Why Sliders.AI Exists
We live in a world drowning in binary decisions: yes/no, click/don’t click, A/B. But real judgment lives in nuance — in emotion, trade-offs, intensity, and values.
Sliders.AI creates a new kind of logic layer: a multidimensional interface for capturing what people actually care about. Users express priorities via emotional sliders. Systems get structured, high-signal input. Everybody wins.
This is not just a better UI.
It’s a behavioral infrastructure for the AI age.
💰 Pitch deck
INT. ARNOLD’S DINER – DAY
POTSIE
I told you, Richie — there’s just too much going on in this deck!
RICHIE
Yeah but didn’t he see the part about “opposites collapsing”?
That was, like, right there … somewhere!
RALPH
Hey, why not make the slides bigger?
RICHIE
Oh no, we forgot JMOS. Can you squeeze it in?
POTSIE
Richie no, you can’t put an encyclopedia inside of a fortune cookie!
FONZIE enters.
He slides in the door, struts over, and scans the deck like it’s a broken jukebox with a secret code.
RALPH
Hey… I think we could wrap this word around that word. You know, like a word hug.
POTSIE
That might be the worst idea you’ve ever had.
RICHIE
Wait—which words?
RALPH
Why don’t we make the colors look like candy? Everybody likes candy.
FONZIE
Alright, listen up… Lemme tell you all you gotta say.
Sliders are real-time channels for emotional clarity.
They let people tune intent, not type it.
They’re interfaces for understanding — fast, silent, intuitive.
They move through alignment, not explanation.
You slide one, and it reveals how you feel.
Then the chick slides, and now you’re synced.
Ayye.
That’s emotional bandwidth.
That’s adaptive connection.
That’s a motorcycle built for two minds.
Wanna feel your way forward and score?
Slide, baby slide.
POTSIE
Cool.
FONZIE
You said it.
AL (from behind the counter)
I once put ketchup on pancakes.
Didn’t make ‘em better — just made ‘em redder.
Whole diner stares at him.
Just sayin’… maybe it ain’t the font.
🤔 Three Guys Walk Out of a Pitch…
[Scene: Three investors at a café, just after the pitch]
Tech Guy (sipping espresso):
“That was… different. I still don’t know what to make of it. It’s not a product in the traditional sense — it’s like… a control system for thought?”
Money Guy (tilting back his chair):
“Yeah, I kept waiting for the actual pitch. You know, here’s the market, here’s the user, here’s the CAC-to-LTV. But instead he starts talking about ‘judgment’ and ‘bias vectors.’ I thought he was gonna levitate.”
Wildcard (laughing):
“But you heard it, right? When he said, ‘We capture judgment in real time’? That stuck. That’s monetization. I’ve never heard it put that way before. We’re always chasing behavior — and this guy’s saying he’s catching the thing that comes before behavior.”
Tech Guy:
“Right, and that’s the weird part. I kept thinking, this isn’t about inference. It’s not AI trying to guess what the user wants. It’s the user telling the system — but not with words, with sliders, with feelings. And it logs that decision vector. That’s novel.”
Money Guy:
“Is it valuable though? That’s what I couldn’t get past. Sliders? Feels like a toy. Like, what are we actually selling here?”
Wildcard:
“Well that’s the flip, right? He’s not selling sliders. He’s capturing the moment of decision. That little spark before you click. Before you buy. Before you even know why you’re leaning one way.”
Tech Guy:
“Exactly. It’s like — everyone else builds after-the-fact systems. They mine the trails. But he’s capturing the intent upstream, in the moment. If that works? That’s cleaner signal than anything we’ve seen.”
Money Guy:
“Cleaner how?”
Tech Guy:
“No noise from the action loop. Just raw input — user says, ‘I trust this more than that,’ or ‘I’m 60% in.’ That’s unstructured in most systems. He makes it structured. Portable. Re-usable.”
Wildcard:
“And he’s saying it scales across domains. Pick a job, a date, a news story, a house — same core tech. Different front end.”
Money Guy (pausing):
“Okay. So what’s the moat? What’s to stop someone from copying it?”
Tech Guy:
“He’s got patents. Sixteen of them. And they’re not narrow — they’re interface-level. They’re not features, they’re interaction models.”
