Need Make + Airtable expert to finish MCA CRM automation

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1

I have a mostly built Make.com automation for a business-funding/MCA operation and I’m looking for someone experienced with Make + Airtable + AI document extraction to audit and finish it.

Current workflow already handles Gmail intake, Google Drive uploads, AI extraction from funding applications/bank statements, and Airtable writes.

I need help finishing the system so it:

  • creates one clean merchant record per business
  • prevents duplicates
  • uses application data as the primary merchant source
  • extracts business name, owner, phone, email, amount requested, monthly revenue, industry, time in business, credit score, funding purpose, existing positions/lenders, business address, EIN, and SSN last 4
  • extracts bank-statement data separately
  • prevents later documents from overwriting good application data with blanks
  • never confuses broker/ISO/funder contact info with merchant info
  • links documents properly in Airtable/Drive
  • runs automatically with proper error handling

Most of the scenario is already built. I’m looking for someone who can inspect and fix what exists rather than unnecessarily rebuild everything from scratch.

I’m open to paying for a short audit first, then having the same person finish the implementation. Please reply or message me with experience, availability, and rate.

6 Likes
3

Hi @Ultra_Fund, welcome to make community I can help with this, auditing and finishing an existing Make + Airtable + AI extraction scenario without rebuilding.

Website: https://www.pathfinderautomationsolutions.com
Calendly: Calendly
Email: [email protected]
Upwork: https://www.upwork.com/freelancers/~01446d60f782215efa

Happy to start with the paid audit first.

Taiwo

4

The main issue is data integrity. Your automation already does the heavy lifting, but it needs stronger logic so merchant records stay clean, duplicates are prevented, application data remains the source of truth, and later documents cannot overwrite good data with blanks or incorrect broker/ISO details.

I’d solve this by adding a proper merchant-matching layer, source-priority rules, non-blank update logic, separate bank-statement extraction, validation for merchant vs broker/funder data, cleaner Airtable/Drive linking, and reliable error handling so the workflow can run automatically without corrupting records.

I’d start with a short audit of the existing Make.com scenario, identify the exact weak points, and then fix only what’s necessary rather than rebuilding the system.

Feel free to reach out to me here to discuss this further, you can also book a meeting with me here to discuss how to get this done.

Would you be able to share the Make blueprint and 2–3 sample application/bank-statement files so I can quickly assess where the current logic is breaking?

5

Hey,

It looks like you already have a solid part of the system built, so I’d be happy to jump in, audit what’s there, and finish it without unnecessarily rebuilding things.

The requirements are clear and technically aligned with my experience.

Experience: 4+ years with Make, Airtable, APIs, and AI automation
Availability: 15–20 hrs/week
Rate: Open to work within your budget

I’d be happy to start with the short audit and then take it through to completion.

Thanks

6

Did you manage to complete this project, or are you still looking for some help? I could also help you out. Let me know.

7

Hi @Ultra_Fund - the duplicate, blank-overwrite, and source-priority symptoms suggest data-integrity problems, but I’d verify whether the root cause is extraction, mapping, scenario flow, or Airtable logic before claiming that. My background is Python/API data pipelines and reconciliation; I won’t overstate Make-specific experience. I’m offering a $100 founding pilot, credited toward implementation: a sanitized review, duplicate/overwrite tests, one bounded staging repair where feasible, and a fixed completion quote within 48 hours. No raw SSNs or unredacted bank statements needed. If useful, reply here and I’ll send the one-page scope.

8

Hi @Ultra_Fund — the key risks are source precedence, null-safe updates, and stable merchant matching. I can provide a fixed $95 audit with written findings and a prioritized fix plan within 24 hours of receiving the Make blueprint and redacted samples. My background is Python/JS/APIs/JSON rather than years of production Make work. Can you confirm your deadline, payment method, and that no live credentials or unredacted SSNs or bank statements are required?

9

Hi @Ultra_Fund,

I’d be happy to help audit and finish this without unnecessarily rebuilding what you already have.

