The same twenty minutes every export
Fixing and adding up by hand.
Make.com data transformation and aggregation workflow for reports and documents built from lists
Make.com data transformation and aggregation workflow that turns raw lists into clean records and single, well-grouped results
An iterator turns a list into separate bundles and an aggregator turns bundles back into one, and each bundle a module handles counts as an operation, so the order of steps changes both the result and the cost. BoldBellows notes the shape of the incoming data and the shape the destination needs before any mapping is drawn, splits lists into bundles only where it must, aggregates them back with the grouping you approved and counts the operations each step will use, then builds the Make.com data transformation and aggregation workflow in the right order: fields are cleaned and typed as early as possible, lists are split only where each item needs its own handling, items are filtered before expensive modules run, aggregators group by the field you choose and build the single result, such as one invoice from twelve order lines or one weekly summary from five hundred rows, and the result is written once. Totals are checked against the source so nothing is lost in the grouping. The handover includes an operation estimate per run. Custom Make.com data transformation and aggregation workflow work covers unusual grouping or text parsing.
View Pricing
From $2,120, paid once. Make.com data transformation.
- Make.com data transformation
- Aggregation that adds up
- Operation cost estimated
How Make data transformation and aggregation earns trust
- Clean earlytypes and dates fixed up front
- Split only where neededfewer bundles, fewer operations
- Grouped by your fieldone result per group
- Totals checkedthe sum matches the source
- Replay safeRunning the same event again changes nothing
- No usage meterafter the $2,120 build there are no seats, tasks or message fees from us
B2Before and after
Every export needs the same twenty minutes of copying, fixing and adding up
A system exports a file of rows. Someone opens it, fixes the dates that came as text, splits the product list in each row, groups the lines by customer and types the totals into the invoice template. In Make the first attempt iterates every row, runs a lookup and a write on each one, and uses five thousand operations to produce what a different order of steps does in eight hundred. A mistake in the grouping puts two customers' lines on one invoice, and nobody notices until the customer writes in.
Sound familiar?
Dates that arrive as text
Later modules rejected them.
Five thousand operations for eight hundred rows
Every row triggered every module.
Two customers on one invoice
The grouping field was wrong.
Totals that did not match
Rows were lost in the grouping.
A report that took all morning
The process was manual.
The same exports, turned into results automatically
Before
- Nothing tells a repeat from a new change
- Exports are cleaned and totaled by hand
- Every row runs through every module
After
- The same event always lands on the same bundle, using the source record, the group and the aggregated result
- Data is cleaned, grouped and totaled by the scenario
- Filters and grouping cut the operations used
D4How it works
How rows become clean groups and one result each
First decide, then find, then act: that is the whole route in one line.
1. Understand the change
The Make record is read to see what really changed, and the result is compared with your rule.
2. Clean, filter, split
Fields are typed, rows that do not need handling are filtered out, and lists are split only where each item needs its own step.
3. Group, total, write
Aggregators group by your chosen field, totals are checked against the source, and each result is written once.
E5Inside your pipeline
Under the hood: clean, group and total
First Mile handles who and whether, Full Route handles what and where, Long Watch handles how it stays true.
First Mile
Trigger validator
Confirms that the trigger really fired for this record before the route moves on.
First Mile
Stable identity ledger
Remembers each Make item and what the data transformation and aggregation route did with it, so the same event cannot do the same work twice.
First Mile
Context packager
Bundles the source ID, time and link with each value so every bundle can be traced back.
Full Route
Field cleaner
Types dates, numbers and text and fixes the usual inconsistencies up front.
Full Route
Early filter
Drops rows that need no handling before any costly module runs.
Full Route
Grouping aggregator
Groups bundles by the field you choose and builds one result per group.
Long Watch
Totals check
Compares aggregated totals with the source and flags any difference.
Long Watch
Operation estimate
Estimates the operations per run so the plan can be sized before launch.
Long Watch
Resolution log
Closes each case with what was changed, by whom and when.
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G7Who it is for
For the logistics coordinators that want to reshape messy data, split lists into items and combine items into summaries, without paying for operations you do not need
Raw lists become useful only when someone cleans, groups and totals them, and that work should not need a person every week.
Finance lead
"Invoices from order lines."
Lines are grouped by customer and totaled.
Operations manager
"Weekly reports without effort."
Rows are summarized into one report.
Automation freelancer
"Keep client costs down."
Filters and grouping lower operation counts.
Ecommerce manager
"Reconcile payouts."
Transactions are grouped and matched to payouts.
Data analyst
"Trust the totals."
Each aggregate is checked against the source.
Founder
"Stop the Monday export."
The scenario builds the report before you arrive.
Expect the price to track bundles, groups and aggregated results
Pricing a data scenario: sources and groupings
You set the price here: pick a package for Make data transformation and aggregation, make three scope choices and watch the total from $2,120.
