AI companions for experience-based industries
Conversational presence designed for the moment a person is actually inside the experience.
Generative Action & Insight Network
The AI companion and living market intelligence layer for the experience economy.
GAIN helps experience-based organizations guide, engage, and learn from users — turning live human interaction into actionable market intelligence.
Currently in validation. Seeking design partners, technical founding talent, and venture-building support.
01 / Problem
Experience-based organizations are flying half-blind.
Organizations know what people buy, attend, watch, visit, rate, or abandon. But they often lack structured, real-time understanding of why an experience mattered, what users needed, what they misunderstood, what they would recommend, what would make them return, and what the organization should do next.
01
A museum knows a visitor bought a ticket. It does not know which room lost them, or what they wanted explained.
02
A hotel knows a guest checked out. It does not know which five minutes decided the review.
03
A streaming service knows a title was abandoned at minute nine. It does not know what the viewer was actually looking for.
04
A festival knows attendance figures. It does not know which moment created the recommendation.
05
A restaurant knows the ticket average. It does not know the unmet demand sitting in the conversation at the table.
06
A university, a resort, a destination, a game studio. Same structure, same blind spot, different vocabulary.
02 / Why now
AI changes the economics of human-experience intelligence.
Until now, deep qualitative insight was slow, expensive, fragmented, and hard to operationalize. Conversational AI, vertical insight models, semantic analysis, human review, and real-time workflows now make it possible to improve the user experience and generate market intelligence at the same time.
Until now
- Qualitative depth priced as a project, not a capability
- Findings arriving weeks after the decision window closed
- Signals fragmented across surveys, reviews, ticketing, and CRM
- Insight that described the past instead of changing the next interaction
- Research budgets that scaled linearly with the number of people asked
Now possible
- Conversation as both the service and the instrument
- Vertical insight models tuned to how a sector actually creates value
- Semantic analysis that structures open-ended language at scale
- Human review keeping interpretation accountable, not automated away
- Real-time workflows that route a finding to the person who can act on it
03 / Product thesis
GAIN improves the experience while learning from it.
GAIN companions guide, educate, contextualize, recommend, answer questions, invite reflection, reduce friction, and extend the value of an experience. Each interaction becomes a source of structured insight for the organization.
01
Experience
A real moment with a real user, in place or online.
02
AI companion
Guides, explains, recommends, and reduces friction in the moment.
03
Vertical insight model
Interprets the interaction through the logic of that sector.
04
Structured signals
Open-ended language becomes comparable, queryable structure.
05
Actionable recommendations
Findings framed as decisions, owners, and trade-offs.
06
Organizational action
The change ships — and the next interaction measures it.
Closed loop · the output of step six becomes the input of step one
04 / Category
Not a survey. Not a CRM. Not a generic chatbot.
GAIN sits between the user and the organization as a single system: a companion that makes the experience better, and an insight layer that makes the organization smarter about it. Six components, one loop.
Configurable vertical insight models
Sector-specific interpretation, because a gallery, a resort, and a game do not create value the same way.
Live qualitative market intelligence
Motivations, friction, expectations, and unmet demand — observed as they happen, not reconstructed later.
Actionable recommendations for organizations
Every finding arrives with a proposed decision, not just a chart to interpret.
Longitudinal pattern recognition
Change tracked across seasons, cohorts, venues, and programs — so trends are visible before they are obvious.
Human review, transparency, and responsible AI principles
People stay in the loop on interpretation, consent, and how conclusions are reached.
05 / Beachhead
Starting where experience is the product.
GAIN begins with arts and cultural institutions because that is where the founder's evidence, credibility, relationships, and domain insight are strongest. Culture is the first wedge, not the final market.
Cultural institutions are unusually good first customers for this category: the experience is the product, the audience relationship is long-lived, and the gap between attendance data and understanding is wide and openly acknowledged.
06 / Expansion
Built for the broader experience economy.
The same companion-led insight model can extend to tourism, hospitality, live entertainment, gastronomy, wellness, education, destinations, streaming, gaming, and other sectors where human experience drives loyalty, retention, recommendation, revenue, and trust.
Culture
TourismHospitalityEntertainment
WellnessEducationGastronomy
Live EventsStreamingGaming
Sequencing is a hypothesis under validation, not a commitment. Expansion order will follow buyer readiness, data access, and willingness to pay — not sector size alone.
07 / Value
One interaction. Two kinds of value.
Most tools ask the organization to choose between serving the user and studying the user. GAIN is designed so that one act does both — the companion earns its place with the user, and the insight layer earns its place with the organization.
For users
A better experience, in the moment
More guided, personalized, meaningful, and useful experiences.
For organizations
Continuous market insight
Continuous market insight into needs, motivations, friction, loyalty drivers, unmet demand, product opportunities, and experience quality.
For leaders
A new decision layer
A new decision layer based on live human experience, not just transactions, ratings, or retrospective surveys.
08 / Stage
Pre-revenue and pre-PMF — but not pre-thesis.
GAIN is currently validating its first product, buyer, MVP scope, design-partner model, and technical founding team. The methodology, founder-market fit, product thesis, and initial vertical strategy are already defined.
Defined Product thesis and category position
Defined Founder-market fit and beachhead vertical
Defined Methodology and initial vertical strategy
In validation First product scope and MVP boundaries
In validation First buyer, budget owner, and willingness to pay
In validation Design-partner model and technical founding team
Next milestone
A scoped companion prototype, 30–50 buyer interviews, and 3–5 design-partner conversations.
Stated plainly: no paying customers, no completed pilots, no confirmed product-market fit, and no guaranteed partnerships. What exists is a defined thesis, a credible wedge, and an open invitation to test both.
09 / Founder
Built from deep founder-market fit.
GAIN is founded by Horacio Lecona, a domain founder and institutional builder with experience leading cultural, philanthropic, university, live-experience, tourism, transportation, and commercial platforms at scale. His work has repeatedly focused on connecting people, institutions, trust, participation, and measurable value.
An AI-native company in validation, built by a founder who has spent a career on the operating side of experience-based institutions — designing participation systems, allocating scarce access, and answering to boards, ministries, sponsors, and audiences at the same time. The insight problem GAIN addresses is one he has repeatedly had to solve without adequate tooling.
Seeking a technical cofounder / CPTO
An AI-native builder to own architecture, model strategy, and the first shipped product.
Seeking design partners
Experience-based organizations willing to test the companion and the insight layer in a live setting.
Seeking venture-building support
Programs and operators who back domain founders at the thesis-to-MVP stage.
Stage
Pre-seed, pre-revenue, in validation. Company incorporation and team formation pending next validation stage.
10 / Get involved
Help build the AI companion layer for the experience economy.
GAIN is looking for design partners, technical founding talent, and venture-building collaborators who believe that the next generation of experience-based organizations will need to understand users in real time — not after the opportunity has passed.
Who we are looking for
- Design partners — experience-based organizations willing to run a scoped companion pilot and share what they learn.
- Technical cofounder / CPTO — an AI-native builder to own architecture, model strategy, and the first shipped product.
- Venture-building support — programs, studios, and operators who back domain founders from thesis to MVP.
- Thesis challengers — people willing to test assumptions while they are still cheap to change.