Tax Preparation, Assessment & Transfer-Pricing Documentation

AI tax preparation, assessment and transfer-pricing documentation: an AI case study across individual, small-business and enterprise tax

AI can read tax source documents, fill the return, check every transaction against indirect-tax rules and draft the documentation multinationals owe in every country, so specialists review the result instead of retyping it. Across 4 source cases, a Brazilian tax-technology company achieved a 400% performance improvement in state VAT automation, helping 19,000 companies and identifying more than BRL 15M in overcharges.

Tax work is mostly retyping and reassembly, until document AI reads the source and people review the output

The Challenge

Tax work is dominated by data entry and document assembly. Individuals and small businesses transcribe figures from W-2s, 1099s, receipts and statements into returns, and in the process they miss reliefs and make errors. Companies subject to complex indirect-tax regimes overpay, because assessing every transaction correctly by hand is impractical. Multinationals must produce local files, master files and country-by-country reports under OECD requirements for every jurisdiction, which consumes weeks of specialist time every year.

The cost lands differently on each group. Professional adviser fees put accurate compliance out of reach for smaller firms, while tax authorities' digital filing requirements keep rising. Individuals and small businesses pay in adviser fees or in errors and missed reliefs. Enterprises pay in specialist time, penalties and overpayments that are never recovered because nobody had time to check.

Returns are retyped by hand. Figures move from forms, receipts and statements into returns manually, so reliefs are missed and errors creep in.

Indirect tax is overpaid. Checking every transaction by hand is impractical, so overcharges go unnoticed.

Documentation takes weeks. Local files, master files and country-by-country reports are assembled by specialists for each jurisdiction.

Compliance is out of reach for smaller firms. Adviser fees are high, and digital filing requirements keep rising.

The Approach

Document AI extracts structured data from tax source documents and auto-populates the relevant fields across the most common forms, with LLM interpretation handling unusual layouts. Computer vision supplies OCR and layout recognition for scanned forms, receipts and financial statements, and natural language processing interprets the content and drafts explanatory text. Machine-learning models assess indirect tax, such as state VAT, across large transaction volumes and flag overcharges and compliance issues.

A digital filing assistant guides small businesses through returns, identifies reliefs and explains each step, and for multinationals AI drafts transfer-pricing documentation from financial and intercompany data. Workflow automation moves each filing from extraction to review. The taxpayer or adviser reviews every output before filing, and each extracted figure stays traceable to its source document. Extraction accuracy is validated against human-prepared returns before autofill is trusted, confidence thresholds send uncertain fields to review, and rules engines encode jurisdiction-specific tax law and are updated as regulation changes.

Techniques: Document AI and OCR extraction · LLM interpretation of unusual layouts · ML-based indirect-tax assessment · Rules engines for jurisdiction-specific law · Generated transfer-pricing documentation with source traceability

AI role

AI extracts tax data from source documents, auto-fills returns, assesses tax liabilities across transactions, identifies reliefs and overcharges, and drafts transfer-pricing documentation for human review and filing.

Automation scope: Individual, SMB and corporate tax preparation, indirect-tax assessment and international documentation.

400%

— performance improvement in state VAT automation at a Brazilian tax-technology company

19,000 companies

— helped to automate VAT by the same company

BRL 15M+

— overcharges identified for its clients

10 most common US tax forms

— covered by done-for-you autofill at a leading tax-software provider using document AI and LLMs

ROI types: Time Savings · Cost Reduction · Risk & Compliance

Evidenced in: Financial Services (tax-technology and tax-software providers, multinational tax teams)

How Dataforest Would Deliver This

Before choosing a model, we settle what the data may be used for. In the US, Section 7216 restricts how preparers use or disclose tax return information without the taxpayer's consent. For Form 1040 filers, Rev. Proc. 2013-14 sets the format of that consent. The answer decides where documents are processed, which vendors see them and whether any model may learn from them.

The first workflow with a clear payback is usually indirect tax: last period's purchase lines for one jurisdiction, run through the rules. Many findings trace back to master data, such as product tax codes and supplier IDs. In transfer pricing, comparable selection and the arm's-length range stay with your specialists. We measure recoverable overpayment and the share of lines a specialist had to touch.

Book an indirect-tax recovery scan: we'll run a sample of last year's purchase lines through your jurisdiction's rules and estimate what was overpaid.

From our projects: LLM contract analysis with document-type routing for a US SaaS platform

A US company founded by former Amazon and Microsoft engineers was building a document platform for construction and legal teams. Reviewing its customers' NDAs, change orders and project agreements by hand was slow, inconsistent and hard to scale.

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We paired an LLM with decision-tree logic: each document category gets its own prompt template, so change orders are not read like NDAs. Analysis starts on upload and returns clauses, dates and obligations as structured data, and large document sets run in batch. After several rounds of fine-tuning and testing, document analysis is up to 70% faster, and review takes seconds per file instead of several minutes.

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The engine reads contracts; it does not calculate tax, apply jurisdiction rules or prepare returns. What it shows is the step before any tax logic: identify the document type first, then extract with logic built for that type. The intercompany agreements behind a transfer-pricing file are contracts too. Read the full case: LLM contract analysis with document-type routing.

"Keep the tax rules in an engine you can update, not in the model's memory. The law changes every year, and a system you have to retrain for each change will always be a year behind."

Aleksandr Sheremeta

Managing Partner

Talk to our AI team

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Frequently asked questions

What inputs does AI tax preparation need?

It needs the tax source documents themselves, such as W-2s, 1099s, receipts, financial statements and scanned forms, read through OCR and layout recognition. Transfer-pricing drafts additionally need financial and intercompany data. Jurisdiction-specific tax law must sit in a rules engine, and extraction accuracy is validated against human-prepared returns before autofill is trusted.

What tax work stays with human reviewers when AI prepares returns and transfer-pricing documentation?

The taxpayer or adviser reviews every output before filing. AI extracts data, auto-fills returns, assesses liabilities, identifies reliefs and overcharges, and drafts transfer-pricing documentation from financial and intercompany data. Confidence thresholds send uncertain fields to a reviewer, so people spend their time on exceptions and judgment rather than on retyping figures from forms.

How does AI tax automation stay accurate when tax law changes?

Rules engines encode jurisdiction-specific tax law and are updated as regulation changes, so the law does not live in the model's memory. Dataforest's guidance is that a system retrained for every change will always be a year behind, because rules differ by jurisdiction and change every year. Machine-learning models then assess indirect tax, such as state VAT, across large transaction volumes and flag overcharges.

How can AI-filled tax figures be traced in an audit?

Each extracted figure stays traceable to its source document, which supports audit defence. Extraction accuracy is validated against human-prepared returns before autofill is trusted, and confidence thresholds route uncertain fields to a named reviewer. Dataforest's view is that an autofilled number that cannot be traced to its document will not survive an audit.

Which tax processes beyond return preparation does AI cover?

The documented scope covers individual, small-business and corporate tax preparation, indirect-tax assessment such as state VAT, a digital filing assistant that guides small businesses through returns and identifies reliefs, and international documentation: local files, master files and country-by-country reports. Time saved on transfer-pricing documentation is described as significant, but the source cases give no exact figure.

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