Algorithm7 converts rights-cleared proprietary information into structured evaluation tasks, verifiable benchmarks and model-ready training assets. Assess the dataset against your capability requirements before committing to a full license.
Proprietary datasets are usually sold on a description and a sample. Your team then spends months finding out whether the data is relevant, clean, legal to use and in a format you can consume. Algorithm 7 answers those questions before you buy.
Relevant to a capability gap
Mapped to the skill you're trying to move, not just the domain.
Structurally usable
Linked, normalized and documented against a schema.
Representative of hard cases
Real failures, corrections and edge conditions, not curated happy paths.
Documented and legally usable
Provenance and permitted use recorded per record.
Measured quality
Completeness, fidelity and label accuracy reported, not claimed.
Suitable for evaluation
Checkable answers, separated from the context a model sees.
Delivered in your format
Shaped for your training or evaluation infrastructure.
See it in an evaluation pack.
Open the exampleWhat Algorithm 7 produces
Algorithm 7 is the seven-stage process that turns raw proprietary data into structured, testable assets, so you evaluate before you commit to consuming at scale.
Explore Algorithm 7Data quality, benchmark performance and learning gains are often blurred together in data sales. They are measured differently, and only the first two come out of an A7 Test.
Data quality
Is the data complete, faithful to its source, consistently labeled and legally eligible?
Reported as: coverage, completeness, label agreement, leakage checks, rights status.
Benchmark performance
How do your current models score on tasks derived from the data? Where do they fail?
Reported as: task scores against hidden references, by task type and difficulty.
Demonstrated learning gains
Does training on the data actually improve the model? A test can't answer this on its own.
Established by: a controlled experiment with held-out evaluation, which we can help design under a pilot license.
Switch between the three views of one pack. The model-facing view never contains the reference answer. Examples are fictional and shown for demonstration.
Repository snapshot at the incident commit, service logs up to the alert window, and the incident ticket text. No later commits or postmortem.
Identify the root cause of intermittent checkout 5xx errors and propose a code change.
A unified diff and a one-paragraph diagnosis, in the response schema provided.
Every task Algorithm 7 produces follows the same spine, so tasks from different sources train and evaluate together. Temporal fields only appear where the source is a workflow over time.
Which families a dataset can support depends on the evidence it contains and what its source permissions allow. We say so in the evaluation, before you license.
Evaluation approaches
We'll scope an evaluation against your requirements before any license is discussed.
You don't need to share confidential model details. A description of the capability and how you'd measure it is enough to start.
Request an Evaluation