AdaSight is the semantic data layer between enterprises and their AI agents — exposing analytics as structured tool calls, typed APIs, and citation-ready context.
$ adasight query --metric=activation --cohort=last_30d
→ MCP tool: analytics.activation_funnel
steps: [signup → first_event → aha_moment]
conversion: 41.2% (↑ 6.1pp WoW)
citation: events.amplitude #2024-Q4 · model: growth_states.v3
The problem
60–80% of time spent cleaning data, not deriving insights
Raw data is messy, inconsistent, requires significant transformation
Existing tools produce visually digestible but semantically shallow outputs
LLMs cannot effectively consume dashboards, charts, or PDF reports
Outputs optimized for visual cognition, not programmatic reasoning
Agents need structured, context-rich, queryable data
The insight
In 2024–2025 we watched companies try to connect GPT-4, Claude, and internal agents to their analytics stacks. The results were poor — not because the models were weak, but because the data wasn't prepared for agent consumption.
Phase II · Agent-ready infrastructure
Native Model Context Protocol server exposing analytics as structured tool calls.
Headless interface for agent orchestration systems.
Data structures designed for agent reasoning.
Technical differentiation
| Capability | Traditional BI | AdaSight Phase II |
|---|---|---|
| Primary consumer | Human analysts | AI agents + humans |
| Output format | Charts, dashboards | Structured JSON, MCP tools, typed APIs |
| Context encoding | Visual cues, legends | Semantic metadata, ontologies |
| Action surface | View-only | Query, transform, validate, act |
| Integration model | Embed / iframe | Protocol-native (MCP, OpenAPI, CLI) |
Traction
100+
engagements shipped
$1M
in sales to date
10×
faster decisions reported
Audited Segment / Amplitude / Braze. Defined growth KPIs. Applied the Duolingo Growth States Model to surface engagement patterns.
"I love how you're correlating data, deriving potential causes, and describing the charts. Immensely helpful."
— Analía Ibargoyen · Head of Product, Tonic
Reactivated dormant Product Analytics. Revalidated events. Turned a quiet tool into a live source of decisions.
"The team will be able to consume the data much more easily and make decisions 10× faster."
— John Ritchie · Head of Product, Seatfrog
Past clients include · Personio · Candis · Notarize · Newscorp
Market opportunity
Phase I TAM
$30B
Business intelligence and analytics market
Phase II TAM
$15–50B
AI agent infrastructure market by 2028
Competitive advantage
Trained on patterns from 100+ growth-team deployments — Tonic, Seatfrog, Candis and many others.
Building agent-native analytics infrastructure before incumbents pivot.
Same semantic layer serves both humans and agents — no separate systems.
Tableau, Looker, Power BI are optimized for human visual cognition. Retrofitting them for agents requires fundamental architecture changes they're unlikely to make.
Snowflake and Databricks provide storage and compute, not semantic preparation. Agents talking directly to data warehouses produce unreliable results.
Team
Co-founder & CEO
Operator, ex-tech partnerships at Booking.com. Owns GTM. Bootstrapped and shipping.
Co-founder & CTO
Former Solution Data Engineer at Amplitude and Optimizely. Consulting clients including Adidas.
15 additional team members from Booking.com · Make.com · Gojek · Sony · Optimizely · Canva · Adidas
Advised by Simon Jackson, PhD (ex-Booking.com, Meta, Canva) and Bruno Pais (Sr. Director AI Products, Sennder)
The ask
The company that owns the data preparation layer between enterprises and their agents will be foundational infrastructure.
Goal 01
Ship MCP + CLI to production
Goal 02
Expand engineering team
Goal 03
10 design partners
Goal 04
Build the category