For Data Teams

Build more, maintain less,
deliver faster.

Redbird automates data collection, transformation, and delivery workflows — so data engineers and data scientists spend less time maintaining plumbing and more time on work that matters.

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The Challenge

Data teams are highly skilled — but chronically over-allocated to low-leverage work

Data team challenge illustration

Bespoke pipelines multiply the maintenance burden

Every custom pipeline built for a business team becomes a maintenance liability. When APIs change, schemas shift, or a source system updates, someone on the data team has to fix it — usually urgently.

Ad hoc requests interrupt deep work

Business teams constantly ask for one-off data pulls, custom extracts, and special analyses that aren't in the data platform. Each request is small, but collectively they consume a significant portion of team capacity.

Business users can't self-serve

Without a layer between the data platform and business users, every data need routes through the data team. Self-service BI helps with structured data already in the warehouse — but new connectors, unstructured ingestion, or multi-source requests still require engineering support.

The Solution

AI that helps data teams quickly automate new data workflows and enable business self-service

Data team solution illustration
01

Automated data collection and ingestion

Connect to APIs, databases, cloud storage, and SaaS platforms through point-and-click RPA automation. Redbird handles data extraction and loading on a configurable schedule — without custom engineering for each source.

02

Workflow-based data transformation

Build transformation logic through a transparent, auditable workflow — not opaque scripts. Business-facing users can understand what's happening; data engineers can maintain and extend it.

03

Self-healing pipeline maintenance

When upstream APIs change, schemas shift, or RPA flows break, Redbird's AI agents detect the issue and automatically repair affected workflow steps — reducing the on-call burden for data engineers.

04

Business user self-service layer

Let business teams pull structured reports, run standard analyses, and access clean data through a conversational interface — without routing every low-stakes request through the data team.

05

SDK and API for engineering integration

Embed Redbird capabilities into your existing data infrastructure using the Redbird SDK. Build custom data products and automated workflows programmatically — so Redbird fits into your stack, not the other way around.

What teams automate with Redbird

Common data team workflows, automated end to end

01

Multi-source data ingestion pipeline

Pull data from multiple APIs and databases on a scheduled basis, apply normalization and cleaning logic, and load structured outputs into your data warehouse — with automatic error detection and repair.

02

RPA-based ETL for data sources without an API

Automate collection and ETL of data from hard-to-reach sources that do not have an API. Redbird's RPA extension lets you record a point-and-click flow and rerun it on a recurring basis.

03

Unstructured data source ETL

Ingest unstructured data sources, extract structured fields from documents and files, and deliver clean structured data on a schedule for use downstream in your warehouse or other pipelines.

04

Data quality monitoring

Run automated quality checks across key datasets — completeness, freshness, range validation — and alert the data team or business owners when anomalies are detected.

05

Ad hoc data extract automation

Convert recurring ad hoc extract requests from business users into automated workflows that run on demand — eliminating the engineering ticket and delivering results directly to the requester.

06

Data product delivery pipeline

Automate the end-to-end process of a data product — from ingestion through transformation to formatted delivery in the stakeholder's preferred format — so it runs without engineering intervention after initial setup.

The Results

What data teams achieve with Redbird

Fewer
Tickets
Recurring ad hoc requests handled automatically, not manually
Lower
Maintenance cost
Self-healing workflows vs. hand-maintained scripts
Faster
Delivery
Time from data request to business-ready output
Higher
Self-service rate
Standard business data requests resolved without engineering involvement
Works with your stack

Connects to your existing data infrastructure — without ripping anything out

Works alongside the tools your team already depends on — and complements the ones you're evaluating.

Snowflake BigQuery Redshift Databricks PostgreSQL MySQL S3 / Cloud Storage dbt Fivetran REST APIs Excel / CSV Kafka + any tool you use

Ready to give your data team back the time they deserve?

Data teams at leading organizations use Redbird to automate difficult pipelines, reduce maintenance overhead, and enable business self-service — without giving up control.

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