The workflow you’ve described involves a few areas where I’d want to pay particular attention: merchant identification and deduplication, separating application data from bank-statement data, preventing incomplete documents from overwriting good application data, and making sure the document relationships remain traceable in Airtable and Drive.

Starting with a paid audit makes sense here. I’d first review the existing scenario, data structure, and current extraction/mapping logic, then identify what should be fixed versus what is already working well.

I’ve also sent you a DM with my relevant work and details.

10

Hi — I’ve built a similar workflow in Make with email/document intake, AI extraction, Airtable, duplicate checks, and manual review for exceptions.

I’d be happy to start with the paid audit you mentioned, look through the existing setup and the source-priority/duplicate logic, and then help finish the implementation.

I can share an example of a similar workflow I’ve built as well.

12

Hey, this looks like a good fit. I’ve built production automations around APIs, AI extraction, CRMs, and the reliability issues you mentioned. I can start with a short audit this week at $60/hr and work within what’s already built instead of rebuilding it.

13

The exact bottleneck is preserving merchant data integrity across Make and Airtable when application and bank-statement documents arrive: deduplication, source priority, non-blank updates, merchant-versus-broker separation, correct linking, and reliable error handling. My strongest demonstrated proof is a local Python stack using Ollama and ChromaDB for document retrieval, a guarded read-only SQLite/FastMCP tool boundary, typed JSON contracts, and deterministic fixtures; I will not claim Make/Airtable production history I cannot show. I can share a 30-second video or sample JSON trace of the matching and non-blank-update logic, then start with a fixed $95 audit using only redacted or synthetic data and no sales pressure.

14

Hi,

this is closely aligned with the type of automation work I’ve been building recently.

I’ve worked with Make + Airtable systems involving API/webhook data intake, AI extraction/structured outputs, deduplication, search-before-create logic, record updates and preventing bad or incomplete data from creating duplicates or overwriting existing information.

Since most of your workflow is already built, I’d also prefer starting with a short paid audit rather than rebuilding it unnecessarily. I can review the existing scenario, Airtable structure and mapping logic, identify where data integrity is breaking, and then scope the fixes.

I’m available immediately. My rate is $20/hour, and I’m also open to agreeing on a small fixed price for the initial audit.

15

Hey Ultra_Fund team—just saw your post on the Make Community regarding your MCA workflow. You’re hitting the classic data-integrity wall: deduplication, isolating merchant data from broker/funder payloads, and stopping blank fields from overwriting good records.I built a fault-tolerant Make.com intake and routing pipeline specifically designed to solve these exact friction points using custom JSON validation nodes, isolated staging schemas, and strict error logging to Google Sheets.I’d love to take you up on your offer for a rapid audit. I can jump into your current scenario today, pinpoint exactly where data is bleeding or overwriting, and map out the fix. Happy to do the initial audit paid as requested, let’s connect and get your MCA pipeline airtight.

16

Hi Ultra_Fund, I came across this while looking at Make/Airtable workflows that are having data-integrity issues.

The duplicate prevention, non-blank update logic, source-priority rules and failure logging you described are actually the exact problems I built a small lead/data-intake automation framework around.

Rather than rebuilding your existing scenario, I’d be interested in looking at the current flow and showing you how I’d handle that reliability layer.

If useful, I can send you a very short demo of the approach.

17

Ultra_Fund, plenty of people here have already named the likely causes, so I will not repeat them. What I can offer instead is the specific test I would run first, which is checkable by you rather than something you have to take on trust.

Two artifacts, before any code changes.

The first is a field authority matrix. One row per field you listed, one column per source: application, bank statement, broker or ISO, and funder. Each cell says which source may write that field, and whether a blank from that source is ever allowed to clear it. I would test whether that rule currently lives in the order the modules happen to run in rather than anywhere explicit, because that is the pattern I would look for first. On one page it becomes something you can approve or correct directly.

