Investigate quality failures
Investigate quality failures using the current data quality rule and its supporting records.
DATA QUALITY RULE / AFFECTED RECORD / CORRECTION RECEIPT
AI agent for data quality teams
Find the broken record before it becomes a bad decision.
Investigate quality failures, repair record defects and verify downstream recovery—carried through by an autonomous AI agent that adapts to your work.
A specialised AI agent you can ask for more. Chat here on the site.
$249.00/month, including $50.00 in monthly credits. Monthly subscription. Before tax.
Specialised AI agentYour own report presetNo connection needed to begin
Data quality rule workbenchSPECIALIST / 1062
YOUR SPECIALISED AI AGENT
One place for investigate quality failures, repair record defects, verify downstream recovery.
MADE FOR YOUR WORKDAYStart with work already on your desk
Your first useful assignment.
Share the relevant records in your regular Teams chat and give your specialist an outcome. No model or tool choices to make first.
Get my AI agent →YOU COULD ASK
“Resolve this week's supplier-master failures, correct the confirmed defects and verify that downstream purchasing reports recover.”
- Investigate quality failures
- Repair record defects
- Verify downstream recovery
A useful first result
An editable data quality rule and correction receipt register with traceable references, resolved discrepancies and specific next actions.
Example assignment, not a promised delivery time. Your agent works from the records and access you provide. Add connections when you want it to act in other systems.
A conversation at your desk
Resolve this week's supplier-master failures, correct the confirmed defects and verify that downstream purchasing reports recover.
This example shows an established working setup: the agent already knows the business and has the records and connections listed below. Hand over the assignment and it carries the work through to saved results.
Illustrative project · configured agent at work
A configured agent with your data quality rule, affected record, correction receipt records, current commitments and the connected accounts used to repair record defects.
- Investigate quality failures
- Repair record defects
- Verify downstream recovery
A working specialist can handle the project, with a configured team sharing the work where available. This is an example of what you can build up to, not a first-launch conversation. Timing and usage depend on the assignment.
- You · Data quality teams
Resolve this week's supplier-master failures, correct the confirmed defects and verify that downstream purchasing reports recover.
- Your AI agent · AI Agent Store
Most failures come from a new import mapping. Two unusual identifiers are valid, so I am preserving them while fixing the mapping.
Work in progress - You · Data quality teams
Keep the original record values in the correction history.
- Your AI agent · AI Agent Store
Confirmed defects are corrected with source evidence, valid exceptions are documented and downstream checks show the actual recovery. Your working files and dashboard reflect the recorded outcomes.
Later · delivery and saved results
DATA QUALITY RULE / AFFECTED RECORD / CORRECTION RECEIPT
A working brief for your AI agent.
Track rule versions, ownership, source identifiers and correction receipts. Identify root causes, reconcile downstream effects and preserve valid unusual records rather than normalising everything to an assumed pattern. Your specialist maintains these working records in regular Teams as the assignment develops.
AI
Your AI agentPrepared for data quality teams
Data quality rule and correction receipt registerEXAMPLE, NOT LIVE ACTIVITY
An example record for data quality rule, affected record, correction receipt, source tables.
01Data quality rule02Affected record03Correction receipt
The useful next step
Investigate quality failures
Investigate quality failures using the current data quality rule and its supporting records.
The details your specialist follows
Working records for data quality teams
Follow each data quality rule through the working record. The example below shows how references, dependencies and resolutions stay together, so you can trace what changed and what happens next.
Keep an eye on: changes in affected record, missing correction receipt evidence and unresolved data quality rule dependencies.
Illustrative working document · your records start empty
| Data quality rule | Dependency to follow | Evidence of completion |
|---|---|---|
| OPPORTUNITY036-104 | Investigate quality failures | Data defects resolved |
| OPPORTUNITY036-118 | Repair record defects | Downstream recoveries verified |
| OPPORTUNITY036-126 | Verify downstream recovery | Quality remediation commitments met |
Active data quality rule records3
Source tables across the planned work72 source tables
Outcomes with supporting evidence2
Illustrative project figures. Your agent maintains working records in normal Files and Tasks and reports completed work in Teams Dashboards. Ask it to adapt those records and reports as your workflow changes.
