flagship platform

$ pipeline.build --100x-faster --85%-cheaper

Build Data Pipelines 100x Faster & 85% Cheaper. A visual data pipeline platform powered by AI agents — build, deploy, and manage complex data workflows with drag-and-drop simplicity. From ideation to production in minutes.

▸ get started free schedule demo

security first 99.9% uptime 24/7 support

10K+

pipelines

99.9%

uptime SLA

50+

integrations

1M+

rows/sec

Built on battle-tested infrastructure

Enterprise-grade open-source technologies trusted by thousands of companies worldwide.

Apache Spark Apache Kafka Apache Camel Spring Boot PostgreSQL Multi-Model AI | OpenAI Anthropic Gemini Ollama Groq HuggingFace OpenRouter + Custom Endpoints

one platform, infinite possibilities

Everything you need, nothing you don't

Stop juggling multiple tools. One unified platform for all your data pipeline needs.

01

AI Pipeline Builder

Describe your pipeline in plain English. AI creates production-ready flows with auto-fix suggestions and performance optimization.
02

Database Pipelines

Apache Spark-powered with 20+ transformation nodes. Connect to PostgreSQL, MySQL, Snowflake, BigQuery and more.
03

API Integration

REST APIs, webhooks, OAuth, JWT authentication. Apache Camel for robust enterprise microservices.
04

File Processing

CSV, JSON, XML, Excel, Parquet. Smart engine selection for optimal performance across formats.
05

Real-time Streaming

Apache Kafka integration with sub-millisecond latency for live data streams and event processing.
06

ML Integration

Train, score, and manage ML models directly within your pipelines. PySpark ML and custom models built in.

Prompt · Memory · Language

Three layers, one platform — the agentic core that powers every pipeline you build in Bonacci Studio.

agentic ai

AI agents plugged into your entire data stack

AI agents that plug into your entire data stack — databases via JDBC, streaming through Kafka, extensibility via MCP. Run PySpark jobs, execute SQL queries, schedule pipelines, and orchestrate workflows — all from a single prompt.

you ›

▸ planning — needs tables customers + orders

▸ warehouse SELECT count(*), avg(o.amount) FROM customers c JOIN orders o USING (customer_id) WHERE c.created_at >= date_trunc('month', now())

✓ queried warehouse · 2 tables · 1,284 rows scanned · 0.4s

3,412 new active customers signed up this month — average order value $87.40, up 12% vs last month.

metric	value
new_active_customers	3,412
avg_order_value	$87.40
mom_change	▲ 12%

you ›

▸ codegen — writing PySpark against connection warehouse

# generated · etl_churn.py from pyspark.sql.functions import col, datediff, current_date, when df = (spark.read.format("jdbc") .option("url", conn["warehouse"]) .option("dbtable", "customers").load()) result = df.withColumn("churn_risk", when(datediff(current_date(), col("last_login")) > 90, "high") .when(datediff(current_date(), col("last_login")) > 30, "medium") .otherwise("low")) result.write.mode("overwrite").parquet("/data/churn_analysis")

executes on the Bonacci Spark engine

INFO submitting job to spark engine…

INFO reading warehouse.customers · 2.4M rows

INFO processing batch 47/50…

✓ wrote /data/churn_analysis · 2.4M rows · 38s

// ThinkingLanguage — query, transform, enrich with AI inline (customer_analysis.tl) let customers = postgres("warehouse", "customers") |> filter(status == "active") |> join(postgres("warehouse", "orders"), on: "customer_id") |> aggregate(total_spend = sum(amount), by: customer_id) // AI-powered enrichment — inline LLM call let enriched = customers |> add_column("segment", ai_complete("Classify as VIP/Regular/At-Risk: spend=${total_spend}")) // Write results + display enriched |> write_parquet("s3://lake/customer_segments.parquet") |> show()

six capabilities

01

AI Agent with Tool Calling

LLM-driven tool-calling loop with database, SSH, MCP, and code generation tools. The agent takes real action — not just suggestions.
02

