Manatal's CV parser often trips up agencies with messy uploads, unsupported formats, and layout quirks. If you're struggling to get clean candidate data into Manatal ATS, this guide explains why and what to do about it.
TL;DR: Manatal's CV parser handles common file types but struggles with complex layouts, images, and tables. Fix the most frequent failures by stripping out tables, standardising fonts, and sticking to supported formats. A consistent, ATS-safe workflow saves time and avoids errors.
What is Manatal and who uses it
Manatal is a cloud-based ATS and recruitment CRM aimed at small and mid-sized agencies, especially across APAC but increasingly in the UK and Europe. It combines candidate tracking, CRM features, and AI-powered matching tools in one platform.
Recruiters use Manatal to manage candidate databases, track job orders, and automate sourcing workflows. Smaller teams and newer agencies favour it for its simplicity and pricing. Some larger agencies adopt it for specific projects or markets.
Its built-in CV parser is a key feature, designed to speed up candidate data extraction from uploaded CVs. Like most parsers, it has limits and quirks that require manual fixes or pre-processing.
How its CV parser actually works
Manatal's parser extracts candidate information by scanning structured text fields in uploaded CV files. It detects standard metadata — name, contact details, education, work history — by spotting keywords and common CV patterns.
The parser relies heavily on clean, linear text. It struggles when formatting is complex, such as:
- Tables or columns that break reading order
- Embedded images or icons
- Non-standard fonts or excessive styling
- PDFs generated from scanned documents or screenshots
Manatal uses AI to guess sections and match data points, but errors increase with messy layouts. The parser's confidence drops when contact details are embedded in headers or footers.
Unlike some competitors, Manatal doesn't natively parse scanned PDFs with OCR. It expects a text layer for extraction.
Supported file formats and limits
Manatal accepts these CV file types:
| Format | Notes |
|---|---|
| DOCX | Best support; text-based, editable Word files |
| Must contain selectable text (not scanned images) | |
| RTF | Supported but less common |
| TXT | Plain text only, no formatting |
File size limit is 10MB per upload. Larger files may be rejected or truncated.
Avoid these formats:
- Scanned PDFs or image-only PDFs
- Older DOC (Word 97-2003) files — may lose formatting
- Pages (Mac) documents — no support
Upload errors and parsing failures are common if the file isn't text-searchable or contains complex layout elements.
The 5 most common CV failures and how to fix them
1. Contact details missing or split across header/footer
Manatal's parser often misses phone, email, or LinkedIn URLs placed in headers or footers.
Fix: Move all contact info into the main body text, preferably at the top. Avoid putting details in page margins or document properties.
2. Tables and columns scramble the data order
CVs with multi-column layouts or tables confuse Manatal, causing job history or education to appear out of sequence.
Fix: Remove all tables and convert columns into a single column layout. Use line breaks and bullet points instead.
3. Embedded images or logos break parsing
Candidate photos, icons, or logos embedded within the CV interrupt text flow and cause parser errors.
Fix: Delete all images and logos before upload. If branding must remain, embed it in a header-free cover letter instead.
4. Scanned or image-based PDFs return no data
Manatal can't extract text from scanned CVs or image-only PDFs.
Fix: Convert scanned PDFs to editable Word or text documents using OCR software before submitting.
5. Non-standard fonts and excessive styling interfere
Fonts not widely supported or excessive use of bold, italics, and colours can distort the parser's reading.
Fix: Stick to standard fonts like Arial, Calibri, or Times New Roman. Keep styling minimal and consistent.
Brand-kit + ATS-safe formatting workflow
An ATS-safe CV puts candidate data front and centre in a simple, linear format. Here's a workflow to standardise Manatal CVs and avoid parser issues:
- Start with a clean Word DOCX template using one column
- Place name and contact details at the top body text, no headers/footers
- Use standard fonts (Calibri 11pt or Arial 11pt) and black text only
- Remove all images, icons, watermarks, and tables
- Use bullet points for job duties, no columns or tables
- Save as DOCX or searchable PDF (if PDF, check text layer with select tool)
- Run a quick read-through to confirm no contact info or dates are hidden
- Test upload with Manatal and review parsed data immediately
If you send many CVs weekly, this workflow saves time by preventing manual fixes and data entry errors.
FAQ
Can Manatal parse multiple CVs in bulk?
Yes, Manatal supports bulk uploads, but batch parsing accuracy depends on file quality and format consistency. Follow the workflow above to reduce errors when uploading many CVs.
Does Manatal support CV anonymisation?
Manatal doesn't anonymise CVs automatically. Agencies should use separate tools to strip personal data before submission if required by compliance policies.
Why does Manatal parse dates incorrectly?
Date formats like "06/07/21" can confuse Manatal's parser depending on locale settings. Use full month-year formats (e.g., "June 2021") for clarity.
Can I upload CVs in languages other than English?
Manatal can process CVs in multiple languages, but parsing accuracy varies. Complex scripts or non-Latin alphabets may cause data extraction issues.
What if my client requests a PDF CV output?
Manatal primarily outputs candidate data in structured formats. To provide a client-ready PDF, export from Manatal and convert using external tools. Avoid uploading PDFs with complex design.
If you regularly send 20+ CVs to Manatal and struggle with formatting errors, try Distill. It strips headers, footers, images, and tables automatically, delivering a clean, ATS-ready Word document in 30 seconds. Try Distill with your ATS export.