From enquiry to a useful next step
Data quality rule and correction receipt register. Your way of working.
01
Brief and source records received
Add a job reference and the details you already know.
02
Investigate quality failures
Data quality rule and Affected record, ready to check.
03
Repair record defects
Give your specialist the outcome and let it carry the work forward.
04
Outcome evidence verified
Keep working records in Files and Tasks.
05
Verify downstream recovery
Keep the job and its next steps together.
Ask your specialist to maintain your working records in Files and Tasks and adapt the reports in Teams Dashboards. These are the regular platform tools, prepared for your business. Connections to other systems are optional.
A small tool for a real job
Source tables across the planned work. Before the back-and-forth.
Watch for changes in affected record, missing correction receipt evidence and unresolved data quality rule dependencies. Investigate the cause, carry useful follow-through to completion and improve the saved method when it helps.
Try the numbers, then bring the confirmed brief to your specialist. Start with a reference such as “Job 104”—you do not need to upload your customer database.
- Data quality rule
- Affected record
- Correction receipt
- Source tables
Try your own numbers
Source tables across the planned work
Planning arithmetic from your inputs. Your specialist can turn the confirmed details into a working brief. This is not a quote or capacity commitment.
Built around the details that matter
Investigate quality failures. With the right questions already in mind.
Continuous internal data quality operations, distinct from building pipelines or delivering analytics projects.
Repair record defects
Repair record defects while keeping affected record changes traceable.
Verify downstream recovery
Verify downstream recovery with checked correction receipt evidence and editable working files.
Prepared for your business
The numbers behind your ai agent for data quality teams.
Six useful measures, ready in your regular Teams dashboard. Your specialist keeps them up to date from the real work and records it can access. Ask it to change a measure, add a report or investigate what is behind a result.
Recorded cases · outcomes
Quality investigations completed
What this measures
Count one distinct observed outcome for “Quality investigations completed”, linked to its data quality rule and stable completion-event ID. Use the real completion timestamp and supporting correction receipt reference. Pending, merely promised, repeated and illustrative outcomes do not count. Revisions or corrections replace the original event rather than creating duplicate completions.
Recorded cases · outcomes
Data defects resolved
What this measures
Count one distinct observed outcome for “Data defects resolved”, linked to its data quality rule and stable completion-event ID. Use the real completion timestamp and supporting correction receipt reference. Pending, merely promised, repeated and illustrative outcomes do not count. Revisions or corrections replace the original event rather than creating duplicate completions.
Recorded cases · outcomes
Downstream recoveries verified
What this measures
Count one distinct observed outcome for “Downstream recoveries verified”, linked to its data quality rule and stable completion-event ID. Use the real completion timestamp and supporting correction receipt reference. Pending, merely promised, repeated and illustrative outcomes do not count. Revisions or corrections replace the original event rather than creating duplicate completions.
Average · hours
Quality failure detected to verified correction
What this measures
Measure “Quality failure detected to verified correction” in elapsed hours using the actual start and end timestamps for the same data quality rule. Store one duration per completed observation with source references and timezone. Exclude pending cases, missing timestamps, reversed intervals and illustrative values. Never substitute a current status or sum recurring snapshots.
Observed rate · %
Quality remediation commitments met
What this measures
Completed commitments assessed for “Quality remediation commitments met”, linked to stable data quality rule references. Numerator: on_time; denominator: on_time plus late from the same assessed population. Classify each observation once and preserve failures. Exclude pending or unknown cases, unassessed records and illustrative values. Compare actual completion with the original agreed deadline and timezone; never move the deadline to improve the result. Corrections replace the original event instead of counting twice.