Multi-Model BYOK

Connect any LLM provider — OpenAI, Claude, Gemini, Ollama, Groq, and more. Each user configures their own API keys and model preferences.
03

Model Context Protocol

Native MCP client for infinite extensibility. Connect any MCP-compatible server — the agent uses its tools seamlessly alongside built-in ones.
04

PySpark Execution Engine

Production-grade PySpark runtime with auto-dependency installation, credential isolation, and real-time log streaming via WebSocket.
05

Cross-Database ETL

Multi-database operations spanning PostgreSQL, MySQL, SQLite, DuckDB, Redshift, MSSQL, Snowflake, BigQuery, Databricks, ClickHouse, and MongoDB — read, transform, and write across systems in one pipeline.
06

Smart Intent Routing

AGENT / CODEGEN / SMART classification routes each request to the optimal execution path — no wasted tokens, no unnecessary tool calls.

persistent memory

The Thinking Prompt agent is backed by ThinkingMemory — a layered memory architecture that gives it persistent context across sessions. No repeated explanations, no cold starts. The agent remembers your data stack, past designs, and learns from every interaction.

01

short-term · context-aware

Working Memory

Holds your current session context — the active pipeline design, connected databases, in-progress queries, and ongoing conversation state. Cleared when the task completes.
02

event-based · temporal

Episodic Memory

Recalls past interactions — previous pipeline builds, debugging sessions, optimization decisions, and how issues were resolved. The agent learns from your history.
03

knowledge · concepts

Semantic Memory

Stores your data knowledge — schemas, table relationships, column naming conventions, team preferences, and domain-specific context. The agent knows your stack.
04

skills · procedures

Procedural Memory

Retains learned patterns — ETL templates, pipeline recipes, orchestration workflows, and best practices from your org. The agent gets better at building what your team builds.

developed by thinkingdbx

ThinkingLanguage

We built our own programming language for data engineering. ThinkingLanguage combines Apache DataFusion with a clean, expressive syntax — letting you query databases, transform files, orchestrate AI agents, connect to the entire MCP ecosystem, and deploy pipelines in seconds, not hours.

▸ launch TL ↗ ▸ github read more →

01

Query Any Database

Connect to PostgreSQL, MySQL, SQLite, DuckDB, Redshift, MSSQL, Snowflake, BigQuery, Databricks, ClickHouse, MongoDB, Redis and more using named connections. Write postgres("src", "employees") and credentials resolve automatically from your Connection Bridge.
02

Apache DataFusion Engine

Process billions of rows in-memory with columnar Arrow execution. Filter, aggregate, join, and transform massive datasets with familiar SQL-like operations and functional pipes.
03

AI Agent Scripting

Call LLMs inline with ai_complete(). Build AI-powered data pipelines that classify, extract, summarize, or generate — all within the same script that queries your data.
04

MCP Ecosystem

Full Model Context Protocol support — both client and server. Connect to any MCP server with mcp_connect(), or expose TL functions to Claude Desktop, Cursor, and Windsurf with mcp_serve(). Agents auto-discover MCP tools alongside native ones — one unified tool list, dispatched transparently.
05

First-Class File Support

Read and write CSV, Parquet, and JSON directly. Transfer files securely via built-in SFTP/SCP connectors. Cloud files are automatically resolved and downloaded — work with read_csv("sales.csv") as if every file is local.
06

Live Execution & Deploy

Real-time WebSocket-streamed output — see results as they happen, cancel mid-flight. Go from prototype to production with tl deploy. Docker, Kubernetes, and interactive REPL built in.

Connection Bridge — Zero Config. All your platform connections — databases, APIs, MCP servers, AI providers — are automatically available to every ThinkingLanguage script. No hardcoded credentials, no config files. Just write your logic and the platform handles the rest.

