Pulling together the information for Spain's EINF (Estado de Información No Financiera, the mandatory non-financial statement) usually turns into an endless chain of emails, lost spreadsheets buried in shared folders, and figures that don't match between HR, Operations and Finance. It isn't a willpower problem. It's a data architecture problem.
The problem: scattered data
Every department keeps information its own way. HR holds headcount and diversity data in one system. Operations has energy consumption spread across loose invoices or an ERP. Finance tracks the related spend in a different tool altogether. No one holds the full picture, and whoever coordinates the report spends weeks chasing answers by email.
That creates three frictions that repeat across almost every company required to report:
- Emails and spreadsheets with no version control: no one knows which file is the latest, or who last edited it.
- Contradictory data between departments: the same headcount or consumption figure comes out differently depending on who reports it.
- Constant human error: copying and pasting between spreadsheets introduces mistakes that only surface, if they surface at all, when the auditor asks.
How Manglai solves it: centralization and automation
Manglai works as a single source of truth. Instead of chasing each department by email, every team uploads its data directly into the platform, with clear workflows and an owner assigned to each indicator.
- One shared data repository: HR, Operations and Finance load their information into the same platform, not separate files that someone has to consolidate by hand.
- Workflows with defined owners: every indicator has a clear owner, so there's no ambiguity about who's responsible for each data point.
- Automatic deadline alerts: the system flags each owner before the closing date approaches, instead of the delay surfacing in the final week.
- Traceability for every data point: you know who entered each figure, when, and from what source, which makes the external verifier's review that EINF requires far easier.
AI as an extra layer: less manual data entry
Centralizing the data solves the scattering problem. Manglai's AI solves the most time-consuming part within that centralization: entering the data in the first place.
- Automatic invoice reading: the AI extracts and classifies electricity, water, fuel and waste consumption directly from the PDF, with no manual typing.
- Template-free file import: spreadsheets or CSVs from HR or a supplier are automatically adapted to the report's format, with no manual reformatting.
- Agents that execute, not just respond: they transform and upload data autonomously, removing the intermediate steps between the original file and the final data point.
Why this matters beyond the EINF
The EINF isn't a one-off exercise: it repeats every year, with data that must stay consistent with the prior year and be verifiable by an external auditor. A process built on emails and spreadsheets doesn't scale: every year that passes, the data volume grows and so does the margin for error. The same scattered-data problem across departments shows up just as often in companies already reporting under CSRD, where the traceability bar is even higher, although Directive (EU) 2026/470 has narrowed that directive's scope to companies with more than 1,000 employees and more than 450 million euros in net turnover.
Centralizing before automating is the right order: without a single source of truth, AI just accelerates the chaos. With it, every new data collection cycle starts from an organized base instead of from zero.
This matters most for sustainability teams coordinating data across several departments or sites that need data to arrive verified, not just collected.
