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toby mather

T2 · manager / operator

Serial entrepreneur building Rig, an AI data infrastructure platform; previously founded Lingumi (language learning for children), scaled to 2M users and exited to Novakid; led product and data at Novakid; second-time founder focused on capital-efficient, AI-native company building.

1 call·1 name·100% bull·last heard 3 months ago·Scaling Europe
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no scored calls yet — needs a stated position or a categorical verdict, with a matured window vs SPY

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$RIGRigposition

Rig raises $2.8M pre-seed to build AI data infrastructure layer for internal enterprise data

Rig provides a context layer that connects heterogeneous internal data sources (SaaS tools, warehouses) so AI agents and custom apps can act on unified enterprise data without wrangling 15+ APIs; early traction with 10+ design partners including Cleo and BirdieCare, 100% MoM revenue growth on minimal spend.

Scaling Europe2026-07episode →

most discussed · click a bar to filter

  • $RIG

recurring themes

  • AI Infrastructure1
  • AI Talent & Labor Market1
  • Developer Tools1
  • AI Economics & Business Models1
  • Enterprise AI Adoption1
1 total
$RIG
Rig
HIGHtoby mather·Scaling Europe·3 months ago·Rig raised $2.8m Pre-seed: Toby Mather, Co-Founder of Rig· position
Rig raises $2.8M pre-seed to build AI data infrastructure layer for internal enterprise data
Rig provides a context layer that connects heterogeneous internal data sources (SaaS tools, warehouses) so AI agents and custom apps can act on unified enterprise data without wrangling 15+ APIs; early traction with 10+ design partners including Cleo and BirdieCare, 100% MoM revenue growth on minimal spend.
"Rig is building the underlying tools and building blocks that let people use all their internal data with AI. So, that's basically infrastructure uh building blocks to ingest data…"
0:49
8
AI Infrastructuretailwind
Context layer emerges as critical infrastructure for enterprise AI adoption
The bottleneck for enterprise AI is not model capability but connecting heterogeneous internal data (SaaS, warehouses, unstructured) into a unified context layer that agents can dynamically query; Rig's approach sits atop existing warehouses as a 'data brain' enabling secure, governed data access for AI workflows.
8
AI Talent & Labor Markettailwind
AI-native hiring favors steep learning curves over experience; career compression accelerates
Founders should hire for learning velocity, endurance, and context-switching capacity rather than years of experience; 21-23 year olds living in AI tools can reach senior technical skill levels in months, with career progression now gated by decision-making and interpersonal skills, not technical knowledge acquisition.
7
Developer Toolsrisk
Vibe coding internal tools is a TCO trap; build only thin UIs on standard primitives
AI-generated code makes building internal tools tempting, but total cost of ownership (security, scaling, dependencies, upgrades) is underestimated; companies should vibe-code only custom UIs atop battle-tested primitives (Supabase, data warehouses) and buy the underlying infrastructure.
7
AI Economics & Business Modelstailwind
Capital efficiency before scale: stay lean until go-to-market motion is fully repeatable
In the AI era, pre-seed startups can achieve 100% MoM revenue growth on near-zero burn ($500) by keeping teams tiny, solving design partner problems deeply, and deferring sales hiring until the onboarding-to-value loop is proven repeatable across customers.
7
Enterprise AI Adoptiontailwind
Horizontal AI data platforms sell to AI-forward non-technical operators, not traditional buyers
Horizontal tools like Rig, Zapier, or Airtable lack a single ICP; buyers are AI-obsessed non-technical leaders (CEOs, CROs, RevOps) who want to rip out legacy workflows, build a data foundation, and automate heterogeneous processes — adoption spreads virally across functions once seeded.