Wildcard:
“He said something else I liked. Something about all other systems flattening humans. This one listens. That’s a hell of a narrative.”
Money Guy:
“But who’s the buyer? That’s the thing — if this is so good, who’s paying? Not users, right?”
Wildcard:
“That’s the trick. He builds something people use, and the real buyer is whoever needs clean decision data: brands, platforms, research, politics, retail. Anyone who wants to know why someone almost said yes — or almost said no.”
Tech Guy:
“I’ve seen startups burn millions trying to guess what he’s trying to just ask the user. But he does it without language. That’s the leap.”
Wildcard (rubbing his temples):
“Jesus. I think my brain’s melting. Like… did we just get pitched by Steve Jobs’ ghost?”
Tech Guy:
“Pull it together, man. This isn’t a vision quest — it’s a term sheet in disguise.”
Money Guy (nodding slowly):
“Okay. So let’s say it’s real. Why don’t I see a deck that leads with that? Why all the noise about licensing, and marketplaces, and stick-figure provocateurs?”
Wildcard:
“Because he’s an inventor, not a packager. He thinks in worlds, not verticals. That’s the risk — and the reason to pay attention.”
Tech Guy:
“I’ll tell you this: if even one of his ideas works, if one product hits, the platform behind it becomes obvious. The IP becomes obvious. The signal becomes the story.”
Money Guy:
“So we’d be betting on the signal?”
Wildcard:
“No. You’d be betting on the ability to turn human judgment into data. That’s not just a bet. That’s an unlock.”
Tech Guy:
“Feels like something we’ll be chasing in three years if we walk now.”
Money Guy:
“Feels like something we’ll regret not chasing in six months if it breaks through.”
Wildcard:
“Then maybe we should stop looking for polish — and start looking for leverage.”
[Beat of silence. They sip.]
🧲 The Gunslinger’s Journey
(aka. Getting on the Ground Floor of Emotional AI)
Scene One: The Simulation
The town of Simulation sat baking under a flat, uncaring sun.
Fresh logos plastered over crumbling walls. Buzzwords scrawled across half-built platforms. Pitch decks tacked to saloon doors like wanted posters. It looked shiny at a glance—but the shine was just desperation in a can.
Every corner of town was broken.
The sheriff’s office had four lawsuits and no law. The bank kept investing in dreams and cashing out in regrets. The doctor’s tools beeped, buzzed, and guessed wrong. The saloon ran sentiment scores on patrons and still couldn’t figure out what they wanted to drink. The school had six dashboards and no learning.
And everyone knew it.
The place was a joke. Except nobody was laughing anymore.
Then came the sound—low and steady. Hooves. One after the other. Slow. Deliberate.
Out of the heat shimmer, a horse emerged. Not a proud stallion. No, this thing was a mongrel. Patchy mane, ribs like razors, a snarl in its gait like it’d fought wolves and maybe eaten one. Its rider slumped in the saddle, coated in dust, hat pulled low. Coat frayed to threads. The kind of figure that looked like trouble—because he was.
He muttered as he rode. Cursed under his breath. Something about corrupted models and trust decay curves. No one could quite hear it. But everyone felt it.
Simulation paused.
He tethered the horse outside the saloon. It bit the post.
Then he stepped inside.
Scene Two: The First Shot
The saloon wasn’t shelter. It was the last place in town with four walls and no pitch deck nailed to them. Inside, the Gang sat low—not out of fear, but because they’d been broken one too many times. They were the last good ones.
Linda, the mayor. Ted and Janine from the clinic. Rob behind the bar. Doctor Paul. Schoolteacher Aria. Marta from the store. Not saints. Just real.
And done.
Then he stepped inside.
Chaz Bleeker laughed when he saw him. “You all still listening to lunatics now? This guy looks like he crawled outta an email spam filter.”
The gunslinger didn’t blink. He drew a slider—Trust—and dropped it to zero. Chaz twitched. Clarity next. Cranked down. Chaz stuttered, stumbled, eyes wide.
Dropped.
Sheriff Rex stood up for the first time in months. Cuffed the pitchman without a word. The investor pinned to Chaz’s app feed pulled their funding live. Dashboard crashed in the street. It was over.
