Insight
Data Foundations Setup
A small cloud data warehouse in BigQuery or Postgres with up to 4 business tools synced daily, 10 cleaned reporting tables and…
Insight
AI Data Cleaning
Messy data cleaned to a documented rule set automatically, before it reaches your reports or your team.
Duplicates, inconsistent formats and missing values creep into every dataset, then spread into reports and decisions. This build cleans a defined dataset automatically, to a documented rule set you approve. Every edit is recorded in a change log, and rows that fail the rules are flagged for a person instead of being guessed. It runs quietly in the background.
- Duplicates, formats and missing values fixed by agreed rules
- Every edit recorded in a change log
- Rows that fail the rules flagged for human review
$3,400
20–30 working days
Optional maintenance $249/mo
What it is
AI Data Cleaning finds and fixes data quality issues, such as duplicates, inconsistent formatting and missing values, in a defined dataset, running to a documented rule set you approve, in the background with no customer-facing channel. Every edit is recorded in a change log.
Why businesses buy it
Bad data costs time twice: once when someone cleans it by hand, and again when a wrong number leads to a wrong decision. Manual cleaning is also inconsistent, because different people apply different rules. This build applies one documented rule set every time and keeps a full change log, so you can trust the result and trace any edit. The written scope sets the systems, workflow steps and test runs on your sample data before you order.
Problems this solves
Duplicate and badly formatted records spoil reports and mailings
The workflow finds and fixes duplicates, inconsistent formatting and missing values in a defined dataset. It works across up to three connected systems, running quietly in the background.
Nobody trusts automated clean-ups that change data without a trail
Every edit is recorded in a change log. The cleaning runs to a documented rule set you approve, so you can see what changed and why.
Clean-up rules are applied differently each time someone does it
One workflow of up to twelve steps, including one AI decision step, applies the same rules on every run. Twenty test runs on your sample data confirm it before launch.
A good fit if
Messy or inconsistent data, such as duplicate records or mismatched formats, is quietly breaking your reports or your other automations.
You can describe your current manual clean-up process, or show us a screen recording of how it is done today.
You want a documented rule set for what counts as clean, rather than a black-box fix nobody can explain.
You already keep the source data in a system such as Google Sheets, a database or a CRM, with admin or API access available.
Not the right fit if
You want the cleaned data explained back to you as insights, not just corrected. See AI Customer Insights, which turns combined customer data into segment-level insights.
You want unusual patterns flagged rather than formatting and duplicate issues fixed. See AI Anomaly Detection, which flags unusual patterns automatically.
You want a customer-facing answer or report from the data, not a background clean-up workflow. See AI Data Analyst Agent.
Your workflow needs more than 12 steps or more than one AI decision point. Add a note at checkout, or start from our Custom AI enquiry form.
Your messy data is mainly CRM records that need deduplicating and enriching on a schedule. See AI CRM Data Hygiene.
You need a data warehouse with your business tools synced into clean reporting tables. See Data Foundations Setup.
How it works
- You give us a written description or a screen recording of how the data is cleaned manually today, plus 5 to 10 real sample inputs.
- We document a rule set for what counts as clean, such as how duplicates are matched and which fields must not be blank, and you approve it.
- A workflow of up to 12 steps is built, including one AI decision or drafting step for judgment calls such as which duplicate record to keep.
- The workflow runs against up to 3 connected systems, correcting records in place or flagging exceptions for a person to check.
- 20 test runs are completed on your sample data before launch, so the rule set is proven before it touches live records.
- Once live, alerts and errors are routed to the person you name, so anything the rule set cannot resolve is never silently dropped.
What you provide within 72 hours
Your portal lists these the moment you order. Delivery starts the working day after everything is in and your access is checked.
- The list of systems involved, with admin or API access for each.
- A written description or a screen recording of the current manual clean-up process.
- 5 to 10 real sample inputs that show the kind of mess the workflow needs to handle.
- The name of who should receive alerts and errors the workflow cannot resolve on its own.
- Admin access and API keys for every connected system, shared by adding our team as a user or creating a key you control, never by email.
- Your business information (company basics, hours and brand voice), shared during intake.
- One decision-maker who can approve the documented rule set before launch.
- An API key for your own AI model account (for example OpenAI or Google Gemini), created for this build with a spending limit.
The 72-hour rule. Send everything on your checklist, including access, within 72 hours of ordering so your build starts on time.
Timeline
Start
Intake confirmed and the current manual process and sample inputs reviewed.
Weeks 1–2
Rule set documented and the cleaning workflow built.
Week 3
Connections to your systems and exception routing configured.
Weeks 3–4
20 test runs completed on your sample data.
Week 4
Your review and two rounds of changes to the rule set.
