Lucanet Launches AI Tagger Agent for XBRL Reporting
Lucanet introduced what it calls a market-first Tagger Agent for XBRL reporting ahead of the upcoming reporting season. The goal: remove the manual, page-by-page tagging grind from financial statement notes and disclosures.
"As the industry's first comprehensive narrative tagging agent, the Tagger Agent addresses XBRL's toughest bottleneck, which is the manual, page-by-page tagging of financial statement notes," says Janis Steinmann, head of XBRL at Lucanet. "By combining deep understanding of financial narrative context with a multi-agent approach, we're giving finance teams a tool that increases tagging accuracy, reduces repetitive work, and lets experts focus on validation and controls rather than tedious tagging. This is a step change for reporting efficiency and quality."
What it does
- Uses a multi-agent AI architecture to handle narrative tagging, numeric table tagging, extension creation, and calculation linkbase generation in parallel.
- Turns a days-long expert process into an automated workflow that finishes in minutes or hours, with humans still approving the final output.
- Delivers over 80% accuracy versus human-tagged reports, according to Lucanet, while preserving expert oversight.
- Optimized for European mandates with support for English, German, Swedish, Italian, and Spanish, aligned to the latest ESEF taxonomy.
Why this matters now
ESEF introduced block tagging for notes to financial statements for fiscal years starting on or after January 2022. Updates effective at the start of 2025 require use of the latest ESEF taxonomy, more granular block-tagging, stricter validation, and updated anchoring rules.
For finance and management teams, that raises the volume and precision of work. Automating first-pass tagging while keeping control over validation is a practical way to shorten close-to-file timelines without sacrificing quality.
Quick refresher: XBRL
XBRL is a global standard for digital financial and sustainability reporting. It makes financial and business data machine-readable for automated validation, easier comparability, and faster regulatory filings. For background, see XBRL International and ESMA's ESEF resources.
Practical impact for CFOs and controllers
- Cycle time: Free specialists from repetitive tagging so they can focus on review, governance, and sign-off.
- Quality: Consistent application of tags across narratives and tables, with calculation linkbases generated alongside.
- Staffing: Reduce dependence on peak-season external tagging capacity; keep expertise in-house.
- Auditability: Human validation and controls remain in place, supporting internal and external assurance.
- Multilingual reporting: Built-in language support fits cross-border filings and investor communications.
How to put it to work this season
- Import your annual report draft (notes, MD&A, and tables) and select the latest ESEF taxonomy.
- Let the agent generate narrative block tags, table tags, extensions, and calculation linkbases.
- Review exceptions, anchoring, and taxonomy choices; resolve any ambiguous mappings.
- Run validations against ESEF rules and fix flagged issues before final export.
- Export and file; document your review steps to reinforce internal controls.
Risks and guardrails
- Maintain human review on material notes, sensitive disclosures, and complex extensions.
- Set thresholds for auto-accept vs. review and track exceptions over time.
- Benchmark accuracy on last year's filing and lock approved mappings for reuse.
- Monitor taxonomy updates and anchoring changes with each report iteration.
If you're building your team's AI capability for finance operations, explore curated tools and training at Complete AI Training: AI Tools for Finance.
Bottom line
With stricter ESEF requirements in effect and filings due soon, automating first-pass XBRL tagging can cut time and cost while keeping control where it belongs-on your team. The Tagger Agent is built to take the grunt work off your plate so you can focus on validation, accuracy, and delivery.
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