Algorithm7 selectively sources exceptional proprietary datasets and engineers them into verified, AI-ready assets. From structured workflows and golden outcomes to scoring rubrics and evaluation environments, we complete the preparation work that frontier labs would otherwise have to do themselves.
Own valuable proprietary data? Assess your datasetFragmented proprietary information: maintenance logs, clinical histories, software incidents, experimental results, financial workflows.
Valuable but unusable as-is. Inconsistent, unlinked and unverified.
The world's knowledge wasn't recorded for AI. Algorithm 7 makes it usable.
We don't aggregate information indiscriminately. We identify exceptional proprietary datasets, engineer them into verified AI-ready products, and give frontier developers a way to assess their usefulness before licensing.
Our standard is not how much data we collect. It's how much usable intelligence we can deliver.
Every candidate dataset is tested against the same seven criteria before any engineering begins. Sources that don't meet them aren't pursued, however large they are.
Not a marketplace
There is no upload-and-hope listing. Every product is selected and engineered.
Not volume-driven
We don't compete on the number of datasets. We compete on what each one can prove.
Worth the engineering
Specialized preparation is only applied where the source justifies it.
Rare proprietary information may be valuable, but making it usable for frontier AI takes another layer of specialized engineering. Workflow reconstruction, expert annotation, ground-truth verification, scoring rubrics, evaluation design and training-ready formats. Algorithm7 does this work before the lab acquires the finished product.
Done: completed by Algorithm7 before delivery. The data you need, with the preparation already done. Scope always depends on what the source evidence and permissions support.
Data quality gets you considered.Data readiness earns the premium.
The value we set out to demonstrate with each product, not a guaranteed price.
Not raw records. Ready-to-use intelligence.
Seven stages. One purpose. Transform the world's proprietary information into intelligence that frontier AI can test, verify, and learn from. Select a stage.
We identify proprietary information with potential training or evaluation value, and assess its uniqueness, completeness, provenance and possible applications.
Uniqueness and completeness
Provenance and modality
Relevance to model development
Demand-led or supplier-led
Algorithm 7 combines software, domain-specific models, rules, verification systems and expert review. Every derived task keeps traceability to its authorized source evidence. A successful evaluation shows something about the tested tasks and data quality; it does not by itself establish that training on the data will improve a model. Training uplift claims require a properly designed training experiment.
Explore Algorithm 7Pick a domain, then step through the three views: the raw source, the A7 evaluation built from it, and the asset a lab consumes under license. All examples are synthetic.
Intermittent checkout failures
Fragments scattered across systems, formats and people. Valuable, but not something a model can be trained or tested on as-is.
Testing a dataset and consuming it are separate products, under separate permissions. What a given dataset supports depends on its evidence and its rights.
An evaluation product that shows whether a dataset addresses your capability gap, before you buy it.
Task definitions and success metrics
Representative authorized samples
Hidden reference answers
Deterministic or expert-reviewed graders
Quality and coverage report
Provenance and rights record
The larger machine-consumable dataset, under agreed license terms once it has qualified.
Structured and linked multimodal records
Supervised examples and outcome labels
Expert corrections and annotations
Original permitted evidence
Metadata and provenance
Schemas and documentation
Controlled environments where agents use tools and are evaluated against defined completion conditions.
Simulated systems seeded from real workflows
Tool definitions and permitted actions
State-based completion checks
Reference trajectories
Built only where the data supports it
Scoped per engagement
Written for research directors, procurement leads and data-acquisition teams alike.
I
Tell us what the model needs to learn.
II
We identify and condition relevant proprietary information.
III
We translate it into a testable asset.
IV
Your team evaluates it against its requirements.
V
You license and consume the data if it meets your needs.
Algorithm 7 is industry-agnostic. What matters is the kind of evidence a dataset holds and the capability it can test. These are potential source categories, not a claim that Algorithm7 currently holds data in each.