80% on the ADE benchmark, driven purely by structured context

Make your data
team feel caught up

The fastest way to deploy agents for any data workflow, backed by a context layer that's always accurate. Go from zero to a working agent in minutes.

Start your free trial Read the docs

SOC 2 Type II20-minute onboardingHuman in the loop

Trusted by

agent console / run #2841

Triggered from Jira DATA-419by @spriha

Reading context31 models, 4 dashboards

Drafting dbt tests12 tests proposed

Awaiting reviewPR #1183 open

Reported back#data-eng in Slack

Agent Console

Run data agents the way you run software.

The Agent Console runs every Sidecar agent against your real stack, with a human in the loop before anything ships. Kick things off from Jira or Slack, and get results back in the same place.

Ships with 50+ job templates, and a studio for building your own
Observe and audit the agent's work in real time: every step and every decision
Get the outcome the job calls for: a PR, a draft, a report — whatever fits
Jira, Linear, and Slack triggers. Sidecar starts where your team already works.

Forward deployed engineer

A dedicated data engineer, included with the platform

Every Sidecar deployment comes with a forward deployed engineer who embeds with your team, owns onboarding, and keeps maintenance work from piling up.

Onboarding & setup. Your engineer scopes your stack, wires up Sidecar, and trains your team, so you're productive within days.
Maintenance off your plate. They handle the unglamorous work: deprecation, test coverage, cost tuning, and documentation, continuously.
Always the same person. No rotating account managers. You get a dedicated data engineer who learns your business and stack.

Included

Your forward deployed engineer

Dedicated · Embedded · Data & analytics engineering

Onboarding

2 to 3 sessions with your team

Touchpoint

Standup or Slack, weekly

Maintenance

Cost, docs, and tests

Billing

In your subscription

Context Garage

Every agent needs context. We built the garage for it.

Context Garage turns your scattered metadata, docs, and tribal knowledge into one versioned context layer. Every Sidecar agent runs on it, and you can serve it to any tool you use.

Most agents guess because they only see fragments of your stack
Sidecar builds a structured, version-controlled model of your warehouse, dbt, lineage, and real usage
Ship it as a live context repo that is always up-to-date

context-garage / main

warehouse.snowflake1,284 tables · synced 2m ago

dbt.models812 models · 94% documented

lineage.graph6,401 edges · v42

usage.querieslast 90 days · 2.1M runs

semantics.metrics78 certified definitions

Bring your own agent. Give it Sidecar's context.

Connect any model or agent framework you already use, and it gets the same structured, always-current understanding of your data platform, with no separate integration to maintain.

Same task, same agent / step 1 of 4

Without Sidecar context

your-agent · raw stack

> audit revenue reporting and add tests before Monday's board deck

search_files("revenue") → 412 matches

read_file("models/legacy/rev_v2.sql")

context window 78% full, still guessing

Tool sprawl and context bloat
Slow, multi-step reasoning
High token and compute cost
Unreliable outputs

With Sidecar context

your-agent · context garage

> audit revenue reporting and add tests before Monday's board deck

garage.resolve("revenue") → 1 certified metric

loaded 3 models, 1 metric def, lineage v42

Deliberately designed tools, tightly scoped context
5x faster reasoning
20x less token usage
Consistently correct

Five agents. One context layer.

Every agent your data team needs, deployed in one place.

Each handles a different part of running your stack, all backed by the same context layer, all reviewable in Agent Console.

Builder Agent

Builder writes, tests, and ships dbt changes for you. Go from ticket to PR in minutes.

Reliability Agent

Stop playing defense with pipeline stability. Reliability watches your stack around the clock, catches breakages early, and tells you exactly what caused them.

Optimization Agent

Watching your warehouse bill climb without knowing why? Optimization keeps cost and performance in check continuously, so nothing creeps up unnoticed.

Custodian Agent

Not sure who can see what, or whether a contract quietly broke? Custodian enforces data contracts, flags sensitive fields, and keeps a record of who accessed what.

Concierge Agent

Tired of fielding ad hoc requests and data questions? Make Concierge the go-to destination for your stakeholders to talk to your organization's data.

Or define your own

Every agent category is programmable. Write the job, Sidecar supplies the context.

Community

What happens when agents actually work for your data team?

25%

Reduction in warehouse costs

60%

Less maintenance work

70%

Faster onboarding

"In this new era, scrappy companies win. That means not hiring expensive employees unless you absolutely need them. With Sidecar, you can maintain a data stack without a data engineer, and that's exactly what we did!"

Kelli Peluso

Data Analytics Manager, Mack Weldon

"Engineers are expensive, yet too often stuck on repetitive but essential data operations. Sidecar changes that by centralizing monitoring and automating solutions, offloading tedious work from engineers. Organizations save costs, strengthen governance and data quality, and free their engineers to focus on what truly drives the business forward."

Jacob White

Former Head of Data Science & Analytics, Airtable

"Sidecar is the first platform that addresses the most painful problems data teams face. Organizing, tracking, resolving, and surfacing data products is the fundamental question facing data teams in Enterprise SaaS, and Sidecar makes it painless. It's the essential tool for data teams, especially those using AI."

Brad Kearney

Former CIO, VP of Data

"As a small data team, Sidecar has been crucial to ensure my team is able to focus on serving the business instead of managing our data platform health."

Lucia Baik

Former Director of Analytics Platform, Zocdoc

"A major challenge at data teams I've led is bringing simplicity to the complexity of our data stack in order to make educated decisions on what to invest in. Sidecar brings that simplicity."

James Mayfield

Former Senior Director of Product, dbt Labs

"Inheriting a data stack almost always means inheriting tech debt. I used to dedicate entire sprint cycles to deprecating unused tables, creating table/field documentation, and adding data test coverage. With Sidecar, we can eliminate that tech debt quickly and keep it from piling up again."

Sam Barger

Director of Analytics, Aurora Solar

Integrations