Algorithm7 is the company. Algorithm 7 is the method.See How It Works
A7 / Algorithm 7The technology behind the nameSeven stages · one translation layer01 / 07
The technology behind the name
What isAlgorithm 7?
The seven-stage system that transforms proprietary real-world information into structured, verifiable AI intelligence. From source records to model-ready datasets, Algorithm 7 enables frontier labs to test the value of data before they consume it.
The world's knowledge wasn't recorded for AI.Algorithm 7 makes it usable.
Algorithm 7 isn't anonymization, data cleaning or brokerage. It is the translation layer between proprietary real-world information and frontier AI development: a repeatable technical process that runs the same seven stages on every source, in any industry.
Software pipelinesDomain-specific modelsRules and schemasVerification systemsExpert review
Not a single mathematical algorithm, and not fully automated. The mix of automation and expert review depends on the data and is scoped per engagement.
Algorithm 701 / 07
Discover
Example datasetCandidate source
SourcesThree operational systems
SpanSeveral years of records
ModalitiesText, tables, sensor series
PotentialDiagnostic and agent tasks
Illustrative. Scroll to watch one dataset change through each stage.
01 / 07
Discover
Identify proprietary information with potential AI training or evaluation value, and assess its uniqueness, completeness, provenance and possible applications. Work can start from a lab's capability specification or from a data owner presenting what they hold.
Demand-led
A lab specifies a capability gap. We find and assess sources that could address it.
Supplier-led
A data owner describes what they hold. We assess what it could teach or test.
Output: a source inventory and opportunity specification
02 / 07
Authorize
Establish who controls the information and the legal permissions governing each use. Access, processing, evaluation, commercialization and training are separate questions, and each is answered in writing before any data moves.
Ownership and custodyPermitted processingContractual restrictionsEvaluation authorityConsent, where requiredCommercial and training rights
Apply the privacy, security, confidentiality and data-minimization safeguards the rights record requires. De-identification is used where it is legally and technically appropriate, with restricted-access processing when sensitivity demands it.
Extract, normalize, organize and reconstruct meaningful information. Context, relationships, sequences, decisions and outcomes are preserved where the source material supports them, and non-sequential data keeps its own structure.
Chronology and decision reconstruction, where evidenced
Normalization to domain schemas, provenance preserved
Output: structured records
05 / 07
Translate
Convert structured information into usable AI assets, each with a domain schema, a grading specification and traceability back to its authorized source evidence.
Training examplesEvaluation tasksReasoning challengesAgent workflowsMultimodal datasetsExecutable environments, where feasible
Output: AI assets with schemas and grading specifications
06 / 07Test before you license
Prove
Validate data quality, source fidelity, task correctness and the verification mechanisms themselves. Then package testable samples, benchmarks and controlled evaluations so labs can assess usefulness before committing to larger purchases.
Source fidelityCompleteness and label accuracyTask correctnessLeakage checksGrader reliabilityExpert review
The buyer measures. Then the buyer decides.
Rights-cleared sampleBlind benchmarkSecure environmentAPI gradingLimited pilot
Proof shows how well the data and tasks meet requirements. Claims that training improves a model require a designed training experiment.
Output: a verification report and an evaluation product
07 / 07
Deliver
License and deliver the approved machine-consumable assets under agreed commercial terms: training datasets, evaluation suites, authorized data access or recurring delivery programs. Restrictions, provenance, versions and quality documentation travel with every release, and data owners are paid under their agreements.
PurposeConsume at scale for authorized uses
ScopeThe full authorized dataset, versioned
AccessDelivered or accessed per license
ReferencesIncluded where the license permits
TrainingPermitted where the license grants it
TermsBuyer license within supplier limits
Output: a licensed, versioned delivery
See it run
From raw source to a testable asset.
Step through a synthetic example on the Overview: the raw source, the evaluation Algorithm 7 builds from it, and the asset a lab consumes under license.