Token-based pricing has dominated the AI industry since the beginning. It’s simple to implement but fundamentally misaligned: customers pay for volume of computation rather than the actual quality or business value of the output. As AI moves deeper into production workflows and agentic systems, this model creates friction, unpredictable costs, and poor incentive alignment between providers and users.
Touchmark is building the infrastructure to price AI outputs based on measurable quality and delivered value instead.
As a fresh Y Combinator Summer 2026 company, Touchmark is an early mover in one of the most important (and under-served) infrastructure layers for the maturing AI economy. This profile provides the timely, human-curated analysis that generic data platforms cannot match.
Data
Funding Stage: YC S26-backed. No additional large external rounds publicly disclosed yet.
Launch / Founding Date: Founded 2026 (YC Summer 2026 batch). Currently in active development with public positioning and SDK availability.
Key Leadership:
Ilia Bolgov, Co-Founder — Previously in Product at Revolut. Maths background from Imperial College London.** Roman Yanushevskyi**, Co-Founder — Previously 2× Quantitative Research intern at Citadel Securities and AI Engineering intern at Lovable. Strong technical and quantitative foundation.
The founding team combines product experience at scale with deep quantitative and AI engineering expertise. Team size is early-stage (typical for recent YC companies in this batch).
Core Tech Stack / Approach: TypeScript SDK (@touchmark/sdk
) that records sessions, emits structured events (including model outputs and code diffs), and feeds them into a quality evaluation and valuation engine. The system returns quality-adjusted prices rather than raw token counts. It is designed to integrate into existing AI application or agent workflows to enable value-based or quality-based monetization models.
Editorial
Plain English Pitch (2 sentences): Touchmark lets AI companies charge customers based on how good or useful the AI’s output actually is, instead of just counting tokens or API calls. It uses a lightweight SDK to track what the AI produces during a session, automatically evaluates the quality of that output, and calculates a fair, value-adjusted price.
ICP & Primary Use Cases:
Primary users are builders of AI products, platforms, and agentic systems who want more sophisticated and aligned monetization than pure token-based billing. This includes AI SaaS companies, autonomous agent platforms, coding assistants, and any product where output quality varies significantly and directly impacts customer value.
The core problem solved is the misalignment created by token-based pricing: it doesn’t reward (or penalize) quality, makes costs unpredictable for users, and fails to capture the real economic value delivered in many workflows.
Key use cases include implementing quality-based pricing for AI features, creating usage tiers or success-based billing models, and providing transparent value measurement to both providers and customers.
Hiring Patterns:
As a very early-stage YC S26 company, Touchmark is focused on building core infrastructure. Expect hiring in AI evaluation/quality systems, SDK and developer tooling, backend infrastructure, and product roles as they expand beyond initial design partners and refine their quality models.
Buying Signals:
- Recent YC S26 acceptance and public announcements.
- Active development and sharing of the TypeScript SDK.
- Clear, differentiated positioning around the future of AI pricing.
- Strong founder backgrounds in product (Revolut) and quantitative/AI engineering (Citadel Securities, Lovable).
These signals indicate a team moving quickly from idea to usable infrastructure in a high-interest category.
Proprietary Insights
Proprietary Score — AI Value Pricing Infrastructure Index:
Touchmark scores strongly on this custom early-stage metric. Key factors include the founders’ complementary expertise (product at scale + quantitative/AI engineering), the extremely timely thesis (token pricing is reaching its limits as AI use cases mature), YC validation, and the practical SDK-first approach that lowers friction for adoption. As more AI products move into production and agentic workflows, demand for quality-aware and value-based pricing infrastructure is expected to grow significantly.
Competitor Matrix (Editorial Comparison):
| Dimension | Touchmark (Quality/Value-Based Pricing) | Traditional Token-Based Billing (OpenAI, Anthropic, etc.) | Usage-Based / Custom Billing Tools | Manual / Spreadsheet Pricing | Outcome/Success-Based Models (Custom) |
|---|---|---|---|---|---|
| Core Strength | Automated quality evaluation + value-adjusted pricing | Simple, predictable volume billing | Flexible metering | Highly customizable | Aligns payment with results |
| Quality Awareness | High (explicit output evaluation) | None | Low | None | High (but manual) |
| Ease of Integration | High (TypeScript SDK) | Very High | Medium | Low | Low |
| Alignment with Value | Very High | Low | Medium | Variable | Very High |
| Current Stage | YC S26, early SDK | Mature | Growing | Ubiquitous | Niche / Custom |
| Best For | AI products where output quality varies significantly | High-volume, predictable usage | Basic metering needs | Very small teams | High-stakes outcome contracts |
Founder & Company Vision Highlights:
The core message is that “the future of AI pricing won’t be per token.” Touchmark is building the infrastructure to evaluate AI output quality and price accordingly, creating better alignment between what customers pay and the actual value they receive. The founders’ backgrounds in product at a major fintech and quantitative/AI engineering roles inform a pragmatic, developer-friendly approach via the SDK.
Deeper proprietary perspectives on quality evaluation methodologies, supported use cases, pricing model flexibility, and long-term vision for AI monetization infrastructure are best obtained through direct outreach to the founding team.
Why This Matters in 2026
As AI capabilities advance and move into more complex, high-value workflows, token-based pricing is increasingly seen as a blunt instrument. It creates misaligned incentives and fails to reward (or even measure) the quality that actually drives customer outcomes. Touchmark is one of the earliest dedicated infrastructure plays focused on solving this problem at the platform level.
High-intent long-tail keywords naturally targeted include:
“Touchmark competitors”, “Touchmark AI pricing”, “quality-based AI pricing”, “value-based pricing for AI outputs”, “Touchmark YC S26”, and broader phrases around “future of AI monetization” or “alternatives to token-based billing”.