View Pricing
One setup fee
I9Your options
If manual copy-paste and spreadsheet formulas after every export and zaps are not enough: a scenario that splits, cleans, groups and aggregates data and writes one tidy result
Each can move rows. Only one cleans first, groups correctly and checks that the totals add up.
| How data arrives | Fixing by hand | Rows through every module | Our aggregation scenario |
|---|---|---|---|
| What starts the work | A person reading notifications | Whatever Make sends | A scenario run verified as current and in scope |
| How identity is decided | Memory and search | No key, only a search | Hand-cleaned exports and expensive row-by-row runs |
| What reaches Data Transformation and Aggregation | What the person had time for | Whatever the trigger returned | Cleaned, grouped and totaled results at a predictable cost |
| After an uncertain result | Someone re-enters the record | Gives a failed-run notice only | Confirms state, then corrects or closes |
Two updates, wrong order
When updates cross in transit, the later one wins and the earlier one is kept in the log, not written over it.
One customer name is spelled two ways in the export
The cleaning step normalizes it before grouping, so both spellings land in one result. It looks up the source record, the group and the aggregated result, compares like with like and records which of the four outcomes applies. Unclear matches are held for review instead of guessed at.
With the source record, the group and the aggregated result in place, a duplicate, a late event and a lost reply all have a safe answer.
Pricing
Build your Make to Data Transformation and Aggregation price
Pick a package, answer three short questions about how Make data should reach Data Transformation and Aggregation, and your price and turnaround move as you choose.
J11The process
Six steps from raw exports to finished results
Work moves through six steps, and you approve the important ones.
-
Day 1
Read the data
We look at the exports, the fields and the results you build by hand today.
-
Access review
Approve access
Permissions are kept to the minimum data transformation and aggregation needs in Make, and the way records are paired is written down with you.
-
Build phase
Agree grouping and checks
The grouping fields, the totals to check and the operation budget are written down and signed off.
-
Before you review
Rehearse before launch
The route is exercised end to end in a test Data Transformation and Aggregation, including the failures, before it touches real data.
-
Release day
Launch with notes
After the final test we enable the route and give you notes that name every owner and repair path.
-
Day 1
Review and adjust
Real traffic often shows edge cases; this step folds them into the rules and the notes.
K12Turnaround
Package timings at a glance
Time on the clock begins after Make access and the matching keys are agreed.
First Mile
6 to 10 business days
Full Route
10 to 16 business days
Long Watch
16 to 24 business days
L13Getting started
What to have ready
A Make connection you are happy for us to test with
A Make login, a recent export or two of the source data and the template or destination where results should go
Make API details for the account we will connect to
Any scenario runs that must never reach Data Transformation and Aggregation
Approval to review a small set of recent Make records
Preferred language and tone for any messages the route sends
M14Safety and ownership
You keep control of Make and Data Transformation and Aggregation, we keep to the map
Nothing outside the agreed Make fields is read for data transformation and aggregation, and nothing is written outside the approved targets.
Standard access, standard behavior
We rely on the regular access each platform offers, never on a change to how Make or Data Transformation and Aggregation work internally.
Scoped, revocable access
Least-privilege access is the default, and it is documented.
Sensitive values held back
Anything sensitive in Make is either excluded or transformed before it can reach Data Transformation and Aggregation.
Recovery without duplicates
Failed or unclear writes are recovered by matching on the source record, the group and the aggregated result, never by generating a new bundle.
Minimal logging
The audit trail is made of IDs and decisions, not personal data.
Full handover
After handover you can maintain, change or retire the route on your own.
N15In their words
What BoldBellows clients say
Verified reviews from business owners who use BoldBellows automations every day, from marketing and sales to support and operations.
AI Workflow Automation for Agencies
Running a digital agency with many clients was overwhelming. BoldBellows built one automation dashboard that tracks tasks, sends updates and creates client reports automatically.
Clients love the transparency and we love the time we get back. Productivity is up, and project delays have dropped to almost zero.
AI Customer Journey Automation
We wanted a smoother path from first contact to purchase. BoldBellows built an AI system that guides each customer there automatically.
It sends personalized messages, recommends products and follows up at just the right moment. Customer retention has improved dramatically.
AI Lead Qualification & Sales Automation
Our sales team wasted hours chasing cold leads. BoldBellows built an AI lead-scoring system that reads behavior, email engagement and buying signals.
It filters out weak leads and sends personalized follow-ups automatically, so our team only talks to people who are ready to buy. Conversions rose 40% in the first month. It paid for itself straight away.
AI Video & Voice Automation
We publish product videos every week, and BoldBellows introduced us to AI video automation. The system writes the script, voices it and produces the finished video in minutes.
The quality is remarkable: professional voiceovers, clean visuals and smooth editing. Production time went from three days to under 30 minutes, and we scaled our video marketing without adding cost.
AI Email & Smart Inbox Management
Our inbox used to be chaos. BoldBellows built an AI email system that sorts messages, drafts replies and flags anything urgent.
It learns how we write and gets better every week. We reply faster and no longer miss important messages. It feels like having a personal assistant working around the clock.
AI Booking & Scheduling Automation
Our coaching business needed a better way to schedule. BoldBellows automated all of it: bookings, reminders, cancellations and follow-ups.