Scene Three: Justice With Teeth
Next came Darcy Malloy. A pitch-slinger with ten failed startups and a deck that screamed lies. She tried to run another con on the crowd.
The gunslinger drew all four sliders:
- Trust: 0
- Belief: null
- Clarity: shattered
- Strategic Value: minus infinity
She dropped to her knees, deck burning out in her hands. Reality rejected her. Her contract dissolved in the cloud. A startup she ghostwrote filed for bankruptcy before sundown.
And the Gang stood up.
Scene Four: What Sliders Do
They walked out of the saloon. No fanfare. Just tools in hand.
Decision Intelligence: Linda synced her city dashboard. Budget flows realigned. Services hit where they were needed. No politics. Just data.
Enterprise Productivity: Ted and Janine uploaded clinic backlogs. Prioritized cases by value and urgency. Throughput doubled. Patients seen. Lives stabilized.
Behavioral Analytics: Rob tuned his sentiment system. Now it told him what people meant, not just what they said. Sales and service clicked.
AI-Augmented Collaboration: Aria and Paul shared emotional markers across school and clinic. Coordinated support replaced crossed wires. Students and patients improved.
Human-Centered Design: Marta rebuilt her storefront interface. Sliders tied to intent, not history. Stock moved. Returns vanished.
It didn’t spread overnight. But it spread. One fix at a time.
Scene Five: The Ride
He didn’t stay.
He stood on the edge of town, arms crossed, coat still ragged.
Signal worked now. Not perfect. But aligned.
He spit in the dirt, cursed the sky, and rode east.
The horse snarled.
The end
Business Model Overview
We monetize through two channels:
1. Licensing Our Technology to the World
We integrate Sliders into existing ecosystems — platforms, marketplaces, chatbots, enterprise tools — and license the tech + models powering it.
Based on projections across 15 strategic partners, the 5-year value uplift enabled by Sliders.AI ranges from $26.75B to $57.1B. If we capture just 10–20% of that value, our licensing revenue would reach $2.7B–$11.4B.
📊 Licensing Uplift Projections
| Company | Uplift (Low) | Uplift (High) | Why They’ll License |
|---|---|---|---|
| ADP | $500M | $1.2B | To better quantify employee preferences, reduce churn, and optimize internal recommendations in HR platforms. |
| Alphabet (Google) | $3B | $6B | To personalize search, tune agents, and enrich user data across YouTube, Ads, and Gemini. |
| Stride | $300M | $700M | To increase student engagement and self-guided learning outcomes using emotional feedback layers. |
| Meituan | $1B | $2.5B | To optimize food/service recommendations based on sentiment, urgency, and emotional state. |
| Indeed | $2B | $5B | To match candidates with jobs using sliders for intent, willingness, and role preference modeling. |
| Jane Street | $1B | $2.5B | To give quant traders emotional context for decision-making and portfolio stress scenarios. |
| Johnson & Johnson (J&J) | $5B | $10B | To reduce disengagement in trials and increase patient retention via sentiment-sensitive UX. |
| Medtronic | $1B | $2B | To interpret and adjust feedback loops in health devices using real-time emotional sliders. |
| Palantir | $2B | $5B | To add affective layers to large-scale modeling platforms — defense, enterprise, public health. |
| Zillow | $1.15B | $2.2B | To fine-tune listing relevance and intent modeling in high-variance, high-emotion home searches. |
| Publicis Groupe | $500M | $1.5B | To personalize ad targeting with behavioral sliders that go beyond demographics and clicks. |
| Walmart | $2B | $5B | To enhance product discovery and impulse shopping using micro-emotional profiles. |
| Meta | $6B | $10B | To power agent economy interactions and tune LLM behavior via direct emotional input. |
| Sony Interactive Entertainment | $800M | $2B | To adapt game difficulty, mood pacing, and narrative beats to player behavior in real time. |
| Tinder | $500M | $1.5B | To move beyond swipe fatigue and into emotion-rich partner matching. |
2. Building Our Own Ventures
We’re not just licensing. We’re building.
