is this just a ChatGPT wrapper?").
Unstable performance (hallucinations, edge-case failures).
Focus on core users: 40% filters out "tourists" and identifies:
Power users who see enough value to return.
Use cases worth doubling down on (e.g., "Our 40% retained users are all freelance designers").
How to Improve Retention
If below 40%:
Segment users: Is retention low because of:
Poor onboarding? (Fix with guided tutorials).
Limited use cases? (Kill underused features).
Trigger habitual use:
Add daily/weekly automated reports (e.g., "Your AI-generated social posts got 50 clicks").
Let power users lead:
Identify what retained users do differently → Amplify those workflows.
Key Takeaway
40% is a starting threshold for early AI products to identify "what’s working."
It’s not the end goal, scale demands higher retention, but early on, it helps avoid building for imaginary users.
Adjust based on:
Industry standards (B2C AI vs. healthcare AI).
User segments
Business model (API tools need lower retention than standalone apps).
The Case for a Landing Client MVP
Instead of chasing PMF in the abstract, build your GTM through one high-context client.
What does this mean?
Find one strategic buyer (often not from your initial target market)
Co-create a narrow solution with them
Observe how workflows adapt, how users interact, and where friction shows up
From there, decide: Is this a use case worth generalizing? Or a niche worth owning?
What to Track Instead of "PMF"
For AI teams in the exploratory phase, track these signals instead:
Time-to-prototype: how fast can we test assumptions?
Internal champion behavior: are they pushing the project forward internally?
Cost-to-insight: how expensive is it to learn something meaningful?
Repeatability: can we see the same problem elsewhere?
Closing: Don’t Build for “Market” Build for Insight
In the earliest stages, AI teams don’t need scale.
They need signal.
Signal doesn’t come from dashboards or traffic.
It comes from client conversations, context-rich experiments, and messy pilot projects.
Your first real PMF might not come from your product.
It might come from your first paying client.
Anchor Articles and Updates
Why Growth Marketing Is Not Digital Marketing and Why This Distinction Matters — It’s not that your marketing strategy is flawed. You might just be addressing the wrong problem.
Content as a Revenue Tool: Shortening Time-to-Close in Startup Sales — Content that shortens sales cycles, Not just builds traffic
Building Revenue Systems When Scale Isn’t an Option — Profitability First: How Startup Teams Can Drive Revenue in Constrained Markets
Case Studies
Mountain Gentleman — They knew they needed to go digital but had no idea how to start.So we saw things through the rider’s eyes.It wasn’t just about buying gear because it felt like building out your dream GTR.Every part of the journey was designed to match that thrill.
CoinRank — CoinRank needed a fresh way to stand out in crypto. We created a short video strategy that turns complex info into quick, engaging clips that grab attention fast.
FAQ
01
What does a project look like?
02
How is the pricing structure?
03
Are all projects fixed scope?
04
Can I adjust the project scope after we start?
05
How do we measure success?
06
Do you offer ongoing support after project completion?
07
How long does a typical project last?
08
Is there a minimum commitment?
When AI Products Can’t Find PMF, Build a Landing Client Instead
PMF isn’t always found in the product
Sometimes, it starts with one strategic client
early-stage-ai-monetization
Startup growth
AI product gtm strategy
PMF Doesn’t Always Start With Product
The Trap Most AI Founders Fall Into
AI founders often spend months (sometimes years) building a product with one goal in mind: achieve product-market fit.
The assumption is: if we keep building, the right users will come.
But for emerging technologies like LLM agents, custom AI pipelines or computer vision tools, this rarely works.
Because in most markets, especially smaller or undeveloped verticals, there is no ready market waiting.
There are only problems, messy, fragmented, and often under-defined.
And this is where the trap begins:
“We have a powerful tool, but no one is really buying it.”
It often starts with a client-specific solution.
In early-stage AI startups, the real “fit” doesn’t happen in your codebase. It happens in a room with one potential client,
where you co-design a solution that solves a real operational problem they’re willing to pay to fix.
This isn’t scaling, it’s calibrated solutioning.
It’s not about building for the masses.
It’s about building for one landing client that gives you directional signal.
Reverse-engineering your GTM strategy through high-context clients.
The Case for a Landing Client MVP
Two Types of Revenue Models: Exploration vs. Scale
Understanding this distinction helps you avoid premature scaling traps.
1. Exploratory Revenue Models
Built for learning
Involve direct collaboration with early clients
Pricing is bespoke or service-wrapped
Success is defined by insight, not margin
Used when:
You’re validating vertical fit
Your use case still requires human involvement
You need access to workflows/data not available in public
Example:
Using your AI workflow builder to help a freight logistics company reduce manual audits, manually integrating their CSVs and Slack workflows, not just building a dashboard.