Autonomous & Managed Agents

Two patterns, one identity model — an always-on watcher for your stack, and named workers you invoke on demand.

autonomous ai agent

Connect & forget — it watches your stack 24/7

An autonomous AI agent that lives inside your data platform. It doesn't just answer questions — it remembers context, takes action, and watches your infrastructure around the clock. Memory powered by ThinkingMemory.

┌────────────────────┐ │ AGENT BRAIN │ │ (multi-step plan) │ └─────────┬──────────┘ ┌───────────────────┼───────────────────┐ ↓ ↓ ↓ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │ Memory Engine │ │ Tool Layer │ │ Heartbeat Mon. │ │ ThinkingMemory │ │ SQL · SSH · │ │ 24/7 watch │ │ schemas, prefs, │ │ APIs · MCP │ │ failures, drift │ │ past designs │ │ │ │ │ └─────────────────┘ └─────────────────┘ └────────┬────────┘ ↓ ┌────────────────────────┐ │ Smart Notifications │ │ Slack · Discord · Teams│ │ Email · PagerDuty · WH │ └────────────────────────┘

01

powered by thinkingmemory

Thinking Memory

Persists knowledge across sessions — schemas, query patterns, team preferences. The agent gets smarter over time, powered by the ThinkingMemory architecture.
02

autonomous actions

Tool Execution

Runs SQL queries, connects via SSH, calls external APIs, and integrates with MCP servers — autonomously.
03

multi-step reasoning

Agentic Mode

Multi-step reasoning with automatic tool selection. Describe what you need; the agent figures out how.
04

24/7 monitoring

Proactive Heartbeat

24/7 background monitoring — pipeline failures, connection health, schema drift — detected before you notice.
05

event-driven routing

Smart Notifications

Routes alerts to Slack, Discord, Teams, Email, PagerDuty, Google Chat, or any webhook — filtered by event type and severity.

How it works — Connect & Forget. You connect your databases and pipelines. The Autonomous AI Agent learns your environment using ThinkingMemory, monitors it continuously, and alerts you the moment something needs attention — through whatever channel you prefer.

managed data agents

Reusable AI workers for your data stack

Configure once. Call from the UI, your pipelines, your cron jobs, or any external system — with memory, permissions, and a full audit trail. Use them the way you'd use a function, not a conversation.

┌────────────────────────┐ │ MANAGED AGENT │ │ name · version · prompt │ │ roles · skills · model │ └───────────┬────────────┘ ┌──────────────────────┼──────────────────────┐ ↓ ↓ ↓ ┌───────────────┐ ┌───────────────┐ ┌───────────────┐ │ Studio UI │ │ Pipeline Step │ │ API Tokens │ │ ad-hoc run │ │ ETL · AGENT │ │ cron · CI · WH│ └───────────────┘ └───────────────┘ └───────────────┘ ↓ ↓ ↓ └──────────────────────┼──────────────────────┘ ↓ ┌────────────────────────────┐ │ Session archive + memory │ │ inputs · tools · outcomes │ └────────────────────────────┘

01

define once, reuse everywhere

Named, versioned agents

Give an agent a name, a system prompt, and declare the connections it needs (database, Kafka, SFTP, API, MCP) as named roles. Teammates plug in their own connections at invoke time — same agent, different data.
02

no provider lock-in

Pick the right model per agent

OpenAI, Claude, or your own self-hosted Llama — chosen per agent. Different agents on different models, all in one platform.
03

six skills built-in

Data engineering skills out of the box

schema-inspector, row-count-check, null-audit, data-profiler, pyspark-table-profiler, pyspark-dedup-check. Write your own in ThinkingLanguage or PySpark and assign them per agent.
04

learns from every run

Four-layer memory

Working, episodic, semantic, procedural. A nightly job distills past outcomes into patterns the agent applies on its next run — no fine-tuning required.
05

three surfaces, one identity

Invoke from anywhere

Run from the Studio UI, drop into any pipeline as an AGENT step, or call via scoped API tokens from cron, CI/CD, and webhooks. Same memory, same audit trail.
06

A2A protocol

Agent-to-agent interoperable

Every public agent exposes an Agent Card at a well-known URL, compliant with the Linux Foundation's A2A protocol. Other AI systems can discover and call your agents directly.
07

auditable by default

Full session history

Every invocation records input, output, tool calls, role bindings, duration, and status. Search past sessions, replay outcomes, or feed them back into the agent's memory.

