Describe a pipeline in plain English or wire it visually. An agent builds, tests, and deploys it on your Spark and Kafka.
Self-hosted from $60k/year · evaluate free in our cloud · sold separately from Bonacci Foundry
Sources in, pipelines out. The agent handles the middle.
Plain language in, version-controlled pipeline code out.
Type what you want, or drag nodes on the visual builder. Both stay in sync.
Pipelines compile to Spark and Kafka jobs on the cluster your analysts already query.
Local Ollama or vLLM, including weights we built for you, or a hosted API on your own keys. No lock in, no per seat AI tax.
# generated by the agent, editable by you source postgres_prod { table = "public.orders" mode = "cdc" # change data capture } transform dedupe { by = ["id"] keep = "latest" } sink warehouse { engine = spark to = "analytics.orders" }
One platform instead of a stack of tools.
Go from a sentence to a running pipeline. Chat or drag nodes on the visual canvas, the agent keeps both in sync.
Databases, REST APIs, files, and Kafka streams. 50+ connectors with cross database ETL and CDC out of the box.
Pipelines execute on Spark and DataFusion with Kafka streaming, the same distributed compute your production jobs use.
Named, versioned agents with tool calling and the Model Context Protocol. Pick the right model per agent.
Agents remember your schemas, fixes, and decisions across sessions and workspaces, so they get better as you work.
One click deploy, live execution, quality scoring, and bottleneck detection with context aware fix suggestions.
Studio points at an endpoint you control. No model vendor, no lock-in.
If you commissioned one from Bonacci Foundry, this is where it goes to work: your weights on your vLLM or Ollama, driving the pipelines that supply it. The two are sold separately and neither requires the other.
Llama, Qwen, Mistral, or whatever you already run. Good enough for a great deal of pipeline work, and it costs you nothing but the GPU it sits on.
OpenAI, Anthropic, Gemini, Groq, on your own keys. Not everyone is air-gapped, and Studio does not pretend otherwise. No per-seat AI tax either way.
# the real config. no hidden endpoints. deployment.mode=onprem # inference points at your box, not ours OPENAI_BASE_URL=http://vllm.internal:8000/v1 OPENAI_MODEL=acme_9b # your weights # no billing calls, no licence server razorpay.enabled=false paddle.enabled=false # your directory, your mail, your storage LDAP_ENABLED=true MAIL_HOST=smtp.internal
Cross database moves, change data capture, dedupe, and transforms. Postgres, MySQL, and the warehouse, wired in minutes.
Consume events, transform on the fly, and sink continuously to the warehouse. Backpressure and recovery handled for you.
CSV, Parquet, and JSON files, plus REST endpoints on a schedule. Paste, upload, or let an agent fetch it for you.
Real, interactive walkthroughs that run right in the browser. No signup required.
Connect sources, design on the canvas, run the agent, and ship.
Built in Studio, on the same stack we sell. Public datasets—check the numbers yourself.
Pricing data across 12 providers and three years, analyzed end to end by an agent in Studio.
read the analysis →Four public datasets, three occupation coding schemes, one crosswalk, built in Studio.
read the analysis →25 months of GitHub, arXiv, and Hugging Face activity. The agent wrote and ran the PySpark.
read the analysis →We are onboarding a small group of design partners: teams who use Studio on production work, meet with the founders monthly, and get lifetime free access. Limited seats.
Answered up front, so the review starts from facts instead of a questionnaire. The full security page goes further, including what we do not claim. Send it to your architecture team.
Self-host on an annual licence, or try it in our cloud.
Hosted by us on our infrastructure, with self-serve plans from free.
Self-hosted licences are invoiced annually. No payment processor, no metering, and no billing callbacks run inside your network.
No slides. We describe a pipeline and ship it to Spark while you watch.
Plain English. Source, transform, destination.
Generates the pipeline, runs it against sample data.
Running on Spark and Kafka, rows landing in the warehouse.