Transformations for
data teams

Pipeline failures still start with a 2 a.m. page and a blank alert. Data Workers diagnoses the root cause on its own, proposes the fix with its blast radius and the way back, and runs it on your orchestrator once a named person approves.

Data quality work piles up in hand-written tests, noisy alerts and SLAs nobody reports on. Data Workers agents write and maintain the tests, learn what normal looks like for each table, catch anomalies before consumers see them and track an SLA for every data product.

Upstream teams rename columns, widen types and drop fields without telling anyone, and the break shows up in a dashboard on Monday. Data Workers catches each change, traces every consumer it touches, and proposes a compatible migration with a rollback plan for a person to approve.

Pipeline requests wait in a queue while engineers rebuild the same boilerplate, and tests get written last, if at all. Data Workers builds pipelines from a plain-language request, tests them before deploy and ships them through review and approval.

Streaming breaks quietly: a consumer falls behind, a producer changes a schema, and tables go stale before anyone notices. Data Workers watches lag, topics and connectors, keeps schemas checked and registered, and puts streaming flows in the same lineage as batch.

Every new data source starts as a ticket, then weeks of access requests, profiling, documentation and tests before anyone trusts it. Data Workers connects the source read-only, profiles it, documents it and writes its tests, with a named approver before anything goes live.

Every new pipeline adds another owner to find, another alert to read and another change to track. Data Workers keeps agents watching the whole estate, lets each domain choose how much they do on their own, and shows every change in one place. Scale the estate without scaling the on-call rota.

Every data access request becomes a ticket, a wait and a guess about who should approve. Data Workers answers requests against your policy, finds the least access that does the job, routes it to the right owner, and keeps an approval record for every grant.

Naming conventions, ownership rules and data contracts live in a wiki page and get checked in review, when someone remembers. Data Workers lets you write each standard once, checks every change against it and turns every gap into a fix for a named owner to approve.

Sensitive data spreads faster than anyone can inventory it, and masking decisions wait on a spreadsheet. Data Workers agents find and tag personal data in the tables you point them at, then propose a masking decision for each sensitive column for a person to approve.

Classification projects label the estate once, then drift as every new table and column lands unlabelled. Data Workers finds sensitive data, proposes tags as data arrives, sends each one to a named approver and carries the label into access and lineage, so the estate stays classified.

Every audit season, data and finance teams stop work to screenshot approvals, export access lists and rebuild who changed what. Data Workers keeps a tamper-evident audit trail as the work happens, so SOX, SOC 2 and internal audit evidence is compiled on request, not reconstructed.

Coding agents, copilots and data agents now reach your warehouse, and each one arrives with its own permissions and no shared record. Data Workers puts every agent that touches data, ours or yours, under the same permissions, approvals and tamper-evident audit trail.

Putting AI agents on regulated data raises two questions fast: where does the data and every model call run, and what record exists of what the agents did? Data Workers runs in your own cloud, region or premises, on models you choose, and keeps the records that EU AI Act and GDPR reviews ask for.

Catalog documentation is written in a burst, then falls behind the warehouse. Data Workers drafts a page for every table, model and dashboard, keeps it current as the data changes, flags missing owners and stale pages, and lists dead assets, with a named person approving.

Lineage drawn once goes stale with the next release, and it stops at the edge of each tool. Data Workers joins the lineage your tools already hold, follows streams and trained models, and keeps it current, so "what breaks if I change this?" is answered before the change ships.

You already pay for a catalog that holds your owners, glossary and policies, and agents now work outside it. Data Workers keeps the catalog you have, reads it, adds the context it lacks and sends every agent change to a named owner for approval.

Asking for data work, approving it and checking what the agents did usually takes five tools and a ticket queue. Data Workers puts the ask, the approval and the record in one conversation, in Slack or the browser, with a named approver on every change. The chat box is in preview.

Warehouse and compute spend grows faster than usage because nobody can prove what is safe to remove. Data Workers runs a weekly cost review across Snowflake, Databricks and BigQuery, checks every dependency first, and sends each cleanup to a named owner to approve.