Observed rate · %
Assessed records passing quality rules
What this measures
Completed assessments for “Assessed records passing quality rules”, linked to stable data quality rule references. Numerator: passed; denominator: passed plus failed from the same assessed population. Classify each observation once and preserve failures. Exclude pending or unknown cases, unassessed records and illustrative values. Apply the documented requirement or acceptance checklist and retain the actual supporting evidence; do not infer a pass from an administrative status. Corrections replace the original event instead of counting twice.
Your reports start empty. Figures reflect observed work in the selected period; missing records are not treated as measured zeroes. Current jobs, schedules and stock stay in your working files and tasks. You and your agent can adapt the dashboard as your business changes.
Keep the tools that already work
Specialised in your trade. Open to your next idea.
Your AI agent starts with the details of your business already in mind. The three workflows are starting points. Ask it for help with another task in the same conversation.
An AI agent prepared for your business
The starting instructions, intake questions and working rules already match data quality teams. Add your service list and one example of your tone when you are ready.
Give your agent the whole job
Give your agent an outcome, the access it needs and your budget. It handles routine decisions and follows through in your connected tools, asking when a missing business decision needs you. Set or change your own boundaries whenever you want.
Ask beyond the starting workflows
Your agent can take on a new assignment, build a new workflow, improve its saved methods and adapt its dashboard as your needs evolve. The starting examples do not limit its capabilities. Give your agent another task in plain language. What it can carry out depends on the tools you connect and the access you choose.
Recommended starting plan
Your AI agent for data quality teams.
Begin with one specialist and a report preset for your business. After setup, your regular Agent Teams conversation opens. No tool selection before your first conversation.
$249.00/month
Capacity for 1 running agent · hosting throughout the month
Start my AI agent →A clear starting total
Your Agent plan is $199/month with hosting included. This recommended offer adds $50 in monthly Platform Credit funding, for $249/month.
$249.00 before tax today and monthly, including $50.00 in monthly Platform Credit funding.
Credits offer an easy start and unused credits roll over. You can switch to your own subscriptions or API keys anytime, with no Platform Credits needed for that AI work. Hosting is included within your agent’s resource allocation. Optional voice and other services use credits.
Hosting availability is checked before payment. Create your account, confirm your monthly subscription, then start chatting when your specialist is ready. Your instructions and dashboard are prepared automatically after payment.
Other plans and connection options →
Prefer your own AI accounts? Choose a plan without the extra $50 monthly credit funding using the options above. Changing AI access alone does not remove that funding. Automatic top-ups start off; buy credits or set top-ups and spending limits when needed. Keep the full Teams controls for models, connections and supported settings.
Before you begin
An AI agent for data quality teams.
How does the agent keep data quality rule work accurate?
Track rule versions, ownership, source identifiers and correction receipts. Identify root causes, reconcile downstream effects and preserve valid unusual records rather than normalising everything to an assumed pattern. It keeps source references with the working files, checks the result and follows up on discrepancies. Ask it to adjust the method or report whenever your needs change.
Is this only for the three tasks shown?
No. This is an AI agent specialised for data quality teams, with three prepared workflows to get you started. You can ask for writing, research, planning, analysis or another task in the same chat. It can make routine decisions and carry the work through using available tools. You set its boundaries; it asks when a real business decision or missing access needs you.
What happens after I subscribe?
After payment, we prepare your AI agent for data quality teams, its workflows and your data quality rule and correction receipt register dashboard. Your regular Agent Teams chat opens when setup is ready. Files, Tasks, settings and billing work the same as for other customers. Reports start empty and count actual reported work; the examples above are illustrative.
Do I need to connect email or messaging?
No. Prepare your first brief and draft here. Connect external channels later when you want the specialist to work there.
Does this replace my existing business software?
Use it alongside your current tools for preparing work and organising jobs. Live stock, availability, sending and payment records require a verified connection or your own confirmation.
Choosing for a different business?
Compare the working records and business types each specialist is prepared for.
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