$ orders-watchdog.agent.yaml

# Defined once. Versioned. Invoked from UI, pipeline, or API. name: orders-watchdog version: "1.4.0" model: anthropic/claude-opus-4-7 # or openai, ollama, self-hosted roles: # named connections — bound at invoke time - name: warehouse type: postgres - name: alerts type: webhook skills: - schema-inspector - null-audit - row-count-check - pyspark-table-profiler prompt: | Investigate the orders table on {{warehouse}}. Compare row counts and null rates to last 7-day baseline. Flag anomalies > 3σ via {{alerts}}. memory: layers: [working, episodic, semantic, procedural] nightly_distill: true expose: a2a: true # auto-publish A2A Agent Card api_token: true # mint scoped tokens for cron/CI/webhooks

Example — orders-watchdog. Bound to the warehouse connection. Loaded with the four data-quality skills. Prompt: "Investigate the orders table, compare to baselines, flag anomalies." Run it ad-hoc from the UI, drop it into your nightly ETL, or mint an API token and call it hourly from cron. One agent. Three callers. Same memory, same outcomes archive, same audit trail.

Mallesh Madapathi on LinkedIn · #managedagents #ai #data

↗

"Reusable AI workers for your data stack — define once, invoke from the UI, your pipelines, or any cron job. Memory, permissions, and a full audit trail included."

Read the full post on LinkedIn →

ai-powered workflow

AI at every step of the lifecycle

From design to deployment, AI agents assist at every stage of your data pipeline lifecycle.

┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ DESIGN │ → │ DEBUG │ → │ GUARD │ → │ OPTIMIZE │ └──────────┘ └──────────┘ └──────────┘ └──────────┘ NL → pipeline instant error continuous bottleneck describe it analysis & quality detection & fix suggest scoring fix suggest

01

design

Natural language to pipeline

Just describe what you need — the AI builds, validates, and prepares the pipeline.
02

debug

Context-aware fix suggestions

Instant error analysis with fixes that understand your pipeline shape, schemas, and dependencies.
03

guard

Continuous quality scoring

Catches issues before deploy — schema mismatches, data quality regressions, performance anti-patterns.
04

optimize

Bottleneck detection

Detects bottlenecks and anti-patterns, suggests fixes — and applies them if you approve.

see the difference

Why teams choose Bonacci Studio

feature	Bonacci	Traditional
AI-Powered Pipeline Generation Describe pipelines in plain English — AI builds, validates, and deploys them automatically.	✓ Built-in	✗ None
Visual Drag-and-Drop Full visual canvas with drag-and-drop nodes — no code unless you want it.	✓ Full Canvas	Code Only
Unified Platform ETL, streaming, orchestration, monitoring, and AI — all in one place. No tool sprawl.	✓ All-in-One	Multiple Tools
Real-time Collaboration Multiple team members can edit, review, and deploy pipelines simultaneously.	✓ Live	✗ None
Time to First Pipeline Go from zero to a production pipeline in 30 minutes — not weeks of config and DevOps.	30 min	2–3 weeks
Cost per Pipeline 85% lower cost than enterprise alternatives — no per-connector or per-row pricing traps.	85% Lower	Higher TCO

Ready to build pipelines 100x faster?

Start free — no credit card. Or book a 30-min demo and see the agent build a pipeline live.

▸ get started free schedule demo

enterprise-grade security

Built with security-first principles

Enterprise-grade encryption and access controls across every layer of our platform.