The second is a replay test. Take one merchant’s documents and run them in both arrival orders, application first then statements, then the reverse. Three things must hold in both runs: no blank ever clears a populated field, every application-authoritative field ends on the same value, and both runs resolve to exactly one merchant record. Whatever the outcome, you get the result in writing. Rows that fail are where I would start looking. If the merchants we test all pass, that is worth knowing too, and I would say so and ask what else to point the same test at rather than keep hunting the same theory.

On duplicates I would test rather than assume. My first check would be whether the match key stays stable when the same business applies twice with a slightly different legal name, but that is a hypothesis until I see your data.

Being straight with you on two things. I have zero clients and zero case studies. The only thing I can offer up front is the test above and its output, and you should weigh that accordingly. Second, I would work only from redacted or synthetic samples, never live credentials and never real SSN or EIN values. Given what those documents contain, that ought to be the standard for whoever you pick, not just for me.

On price. The audit you asked for, meaning the two artifacts above plus a written findings list and a fix order, is 400 USD fixed. If you then want the implementation, I work in five business day fixed scope blocks at 1500 USD, spec to handover with acceptance tests agreed before the build, and I would credit the audit fee against that. I am available from Monday, async and written first, calls when they help.

18

Hi Ultra_Fund,

Your audit-first approach fits how I would handle this. I can trace one redacted application and bank-statement pair through the existing Make scenario, map field ownership and precedence, reproduce the duplicate, blank-overwrite, and broker-versus-merchant risks, then deliver an implementation-ready module fix and acceptance-test map.

The audit can use a blueprint export, Airtable schema, and synthetic or redacted fixtures; I would not need live SSNs, bank statements, or production writes.

Would you like me to start with that short audit?

Jason

19

@Ultra_Fund — your paid-audit-first approach is exactly how I’d handle this.

The interesting part of your problem isn’t just Make — it’s data integrity: which source may change which field, duplicates, blank overwrites, merchant vs broker/funder contamination, and what happens when documents arrive in a different order.

I can do a 24h audit for $400 fixed from the Make blueprint, Airtable schema and redacted/synthetic fixtures — no production writes, live credentials or real SSNs.

You’d get:

  • field authority / source-precedence map
  • replay tests with different document orders
  • PASS / FAIL for the corruption cases you described
  • exact fix order + fixed implementation scope

$200 to start / $200 on handoff.

My current proof is from my own operating system rather than client Make work, and I’ll label it exactly that.

If the current logic is already correct, I’ll say so rather than invent work.

Volo

20

@Ultra_Fund, this fits TinyOps Studio’s audit-first rescue model. I would start with one redacted application and one redacted bank statement, trace both arrival orders through the current scenario, and test four failure modes: duplicate merchant creation, blank overwrite, broker or funder contamination, and broken Airtable or Drive linking. No live credentials or unredacted identifiers are needed.

The fixed audit is $250 and takes two business days. You get a field-authority map, pass or fail replay results, and the exact repair order. If you proceed with implementation within 14 days, the $250 is credited in full. TinyOps has public n8n workflow proof; I will not claim prior Make or Airtable client work I cannot show.

If this is still open, which issue is costing you more now: duplicate merchant records or good application fields being overwritten?

21

Hi @Ultra_Fund — I reviewed the failure modes you described and I’d approach this as a data-integrity audit first, not a rebuild.

The main points I’d verify are:

  • deterministic merchant matching / deduplication
  • source precedence so application data stays authoritative
  • null-safe updates so later documents never overwrite good values with blanks
  • separate extraction paths for applications vs bank statements
  • strict separation of merchant vs broker / ISO / funder contacts
  • Airtable + Drive linking integrity
  • retry / error handling so the scenario can run unattended

I can start with a fixed $125 audit, delivered within 24 hours after receiving the Make blueprint and 2–3 redacted sample inputs.

The audit would include the exact failure points, recommended changes, and a prioritized fix plan. If the audit confirms the issues above, I can also implement and test the fixes immediately afterward.

I would avoid rebuilding anything that is already working correctly.