Across nine in-house patents and concept engines — from mental health to storytelling to ethics to physics simulations — we project $1.3B to $7.7B in net profit over five years if properly funded and scaled.
These include:
| Product/Patent | Profit (Low) | Profit (High) | Why People Will Use It |
|---|---|---|---|
| SituSlide (Mood ψ² Existence) | $300M | $1B | To express mood non-verbally in chats, improving agent empathy and tuning. |
| Identinoise (Story ψ² Identity) | $150M | $600M | To generate biographical and brand storytelling through emotionally coherent timelines. |
| Story Arc (Character ψ² Scene) | $100M | $400M | To develop resonant characters and narratives in games, film, or education. |
| Mischievousity (Reaction ψ² Action) | $80M | $250M | To simulate unpredictable responses for game AI, improv tools, or behavioral agents. |
| Soil Lover (Microbe ψ² Support) | $70M | $200M | To model micro-scale emotional health and community resilience in AR/VR tools or education. |
| Dialectic Sliding (Opposites ψ² Synthesis) | $60M | $150M | To resolve contradictions and polarities for mental health, mediation, or design. |
| Integrify (Interpretation ψ² Ethic) | $50M | $120M | To guide complex ethical decisions in AI assistants, chatbots, and governance tools. |
| Hoverboard (Multiverse ψ² Universe) | $10M | $2B | To build wild, user-driven physics playgrounds with real-time emotional rule-breaking. |
| ThoughtLang (Language ψ² Word) | $500M | $3B | To create highly personalized, meaning-rich AI chat environments and next-gen LLM inputs. |
These aren’t app ideas. These are modular cognition engines. Sliders isn’t a feature — it’s the kernel for intelligent, emotionally aware systems.
Behind the Interface: The Real Engine
Across this page, we’ve focused on what you see — sliders, stories, interfaces. That’s the surface.
But the deeper value lives beneath.
Sliders.AI is not just a UI innovation. It’s a data engine — one that transforms emotional expression into high-resolution, structured intelligence that platforms can act on in real time.
Under the hood, we’re powering:
- Intent scoring and preference vectors
- Live decision-state modeling
- Training data for AI personalization
- Micro-emotional tagging for rec engines and agents
- Composable APIs for product and insight integration
This backend is designed for inference, not just expression. It makes other systems smarter — not by forcing hard choices, but by interpreting the soft signals that drive human behavior.
And it’s monetizable.
We’re not just helping platforms look good. We’re helping them convert better.
We’re not just capturing emotion — we’re productizing judgment.
This backend unlocks a second layer of value: not just UX improvement, but deep performance gains across targeting, prioritization, churn reduction, personalization, and conversion.
It’s not visible in every table, and it doesn’t show up in every demo —
but it’s working. And it’s part of what makes Sliders.AI a platform, not a product.
In short: what we show is the topsoil — but we’ve built a deep root system. That’s what allows us to scale across sectors and tune to purpose. If you’re a platform leader, systems architect, or investor looking under the hood, know this:
The true power of Sliders.AI is not what it displays — it’s what it enables.
Why Investors Care
- 💸 Massive TAM: Emotional decision layers touch every vertical — HR, health, commerce, media, search, AI agents.
- 🔐 Filed IP: Sliders are protected — quantized logic, compositional semantics, and more.
- ⚙️ Drop-In Ready: Low-code frontend & backend modules, available today.
- 📊 Demonstrated Traction: Projects aligned with real-world uplift. Licensing partners mapped. Prototypes live.
Sliders aren’t just a new UX—they’re a new logic structure for emotion-aware computing.
💡 (Example) AITECH: Why It’s a Game-Changer
In the rapidly expanding world of AI development and LLM customization, Sliders.AI offers a natural, intuitive way to train models without coding:
- Developers can fine-tune outputs for tone, truth, creativity, etc.
- End-users can steer generation in real time via sliders.
- Enterprises can layer emotional intelligence onto existing AI stacks.
This is a bridge between human intuition and algorithmic logic, and could be the missing interface layer between LLMs and end-users.
Final Takeaway
We’re not another interface company.
We’re the emotional API for the next generation of digital experience.
Licensing brings immediate upside.
Internal builds generate exponential lift.
Either way, Sliders wins.