ThinkingDBx is building the Bonacci family of agentic platforms, a vertical AI model from scratch, our own pipeline language, and memory infrastructure for AI agents.
Modern data teams stitch together five to ten point products to move data, transform it, observe it, and prepare it for AI. The result is brittle, expensive, and incompatible with how autonomous systems actually work.
Fivetran, dbt, Airflow, Monte Carlo, plus a vector DB bolted on for AI. Each one a contract, a console, and a different mental model.
Mid-sized data teams spend $200K to $2M annually on pipeline tooling alone. Per-row, per-connector, per-seat. It compounds.
Every tool assumes a human in the loop. An agent cannot reason across the seams. Memory is missing. Lineage is opaque.
The window to define this category is the next 18 months. Incumbents are architected for the pre-agent era and cannot retrofit.
We collapse the modern data stack into a single agentic platform, powered by a vertical model we are training ourselves, expressed in a language we designed for data pipelines, with memory built natively for agents.
Agentic data engineering platform. Visual + AI chat.
Pure agentic DE/DS. No canvas, just intent.
Vertical model, built from scratch for DE/DS.
Compiled language for pipelines. Apache DataFusion.
Agent-native memory. Four-layer architecture.
Bonacci Studio runs on ThinkingLanguage. Bonacci Flow reasons via the Bonacci model. Both products use ThinkingMemory. No incumbent has all five. No single-product AI startup has the surface area.
Agentic data engineering platform. Pipelines built visually or through AI chat across JDBC, Kafka, MCP, PySpark, and SQL, orchestrated by autonomous agents.
A pure agentic data engineering and data science platform. No drag-and-drop. No canvas. Just intent. Agents plan, build, train, and ship pipelines and models end-to-end.
The Flow runtime depends on Bonacci MoE 9B for deep reasoning over data engineering primitives. We are building them in lockstep, with first GA targeted within the funded runway.
Our own model, built from scratch for data engineering and data science. Not a fine-tune. Not a wrapper. A vertical foundation model with first-class understanding of ThinkingLanguage, Spark, SQL, and pipeline execution semantics.
Mixture-of-experts. Built for vertical depth in DE/DS reasoning.
General corpus + FineWeb-Edu. TPU v6e-16.
15K curated DE/DS pairs. Underway.
Released on HuggingFace, embedded in Bonacci.
Why a vertical model? Horizontal LLMs cannot reason over Spark execution plans, schema lineage, or pipeline failure modes. A model trained specifically on this domain produces deterministic, shippable code from intent.
The first compiled language where data pipelines, ML, and streaming are first-class primitives. Built on Apache DataFusion. Lets agents and humans express end-to-end pipelines with deterministic execution semantics.
Agent-agnostic memory infrastructure. Working, Episodic, Semantic, and Procedural memory layers, built for AI agents rather than retrofitted from vector search. Auto-compresses, forgets, and consolidates.
Data infrastructure spend is large, growing, and entering a generational transition. The incumbents won the last era. They cannot win this one.
The shift is not optional. Every enterprise CTO has approved AI budget. Almost none can deploy because their data layer is the bottleneck. We sell the answer.
Incumbents lack the agent layer. AI-startup bolt-ons lack the data infrastructure. Vertical model players lack the platform. We ship all five layers in one coherent stack.
| Platform | Vertical Model | Pipeline Language | Agent Memory | Cost Position | |
|---|---|---|---|---|---|
| Fivetran / Informatica / Talend | Partial | No | No | No | Expensive |
| Databricks / Snowflake | Partial | No | No | No | Expensive |
| AI startup bolt-ons | No | No | No | Partial | Mid |
| Horizontal LLM providers | No | No | No | No | Variable |
| ThinkingDBx | Yes | Yes | Yes | Yes | 85% cheaper |
Pre-revenue and bootstrapped to date. Every layer is real and shipping; we are entering the GTM and capital-raise phase to scale what is already working.
In production. Onboarding first enterprise design partners.
Released. Developer community building. Signal for global developer reach.
Live, compiled. Powers Bonacci Studio in production.
Pre-training complete on TPU v6e-16. SFT + GRPO post-training underway.
What seed capital unlocks: Bonacci Flow GA, Bonacci MoE 9B weights release, first 20 enterprise design partners, hardened enterprise Studio, and the GTM motion to scale them.
Senior Data Engineer and Data Scientist with 7+ years of experience across different domains of IT, and an MSc in Computer Science, AI & ML.
Targeting Bonacci Flow GA, Bonacci MoE 9B weights release, hardened enterprise Studio, and the first cohort of enterprise design partners.
We are raising our seed round. Co-leads welcome. Strategic angels with data infrastructure, AI/ML, or developer-tools expertise especially valued.