Fix Make.com/Parseur/Airtable Automation

@shivansh121

Hi, I am available here for the work so please message me here & LET'S GET STARTED THE WORK WITH ME. Looking forward to an early and positive response. Regards, Shalu

@johnallenvillan1

Hi , I’ve reviewed your setup, especially the recurring 422 errors on the “Price per kg” field, and it’s clear the Make → Parseur → Airtable chain is failing at the formatting layer rather than the ingestion itself. In a similar seafood import workflow, I stabilised price parsing by normalising symbols, decimal rules, and fallback values, reducing ingestion failures to zero within one pass. The real challenge here is the inconsistency coming from OCR: values like £6.80, 680, or 6,80 require a deterministic cleanup pipeline before Airtable ever receives them. If this isn’t handled upstream, Airtable will keep rejecting records regardless of mapping tweaks. I’ll implement a strict numeric normaliser in Make, add guards for blanks, enforce dot‑decimal formatting, and restructure your Airtable mapping so every field resolves to a predictable type. I’ll also incorporate light error‑skipping logic to ensure the scenario never crashes. Before starting, I need to confirm how many supplier formats Parseur is currently handling and whether any fields beside price occasionally fail. Thanks, John allen.

@kishanbwts

I’ve fixed similar OCR-to-Airtable automations where numeric fields failed due to inconsistent formatting. The 422 error usually comes from unexpected strings or misplaced decimals. To fix your setup, I’ll start by adding parsing steps in Make to clean the price data: strip £ signs, convert commas to dots, and scale values like 680 to 6.80 properly. I’ll also enforce Airtable’s number format before inserting, preventing rejections. For stability, I’ll add error handling so bad rows skip without stopping your scenario and add logging to spot recurring data issues. Are the price fields always in the same column in Parseur? Also, do you want strict validation on price ranges or just skip invalids? If you like, I can suggest parsing the item description further for species and size fields to organize your data better and add flags on outlier prices. I’m ready to jump in as soon as you share access and files to get your automation running smoothly without errors.

@muneeb6767

I’ll fix your Make → Parseur → Airtable flow by normalising price fields (regex + formatter), ensuring numeric mapping (no 422 errors), and adding error handling to skip/flag bad rows for stable daily runs. Optional: extract species/size + validation flags for clean data.

@toqeer74

The 422 error when creating records in Airtable, especially for the “Price per kg” field, indicates a fundamental issue with data mapping. This is most likely due to a combination of data type mismatches from Parseur's OCR output and the formatting in Make.com. I propose a focused approach to refine the mapping to ensure that: 1. Numeric fields are accurately formatted, removing currency symbols and standardizing decimal representations. 2. Errors during data ingestion are managed with robust validations to bypass or handle non-conforming data. I can deliver initial changes in just 1 day to set the foundation for a stable system. Happy to share a few early ideas, want me to put something together?

@Nabbpb

Hi, Core challenge is normalising inconsistent OCR output into clean, type-safe data that Airtable accepts every time, especially for numeric fields like “Price per kg” where small format issues cause 422 failures. I’d fix this directly inside Make by adding a proper transformation layer before Airtable. That includes stripping currency symbols, standardising decimals (6,80 → 6.80), handling edge cases like 680 → 6.80 where needed, and safely casting everything to numbers. I’ll also add validation steps so bad rows don’t break the scenario—either skipped or defaulted with flags. For stability, I’ll clean up the mapping, add error handling (routers + fallback paths), and make sure every run completes without crashing. If needed, I can also lightly improve Parseur templates to reduce noisy OCR output at the source. Optional: I can extract structured fields (species, size) and add simple anomaly checks for unusual prices.

@YuriiVusochin

Hello, I won’t go into my previous projects, you’ve probably seen plenty of similar proposals, and they tend to feel repetitive. Instead, I’d like to share how I approach my work. I understand that deciding to post a job isn’t easy. Every dollar comes from your time and effort, and I respect that. If a developer doesn’t treat a project like their own, it’s unlikely to succeed. That’s exactly how I work. I take responsibility, I care about the outcome, and I do my best to deliver something you’ll be happy with. Yurii

@vasylklymiu

Hi, I can take this on I have worked with Make and Airtable automations where small data inconsistencies cause failures, so I am comfortable tracking down and stabilising flows like this quickly I also focus on making these pipelines resilient so they keep running smoothly even when incoming OCR data is messy or inconsistent I would fix the Airtable mapping by normalising all numeric fields in Make before insertion, ensuring the Price per kg field is always sent as a clean number value that matches Airtable field types I would implement parsing logic to strip currency symbols, standardise decimal formats, and safely convert edge cases like 680 or 6,80 into valid numeric values with fallback handling for invalid inputs I would also add error handling in the scenario so failed rows are skipped or flagged instead of breaking the entire run, ensuring consistent daily ingestion without interruptions My skills include Automation, Airtable, and Python with approximately 8 years of development experience working on data pipelines and OCR based workflows Happy to get this cleaned up quickly and make the system reliable for your daily price updates

@jeremiasg17

Hello! I’ve tackled a similar automation project involving PDF data extraction and integration into Airtable. My previous work improved data reliability by over 90%, ensuring seamless ingestion without errors. I can share the implementation details in our chat. To address your current setup, I’d focus on ensuring the data types match Airtable’s requirements and refining the OCR parsing to eliminate inconsistencies, particularly with pricing formats. I'm curious, how are you currently handling edge cases with your data, like unexpected formats or missing values? If you’d like, we could set up a quick call or I could start with a small milestone to fix the mapping and stabilize the ingestion. If you’re open, I can share my previous build, and we can see if it fits your needs. Looking forward to your thoughts!

@sergheib9

Hi there, I’ve reviewed your setup and understand the issue: your Make → Parseur → Airtable flow is mostly working, but Airtable is rejecting the Price per kg field due to inconsistent OCR formatting and numeric mapping. I can fix this by: Cleaning price values before Airtable mapping Removing £ symbols and extra text Converting commas/dots correctly Handling cases like 680 → 6.80 where needed Skipping or flagging invalid rows instead of crashing Adding error handling so the scenario runs reliably daily I can also add optional logic to extract species, size, and validation flags for unusual prices if needed. I have a few questions: Q1 – Is “Price per kg” currently set as a Number or Currency field in Airtable? Q2 – Do all suppliers use similar price-list formats? Q3 – Should invalid rows be skipped or added with a warning flag? Looking forward to hearing from you.

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@inciterzt

Hi, I’ve worked on very similar seafood/price list automations, and the issue you’re facing is exactly where OCR + Airtable schemas usually break. From your setup (Email → Make → Parseur → Airtable), the 422 error on “Price per kg” is almost certainly due to inconsistent OCR outputs (e.g., £6.80 / 6,80 / 680) not being normalized before mapping. Here’s how I’ll fix and stabilise it: • Clean & standardise price values inside Make (remove £, unify decimals, safely convert formats like 680 → 6.80 only when valid) • Enforce strict numeric mapping to Airtable (no strings reaching number fields) • Add validation layer before record creation (skip/log invalid rows instead of failing the scenario) • Improve Parseur + Make mapping so every row is predictable and consistent To make it reliable for daily supplier feeds, I’ll also: • Add fallback handling for missing/dirty OCR data • Implement logging so you can trace any anomalies • (Optional) Structure item descriptions into species/size + flag unusual prices This is a quick fix for me (2–3 hours), but I’ll make sure it’s robust enough for real daily operations—not just patched. Happy to jump in immediately once you share access and sample files. Best, Inciterz Team