DataAlchemist connects contractual form, source evidence, and role indicators so users can evaluate whether a record belongs in the benchmark set.
From Extracted Fields to Transaction Intelligence
Most benchmarking tools organize the surface layer of a transaction: rates, dates, agreement types, industries, payment terms, and contract text. That layer is necessary. It is not enough.
Transfer pricing and valuation work increasingly depends on the structure behind the row: what rights were granted, how the payment was defined, what functions each party performed, whether the parties were related, and whether the agreement form reflects the economic substance of the transaction.
How the Platform Works
Source Evidence
The platform starts with verifiable source materials: license agreements, service contracts, leases, patents, regulatory filings, press releases, and corporate disclosures. These materials remain connected to the resulting benchmark record, so users can move from a normalized field back to the evidence behind it.
Structured Intelligence
DataAlchemist converts source material into comparable records: normalized terms, payment bases, rights granted, IP type, industry context, relationship indicators, and role signals. This is where raw documents become usable benchmark intelligence.
Substance Context
The platform enriches records with functional and economic context: DEMPE evidence, licensor and licensee roles, related-party indicators, risk/control signals, and development-stage context. This layer surfaces when two transactions that look identical on the row diverge once you read the substance.
Comparability Engine
The comparability engine allows users to test benchmark sets across industry, IP family, licensed rights, relationship, royalty base, functional profile, evidence depth, and risk indicators. The result is a more disciplined way to filter and review comparables.
See the workflow behind the benchmark.
Review short walkthroughs showing how DataAlchemist connects source evidence, structured records, and comparability diagnostics inside the platform.
Edgar — the engine behind the record
Edgar, DataAlchemist's in-house, domain-trained AI model, supports the construction of every benchmark record: triaging source documents, structuring their terms (payment, rights, roles), linking each record to its evidence, and enriching it with asset and market context.
It is not a search layer on top of a database. Edgar runs through record construction before a benchmark is shown, with each record reviewed and confirmed by a domain specialist.
Why Substance Changes Comparability
A royalty agreement may show a rate and payment base. But comparability often depends on deeper questions: Who controls the asset? What functions does each party perform?
Two agreements may look similar in a table and still differ materially in economic substance. DataAlchemist surfaces those distinctions before users rely on the benchmark range.
Analyst Review
DataAlchemist combines model-assisted processing with rule-based checks and analyst review to validate consistency.
The goal is traceability and comparability discipline at scale. This review layer keeps the product from becoming a flat warehouse of extracted fields and preserves the focus on evidence-backed comparability analysis.
Supported Workflows
Transfer Pricing Benchmarking
Use royalty, service, lease, and contract data to support intercompany pricing analysis, audit preparation, and policy design.
Royalty Benchmarking
Build royalty benchmark sets with normalized licensing terms, payment structures, evidence packs, DEMPE indicators, and industry context.
IP Valuation & Deal Advisory
Support IP valuation, reasonable royalty, licensing, PPA, technology transfer, and expert analysis with comparable transactions connected to contractual, technical, market, and regulatory evidence.