AI Automation
Pipelines that keep your data and workflows moving
DBAI designs and operates the automation pipelines behind your data and workflows: a trigger fires, data moves and normalizes, exceptions route to a person or an agent, and every run is logged.
Automation vs. agents
Not the same job
Automation runs a defined pipeline: a trigger fires, steps execute in order, and the result lands where it belongs. It doesn't interpret judgment calls — it routes them. An agent owns a process end to end and decides what to do next when the situation isn't defined. Most businesses need automation first, with the exceptions it can't resolve routed to a person, or to an agent when judgment is genuinely required.
Who it's for
Operations where the systems already exist but don't talk to each other: a supplier feed that has to reach a storefront, a dialer's records that have to reach a dashboard, a status that has to update in three places at once.
The problem
Manual data entry and cross-referencing between systems that don't talk to each other — catalog updates, record syncs, status changes — done by hand, on a schedule that depends on someone remembering to run it.
What we build
What the pipeline does
- Triggered pipelines
- Event- or schedule-based runs connecting the systems you already use — no new platform to adopt.
- Data ingestion and normalization
- Pulls from source feeds — SFTP, APIs, webhooks — and maps records into the format your systems of record expect.
- Approval and exception routing
- Work that doesn't fit the rule stops for a person to review, or escalates to an agent, instead of failing silently.
- Monitoring and failure recovery
- Error logging, retries and alerting, so a failed run gets caught and fixed instead of going unnoticed.
- Model-assisted decisions inside the pipeline
- A bounded classification, extraction or scoring step feeds back into a deterministic flow — not open-ended autonomy.
How it works
One pipeline, start to finish
TRIGGER
An event, a schedule, or an inbound record starts the run.
INGEST
Source data is pulled from the feed, API or webhook.
NORMALIZE
Records are mapped and cleaned into your systems' formats.
ROUTE
Work that matches the rule continues; exceptions go to a person or an agent.
SYNC
Clean output writes back to the systems of record.
MONITOR
Every run is logged; failures alert and retry.
Implementation
Three steps to a running pipeline
- 01
MAP THE PIPELINE
Identify every system the workflow touches, where the data originates, and where a decision currently requires a person.
- 02
BUILD AND TEST
The pipeline runs against real data in a staging environment before it ever touches production.
- 03
OPERATE
We monitor runs, handle exceptions, and maintain the pipeline as your source systems change.
Real proof
A pipeline running in production
Related
Bring us the pipeline nobody wants to babysit.
We'll show you where it breaks today and what running it clean looks like.
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