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Data Quality Agent

AI data quality: your data connected, monitored and put to work.

Datakili's Data Quality Agent merges, reconciles and cleans your raw data from multiple sources. It catches anomalies before they reach your reports and prepares the fix for your approval.

Launch the demo Test it on your data
Your sources feed the Data Quality Agent, which checks every record continuously, flags anomalies, finds the root cause, alerts the right team and delivers one unified, reliable view in the Agentic Business Cockpit. CRM Web & app Calls Data Quality Agent24/7 monitoring 0 anomalies flagged Missing fieldDuplicateInconsistencyFeed stopped Agentic Business Cockpit 0 records checked Alert · Feed stopped step3 · −41% since 2:02 PM Cause: iOS app v5.2, tracking tag removed. Action: ticket to App team 3 sources → 1 customer one unified view 82% data reliability monitored continuously
From promise to impact

No reliable agent without reliable data

An AI agent does more than answer questions: it reads your data, uses your tools and takes actions in your systems. If its data is incomplete or poorly connected, it acts on the wrong information, and the promised value disappears.

See the use case: customer data quality audit →
What the agent does

Four capabilities, one agent

Connect

Reconcile your sources

Automatic profiling, identifier normalization, and join proposals with their match rates. Uncertain matches are set aside, never forced.

See step 1 →
Monitor

Catch anomalies

Continuous technical and business checks. Deviations are measured against what's expected, seasonality included, to keep false alarms down.

See step 2 →
Act

Diagnose and alert

The agent finds the root cause, sizes the impact and drafts the message for the team in charge, in the tools they already use. It can also trigger another agent.

See step 3 →
Reveal

Surface business insights

Emerging customer journeys, the real impact of your AI agents, and the new analyses each new source makes possible.

See steps 4 and 5 →
Field insights

Four situations most data and customer experience teams know well

Our data doesn't talk to itself.

CRM, contact center, web, app and surveys live in silos. Every join is a manual project, and adding a new source takes weeks.

The agent connects your sources and proposes join keys, each with a confidence level.

We find problems too late.

A feed breaks silently, a tracking tag disappears after a release. Someone notices when a strange number shows up in the leadership review.

The agent monitors freshness, volumes, schema and consistency, continuously.

An alert is useless if nobody acts on it.

The information doesn't reach the right team, or arrives with no diagnosis and no impact. Alerts pile up and nobody reads them anymore.

The agent diagnoses the cause and drafts the message for the right team. You approve before it's sent.

We're sitting on data we don't use.

New customer journeys go unnoticed, new sources stay underused, and teams keep running the same analyses.

The agent surfaces emerging journeys and lists the analyses a new source makes possible.

Interactive demo · about 5 minutes

A day with the agent, in five steps

Pick your industry. The agent reasons in the right-hand panel and asks for your approval before every action.

Industry:
Illustrative data
    The Datakili harness

    What makes the agent production-ready

    The AI model matters, but the harness is what gets an agent into production: it governs what the agent sees, what it decides and what it does.

    Access

    The agent only sees the sources and fields it's authorized to use. PII is masked before any analysis.

    Business rules

    Alert thresholds, data owners, teams to notify: the agent applies your rules.

    Guardrails

    What the agent does on its own and what it brings to you: no join, ticket or message goes out without your approval.

    Control over every action

    Every check, conclusion and action is explained and recorded in the log.

    Run a pilot

    Pilot the agent on a focused scope

    Start from the process, not the technology. Five principles for a pilot that delivers concrete results before you decide to go further.

    1. One specific taskOne journey and 2 to 4 sources, for example "where's my claim?" calls.
    2. The right dataThe agent profiles, matches and cleans the sources you pick.
    3. Clear rulesTogether we define what the agent does on its own and what it brings to you.
    4. Your existing toolsAlerts and tickets land where your teams already work.
    5. A focused startA limited scope, metrics from day one, then we expand.

    Request a pilot

    Takes 1 minute, no commitment. We'll get back to you to scope it together.

    What do you want to tackle first?

    We'll only use your details to follow up about this pilot.

    Part of the Agentic Business Cockpit

    From raw data to managing your AI agents

    The Data Quality Agent lays the groundwork for the Cockpit: reliable data, optimized match rates, key correlations brought to light. The Cockpit then helps you understand, manage and measure the impact of every channel, AI agents included.

    Explore the Cockpit →