01
Modern Cryptography

Industry-leading encryption standards.
- TLS 1.3 encryption
- AES-256 data encryption
- bcrypt & SCRAM-SHA-256
02
Authentication Excellence

Multi-layer authentication security.
- JWT tokens
- Multi-factor authentication
- Password breach checking
03
Input Validation

Comprehensive attack prevention.
- SQL injection protection
- XSS prevention
- Parameterized queries
04
Session Security

Secure session management.
- HttpOnly cookies
- SameSite protection
- CORS configuration
05

Security Headers & Monitoring

All critical headers properly configured. Integrated security dashboard for monitoring security events. Professional-grade security patterns throughout the codebase.
06

Compliance Ready

GDPR, SOC 2, NIST standards.

built for enterprise

SOC 2 In Progress GDPR — EU Compliant NIST Framework TLS 1.3 DPIIT Certified

✓ We're a DPIIT — Startup India Certified Startup.
✓ SOC 2 Type II certification in progress.

see it in action

Pipeline building made easy

From idea to production in under 30 minutes. Minimal code required.

⚡

Lightning Fast

100x faster pipelines.
↻

Zero Learning Curve

Master in minutes.
✓

Production Ready

Enterprise infrastructure.

▸ follow on linkedin →

unbeatable value

Cost-effective data pipeline solution

Bonacci costs 85% less than big players. Get enterprise-grade data pipelines without the enterprise price tag. No per-row pricing, no per-connector fees, no hidden infrastructure costs. Bonacci's unified architecture eliminates the tool sprawl that drives up traditional platform costs.

no per-row pricing no per-connector fees no hidden infrastructure costs

cost per 1 billion rows / month

Fivetran

$120,000

Informatica

$75,000+

Airbyte

$50K+

Matillion

$40K+

Bonacci

~$300

85%

lower cost · vs enterprise

100x

faster development

5,000

pipelines · same budget as 1 Databricks pipeline

developer edition pricing

Choose your perfect plan

Start free, scale as you grow. No hidden fees, no surprises.

Pricing shown is for Developer Edition. Enterprise plans vary based on requirements.
New users get a 21-day Pro trial free!
Contact us for pricing information and custom enterprise solutions.

flexible deployment

Deploy your way

Choose the deployment option that fits your business needs.

option a

Cloud

Our Developer Edition is cloud-based and ready to use. Get started in minutes with no infrastructure setup.

Instant setup, no installation required
Automatic updates and maintenance
99.9% uptime SLA
Scalable infrastructure
24/7 support

▸ get started free

option b

On-Premise & Enterprise

Custom-tailored solutions for enterprises with specific security, compliance, and deployment requirements.

Deploy in your own infrastructure
Full control over data and security
Custom integrations and features
Dedicated support team
White-label options available

contact sales

Enterprise pricing may vary according to requirements and package.

ready to ship?

Transform your data workflows

Join the future of data pipeline development. Start building in minutes, not weeks.

▸ get started free schedule demo

— from idea to production in 30 min ✎

$ pipeline.build --100x-faster --85%-cheaper

AI Pipeline Builder

Database Pipelines

API Integration

File Processing

Real-time Streaming

ML Integration

AI Agent with Tool Calling

Multi-Model BYOK

Model Context Protocol

PySpark Execution Engine

Cross-Database ETL

Smart Intent Routing

Working Memory

Episodic Memory

Semantic Memory

Procedural Memory

Query Any Database

Apache DataFusion Engine

AI Agent Scripting

MCP Ecosystem

First-Class File Support

Live Execution & Deploy

Thinking Memory

Tool Execution

Agentic Mode

Proactive Heartbeat

Smart Notifications

Named, versioned agents

Pick the right model per agent

Data engineering skills out of the box

Four-layer memory

Invoke from anywhere

Agent-to-agent interoperable

Full session history

Natural language to pipeline

Context-aware fix suggestions

Continuous quality scoring

Bottleneck detection

Ready to build pipelines 100x faster?

Modern Cryptography

Authentication Excellence

Input Validation

Session Security

Security Headers & Monitoring

Compliance Ready

Lightning Fast

Zero Learning Curve

Production Ready

Cloud

On-Premise & Enterprise

Transform your data workflows