Go-live
Live, handover report sent, 30 days of support starts.
| Option | Delivery time | Price |
|---|---|---|
| Standard | 20–30 working days | $3,400 |
| Express | 15–25 working days | $4,100 |
| Rush | 10–20 working days | $4,750 |
What you receive at handover
- The AI data cleaning workflow, running in the background in your environment
- A documented, approved rule set, with prompt and guardrail configuration for the AI step
- Connections to up to three agreed systems
- A change log recording every edit the workflow makes
- A test report from twenty test runs on your sample data
- An admin guide, the written handover report and 30 days of post-launch support
Expected benefits and how to measure them
- Can make reports and mailing lists more reliable
- Helps staff stop fixing the same data problems by hand
- Gives you a clear record of every change
- Duplicate records
- Count duplicates in the dataset before the first run. Count again after a month of runs.
- Records with missing key fields
- Run the same field-completeness check before launch and monthly after.
- Manual clean-up time
- Ask staff to estimate weekly time spent fixing data before launch and again after.
Before you start: Take a snapshot of the dataset and count duplicates, formatting issues and missing values. Note how much time staff spend fixing them.
These are things to track, not promised results. Outcomes depend on your data, your team and your market.
Running costs
The build itself is a one-time payment, shown before you buy. Running the workflow costs roughly $20 to $100 a month in your own accounts, covering the automation platform and AI model usage, depending on data volume.
AI usage during the build and testing is billed to your own accounts too, so the provider relationship stays in your name.
Ask about running costsCustom requirements
If your workflow needs more than 12 steps, more than one AI decision point, or a fourth connected system, add a note at checkout for a written Change Quote. Your order still runs on its original delivery date. Larger or bespoke projects go through the Custom AI request form instead.
Request a custom versionKeep it running
Monthly maintenance covers monitoring, fixes, updates, a health report and change hours for rule tweaks as your data changes shape. If you plan to add systems or datasets, that goes through a Change Quote. Maintenance starts after the included support period and can be cancelled any month.
$249/mo (the minimum fee, includes 2 change hours)
Optional. Starts after the post-launch support period. Cancel any month.
How maintenance worksHow this compares
| Do it yourself with a SaaS tool | Hire a custom agency | Upmark Labs | |
|---|---|---|---|
| Price | A data quality tool charges per row processed or per connector | A data engineering agency quotes per cleanup project | Ours is one fixed price, shown before you buy |
| Delivery | A data quality tool is instant to sign up but generic rules out of the box | An agency project usually takes several weeks | Ours has a fixed delivery time shown before you buy, with faster options |
| Rules | A data quality tool ships default matching rules you cannot always see | An agency documents rules if you ask for it | Ours documents the rule set and gets your approval before it runs |
| Exceptions | A data quality tool often silently drops records it cannot resolve | An agency build depends on what was agreed | Ours routes anything unresolved to a named person, never silently |
Questions about this product
What does the AI Data Cleaning price include?
One fixed price, agreed before work starts, covers up to three connected systems and one workflow of up to 12 steps, including one AI decision step, proven on 20 test runs with your sample data. Faster delivery options are shown in the price panel before you order, and there are no other build fees.
What kind of data issues does AI Data Cleaning fix?
Duplicate records, inconsistent formatting and missing values are the most common, matched and corrected against a documented rule set you approve before launch. The exact rules come from your current manual process and 5 to 10 real sample inputs you provide.
How is AI Data Cleaning different from AI CRM Data Hygiene?
AI Data Cleaning works on any dataset you name, such as a spreadsheet, export or database table, using a documented rule set. AI CRM Data Hygiene is built for CRM records only. It deduplicates, standardizes and enriches contacts and companies on a schedule. Choose Data Cleaning when the mess sits outside your CRM, or spans several sources.
What happens when AI Data Cleaning cannot resolve a record?
It is flagged as an exception and routed to the person you named during intake, rather than corrected on a guess or silently dropped. That exception routing is part of the base build, tested across 20 runs on your sample data before launch.
How much does AI Data Cleaning cost to run each month?
Roughly $20 to $100 a month in your own accounts, covering the automation platform and AI model usage. The figure depends on how much data runs through the workflow each month. You pay these providers directly, with no markup from us.
Can AI Data Cleaning cover a fourth system later?
Yes, as an add-on. A fourth connected system, or a workflow with more than 12 steps, is priced in a written Change Quote before any work starts. Small rule adjustments fit inside the monthly change hours that come with maintenance instead.
Who owns the rule set AI Data Cleaning documents?
You do. The documented rule set and the workflow itself run in your own accounts, against your own systems. If you stop maintenance, the workflow keeps running; you lose the monthly monitoring and included change hours, not the rule set.
Fix the data quality problem before it reaches another report.
Order AI Data Cleaning and a documented rule set starts cleaning your data automatically, with every edit logged. The scope above is what you get, in writing, before you pay.
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