No-shows fell by 60% and clients enjoy how smooth it is. It feels personal, not robotic. One of the best upgrades we’ve made.
AI Translation & Localisation
When we expanded abroad we needed fast, accurate translations. BoldBellows built an AI translation system that turns our content into several languages instantly.
The translations read naturally and suit each culture. We now serve customers in five new countries without hiring translators. BoldBellows helped us go global.
AI HR & Recruitment Automation
Our HR team was drowning in applications. BoldBellows automated screening, skills-based filtering and interview scheduling.
It saves us hours every week and means we only interview qualified people. Hiring is now faster, better organized and more professional.
AI Accounting & Invoice Automation
BoldBellows automated our whole invoicing process: creating invoices, sending reminders and tracking payments.
Late payments dropped sharply, and our accounting team finally has time for strategy instead of repetitive admin. Worth every penny.
AI Chatbot & Customer Support
BoldBellows built an AI support chatbot for our online boutique, and it has changed how we run the business. We used to have two full-time staff answering the same questions all day. Now the AI handles order tracking, product recommendations and even refund requests on its own.
What surprised me most is how natural it sounds. Customers compliment the ‘agent’ without realizing it’s AI. Our support workload dropped by 75% and customer satisfaction went up dramatically.
AI Content & Social Media Automation
Our marketing agency couldn’t keep up with content. BoldBellows set up an AI content engine that writes blog posts and captions and even designs our social media creatives.
It spots trends and suggests topics for us, so we stay ahead of competitors. We now produce three times the content with the same team, and engagement has soared. They didn’t just automate our workflow, they lifted our whole brand.
AI Data Entry & Reporting Automation
We used to spend hours collecting data and building weekly reports by hand. BoldBellows automated all of it.
The reports are clean, visual and easy to read, and our managers rely on them for quick decisions. Human error is gone and our numbers are more accurate across the board.
AI E-commerce Listings Automation
BoldBellows automated our product listings. The AI writes descriptions, organizes photos and publishes listings across several marketplaces.
It even answers customer questions instantly. Our store looks more professional and sales have noticeably increased.
AI Document & Contract Automation
Onboarding was slow because we wrote every contract by hand. BoldBellows automated the lot: contracts, welcome emails and client setup.
What took 48 hours now takes under 10 minutes, and clients regularly tell us how professional it feels.
AI Social Comment & DM Automation
We get thousands of comments and messages every week. BoldBellows built an AI system that replies instantly, filters spam and surfaces the important ones.
Engagement is up and our page looks active around the clock. It’s like having a full social media team that never stops.
AI CRM Automation
Our CRM was barely used because updating it by hand was tedious. BoldBellows automated data entry, tagging, segmentation and follow-ups.
Now the CRM is always up to date and our campaigns are far more accurate. It improved our whole sales pipeline.
AI Voice Assistant
BoldBellows built a custom AI voice assistant for our customer hotline. It answers questions, books appointments and handles simple requests.
Customers love how fast it is, and our call-centre workload dropped dramatically. One of the smartest upgrades we’ve made.
AI Automation Partner
BoldBellows is hands-down the best AI automation partner we’ve worked with. Their systems are fast, reliable and built around how our business actually works.
Every automation they’ve delivered, from customer service to content, runs flawlessly. We’ve saved time, cut costs and grown faster than we expected.
P16FAQ
Transforming and aggregating data in Make: questions from finance and operations
Short answers to what people ask before they book. Anything else can go in your quote request.
Do we need a build for Make data transformation and aggregation, or is a zap fine?
Where tidy summaries and clean records from raw, repeated data is enough, stay with a zap. The difference shows when you must turn a list of order lines into one invoice, or many rows into a single weekly report. The connector carries the event; the build carries the decisions.
What is the difference between an iterator and an aggregator?
An iterator splits a list into separate bundles, and an aggregator combines bundles back into one.
How do you keep operation counts down?
By cleaning early, filtering before costly modules and splitting lists only where each item really needs its own step.
Can it build one invoice per customer?
Yes. Lines are grouped by customer and each group is written as a single invoice.
How do you make sure no rows are lost?
Aggregated totals are compared with the source, and any difference stops the run and is reported.
Can it handle text parsing?
Yes, for the patterns we agree on. Irregular text is routed to a person instead of guessed.
What affects the price of a Make aggregation workflow?
The number of sources, grouping rules and results, which together become one fixed quote.
Who signs off the Make fields used by data transformation and aggregation?
It is included. The Make fields that matter to data transformation and aggregation are written out with their targets, and anything not on the list stays behind.
Can Make data transformation and aggregation branch on owner, amount or status?
Make data transformation and aggregation can branch by type, amount, owner or segment, starting at $2,120.
What if Make withdraws a permission we rely on?
We write down what the route assumes. When an assumption stops being true, the route stops that step and says so, rather than improvising.
Q17Turn raw exports into finished results
Give your data a scenario that cleans, groups and checks
Share how data transformation and aggregation should behave in Make; the price comes first, beginning at $2,120.
- From $2,120, paid once
- Clean early
- Totals checked
- No monthly fee from us
If your process has extra branches, we will price them on their own line.