Agent token spend climbs when every question starts from scratch. Data Workers has agents read one governed definition per metric instead of rediscovering the schema each time, sends each task only the context it needs, and reports usage and cost per agent and per action.

Agent cost is hard to plan when it arrives as one invoice line and a surprise. Data Workers prices queries before they run, reports usage and cost per agent and per action, alerts when an agent's usage leaves its normal range, and charges a flat platform fee with no usage meter and no markup on model spend.

Most teams keep agents read-only because one bad write can wipe a table. Data Workers gives agents write access through a single approval path: every change is proposed with its impact and an undo path, approved by a named owner and recorded in a tamper-evident audit trail.

Teams hold back on agents that change data because nobody can say how a bad change gets undone. Data Workers records how to undo each change before it runs, puts the change and its undo in front of a named approver, and keeps a tamper-evident record of what happened.

A data agent that guesses at a join or invents a table is worse than no agent. Data Workers builds agents to answer from your real tables and governed definitions, show the source for each fact, check SQL before it runs, say so when the data is missing and remember corrections.

Most data teams run their agents at one setting everywhere, either locked down or trusted too far. Data Workers sets autonomy per domain, from L0 to L4. Every domain starts by watching and suggesting, and its owner raises the level on that domain's own review record.

A benchmark score tells you how an agent does on someone else's tables. Data Workers is evaluated on DataAgentBench with a published method, then builds an eval set from your own historical questions during onboarding and keeps it as a regression suite.

A generic agent repeats the same mistake next week and ignores how your team really works. Data Workers agents take in your runbooks and definitions, remember every correction, flag context that has gone stale, and work to your conventions, with a named person owning what counts as right.

Engineers already work in Claude Code, Cursor and Codex, but those agents see the code, not the data estate. Data Workers connects them over MCP to governed context, lineage and the back-office agents, so a change arrives with its impact, its tests and a named approver.

Every AI tool your company adopts wants its own connection to the warehouse, its own permissions and its own way to change data. Data Workers gives every MCP-capable agent, yours or ours, one governed layer: trusted context to read, approvals to act, an audit trail for both.

Many estates run two or three warehouses, and each vendor's own tools stop at the edge of their platform. Data Workers puts one set of agents, one set of standards and one audit trail across Snowflake, Databricks and BigQuery, and traces lineage between them.

Most platform rollouts begin with a data copy, a new warehouse or a quarter of integration work. Data Workers connects read-only to the stack you already run, spends the first week watching and suggesting, and widens only when your owners say so. Nothing migrates.

Azure data teams spend their week rerunning Data Factory pipelines, chasing owners and answering the same alerts in Teams. Data Workers works with the Purview catalog, Entra ID groups and Azure OpenAI you already run, and handles the operations work with a named approver in charge.

Data incidents get discussed in chat, but the fix still waits on a person hunting for context. Data Workers posts alerts to your Slack and Teams channels and sends each approval request to the owner in Slack with what will change, why and how to undo it.

Moving off Teradata, Netezza, Oracle or Hadoop is usually a multi-year program with a risky cutover. Data Workers inventories every object and consumer, moves workloads in approved waves and proves parity table by table.

Years of legacy SQL, stored procedures and scripts decide how fast any move to Snowflake goes, and someone has to read every line. Data Workers inventories the code, shows what each piece touches, helps translate it and flags what needs a person, with approvals and an audit trail.

Most companies run several BI tools nobody chose on purpose, each with its own version of revenue. Data Workers inventories what people actually use, moves the metric logic into governed definitions you own, proves every number and retires the rest.

Your ontology, pipelines and business logic should be yours. Data Workers rebuilds them on your own warehouse, dbt project and governed context layer, runs side by side with Foundry and moves one workflow at a time.

Legacy ETL tools hold decades of business logic in proprietary mappings. Data Workers reads the jobs, rebuilds them as dbt and orchestrated code, proves the outputs match and retires the old tool.

Control-M, Autosys and aging Airflow deployments hold thousands of jobs nobody wants to migrate. Data Workers maps every job and dependency, moves them in waves and verifies every SLA.

Data mesh programs define the operating model and stall in execution. Data Workers gives every domain an agent workforce that builds, registers and maintains data products to the firm's standards.