you: load transactions from core banking, mask PII, stream to the warehouse agent building...
core_banking
mask_pii()
warehouse
Describe a pipeline in plain English, or wire it up visually. An AI agent builds, tests, and deploys it on your Apache Spark and Kafka, running inside your VPC, your data center, or a fully air-gapped network. No data egress. No third-party LLM in the data path.
Self-hosted from $60k/year · air-gap capable · local inference on your own GPUs
Studio ships as a container you run yourself. It turns plain language into real, version controlled pipeline code, and every byte of that process stays inside your perimeter.
A Docker Compose stack in your VPC, your data center, or an isolated network. No callbacks, no usage telemetry, no licence server. Keys validate offline against a signature.
Point it at Ollama or vLLM running on your hardware. Prompts, schemas, and sample rows never reach a third-party model.
Pipelines compile to Spark and Kafka jobs on the infrastructure you already run. Production, not a sandbox.
# the real config. no hidden endpoints. deployment.mode=onprem # inference points at your box, not ours OPENAI_BASE_URL=http://localhost:11434/v1 # ollama # 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
The big platforms are SaaS by design: your schemas, your samples, and your prompts cross their boundary. We built the opposite, then made the agents good enough that you don’t pay for the privacy.
Local inference by default, offline licence validation, your own SMTP and object storage. Nothing in the install path requires a call to us, so your firewall rules, not our promises, are the control.
Run Ollama or vLLM on your own GPUs. Or bring your own API keys if you permit egress. Either way, no per-seat AI tax and no vendor deciding your model.
Docker Compose into a VPC, a private data center, or an isolated network. One compose file, one install script, images you can mirror into your own registry.
Before any irreversible action, a forward check simulates the result. Destructive commands are rejected, even in full-auto mode.
N agents race the same task in isolated git worktrees. You review the diffs and merge the winner. Git is your undo.
A ThinkingMemory backbone recalls schemas, decisions, and outcomes into every run, so agents build on what they already know.
If your answer to “can we send this schema to a hosted model?” is no, the mainstream AI data tools are closed to you. This is the one built for that constraint.
Classified and controlled-unclassified networks where air-gap is a hard requirement and offline delivery is the only install path.
Core banking, transaction, and PII data under supervisory rules that forbid third-party model processing.
Patient records and trial data where the boundary is the compliance control, not the encryption.
Energy, utilities, and industrial operators running OT networks that are isolated from the internet by design.
Studio is the platform you deploy. Underneath it sits infrastructure we wrote ourselves. A memory database, a compiled language, and a terminal agent, two of them open source. If you are betting a regulated deployment on a vendor, this is the depth behind it.
Parallel agent fleets and persistent memory for coding and data engineering, in your terminal. Free forever, bring your own keys, and it ships finished pipelines straight into Studio.
A memory database for agents. Intent goes in, the right context comes out, packed to a token budget.
A typed, compiled language where tables, streams, and models are native types.
Real analyses, built and executed by agents in Bonacci Studio, published with the data.
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 →No slides, no recording. We run Studio against a local Ollama model with the machine off the internet, describe a pipeline in plain language, and ship it to Spark while you watch.
Your network shape, your compliance boundary, your existing Spark and Kafka.
Studio running air-gapped against a local model, building a pipeline on your kind of data.
Compose stack into your environment. Your security team reviews the images and config before anything runs.