2. Scale Revenue Models
Designed for repeatability
Pricing is standardized
GTM is automated or semi-automated
You’re optimizing margin, not learning
Used when:
You’ve proven a narrow use case
ICP is clear
You’ve seen repeat interest from similar profiles
But: Trying to scale before exploration almost always leads to wasted engineering and stalled go-to-market.
Left： Evaluate Client Engagement
Key Questions:
Did the client achieve measurable ROI (e.g., 30% efficiency gain)?
Are they willing to expand usage/refer peers?
Right：Assess Generalizability
Decision Criteria:
API/SaaS Path:
Low domain-specific logic (e.g., document OCR).
Multiple clients need similar functionality.
Vertical Path:
High regulatory/data specificity (e.g., radiology image analysis).
Client willing to be a launch partner.
Pricing & GTM Strategy
For API/SaaS:
Model: Usage-based pricing ($0.01/call) + tiered subscriptions.
GTM: Developer portals, platform integrations (Slack/Zapier).
For Vertical SaaS:
Model: Annual license ($50k+/year) + per-unit fees.
GTM: Industry events, compliance-focused sales.
40% Rule
Source: Borrowed from SaaS/consumer tech metrics, where:
<20% retention: Likely a failing product.
20-40%: Needs iteration.
>40%: Strong PMF signal (Andrew Chen, a16z).
For AI products: Adjusted downward because:
Early AI tools often solve "nice-to-have" problems (e.g., productivity boosters vs. mission-critical workflows).
User behavior is still adapting to AI interfaces (e.g., prompt engineering fatigue).
Why Early-Stage Can Use 40%
Lower baseline: Compared to mature SaaS (where 70%+ retention is expected), early AI products face:
Higher user skepticism ("Is this just a ChatGPT wrapper?").
Unstable performance (hallucinations, edge-case failures).
Focus on core users: 40% filters out "tourists" and identifies:
Power users who see enough value to return.
Use cases worth doubling down on (e.g., "Our 40% retained users are all freelance designers").
How to Improve Retention
If below 40%:
Segment users: Is retention low because of:
Poor onboarding? (Fix with guided tutorials).
Limited use cases? (Kill underused features).
Trigger habitual use:
Add daily/weekly automated reports (e.g., "Your AI-generated social posts got 50 clicks").
Let power users lead:
Identify what retained users do differently → Amplify those workflows.
Key Takeaway
40% is a starting threshold for early AI products to identify "what’s working."
It’s not the end goal, scale demands higher retention, but early on, it helps avoid building for imaginary users.
Adjust based on:
Industry standards (B2C AI vs. healthcare AI).
User segments
Business model (API tools need lower retention than standalone apps).
The Case for a Landing Client MVP
Instead of chasing PMF in the abstract, build your GTM through one high-context client.
What does this mean?
Find one strategic buyer (often not from your initial target market)
Co-create a narrow solution with them
Observe how workflows adapt, how users interact, and where friction shows up
From there, decide: Is this a use case worth generalizing? Or a niche worth owning?
What to Track Instead of "PMF"
For AI teams in the exploratory phase, track these signals instead:
Time-to-prototype: how fast can we test assumptions?
Internal champion behavior: are they pushing the project forward internally?
Cost-to-insight: how expensive is it to learn something meaningful?
Repeatability: can we see the same problem elsewhere?
Closing: Don’t Build for “Market” Build for Insight
In the earliest stages, AI teams don’t need scale.
They need signal.
Signal doesn’t come from dashboards or traffic.
It comes from client conversations, context-rich experiments, and messy pilot projects.
Your first real PMF might not come from your product.
It might come from your first paying client.
Anchor Articles and Updates
Why Growth Marketing Is Not Digital Marketing and Why This Distinction Matters — It’s not that your marketing strategy is flawed. You might just be addressing the wrong problem.
Content as a Revenue Tool: Shortening Time-to-Close in Startup Sales — Content that shortens sales cycles, Not just builds traffic
Building Revenue Systems When Scale Isn’t an Option — Profitability First: How Startup Teams Can Drive Revenue in Constrained Markets
Case Studies
Mountain Gentleman — They knew they needed to go digital but had no idea how to start.So we saw things through the rider’s eyes.It wasn’t just about buying gear because it felt like building out your dream GTR.Every part of the journey was designed to match that thrill.
CoinRank — CoinRank needed a fresh way to stand out in crypto. We created a short video strategy that turns complex info into quick, engaging clips that grab attention fast.
FAQ
What does a project look like?
How is the pricing structure?
Are all projects fixed scope?
Can I adjust the project scope after we start?
How do we measure success?
Do you offer ongoing support after project completion?
How long does a typical project last?
Is there a minimum commitment?