Companies from the studio's first chapters.

PulsePanel spans sociological research, customer-experience, and public-consultation use cases. Its AI-assisted matching, targeting, and response-quality layer — PulseAI — is a PulsePanel-native capability that improves both recruitment precision and data quality.

It is the clearest available evidence that the studio's thesis works: a specific piece of academic research, translated into a prescriptive commercial step, built by a founding team formed through the studio, now operating as a going concern with paying customers.

Companies start with free, automated Scope 1 and 2 carbon accounting, generated directly from the spreadsheets they already keep, with AI handling the tedious work of data extraction and classification while Axiobit's own methodology handles the calculation itself. From there, the platform opens into a full suite of services — Scope 3 accounting, Environment, Social and Governance assessments, DNSH screening, EU Taxonomy alignment, Double Materiality, and Sustainable Finance advisory — so a client's ESG journey can deepen without ever having to switch tools or providers.

What sets Y-Carbon apart is the marketplace itself. Every consultative engagement is delivered by a CESGA-certified consultant, matched to the client's sector and need, and approved by Axiobit before work begins — so quality is never left to chance. Every consultant works from the same standardized questionnaires and delivers against the same structured outcome framework, meaning a Double Materiality assessment or a Taxonomy alignment report reads consistently no matter which consultant produced it. And nothing reaches a client unreviewed: every deliverable, whether AI-drafted or consultant-written, passes through a quality gate before publication. This is what makes "audit-ready" a real operating principle rather than a marketing claim.

The result is a platform that behaves less like software and less like a traditional consultancy, and more like a curated professional network with software discipline underneath it. Clients get transparency into every stage of their engagement, a growing library of standardized reports they can trust and compare over time, and the ability to build lasting relationships with the consultants who understand their business. Consultants get a stream of qualified engagements matched to their specialty, a track record built on real client feedback, and a methodology that lets them focus on judgment rather than reinventing structure for every client. Y-Carbon isn't trying to replace ESG expertise with automation — it's trying to make that expertise consistent, accessible, and provable at scale.

This is not a niche problem. Recent research into enterprise generative AI adoption found that the vast majority of pilots deliver no measurable financial return — and the researchers' own explanation was architectural, not technical: most systems can't retain feedback or adapt with use. The same models power the rare successes as the common failures. What separates them is whether learning was ever part of the design.

Tehne starts from a simple definitional claim: a process, plus embedded AI technology, is automation. It only becomes Enterprise AI once a third, compulsory component is present — a learning core capable of absorbing small, high-value, enterprise-specific corrections incrementally, as they happen, without waiting for a batch of data that may never accumulate.

The name comes from technē — the Greek term for applied, embodied know-how, as distinct from purely theoretical knowledge. It's a deliberate choice: Tehne is concerned with the tacit, practical knowledge that lives inside a business's daily work, not abstract intelligence in general.

As a First Chapter Studio venture, Tehne is currently in its research phase — grounding the approach in the literature on few-shot and incremental learning, and validating its core claims empirically, before development of a standalone, licensable platform begins.