AI-augmented data harmonization
for life sciences
- You've invested in high-quality data
- You've invested in the right data models, ontologies, and standards
- You've invested in the people and tools that turn data into evidence
From source to analysis-ready data
Combining data from multiple sources has always been a challenge, often taking weeks or months before datasets are ready for analysis. Rules-based pipelines like ETL scripts can help, until they meet a new source, a new interpretation of the data, or a new version of a standard. A2K closes that gap by pairing the efficiency of a rules-based engine with the flexibility of AI-enhanced interpretation.
- Before any data arrives, A2K translates your data model, ontology, or standard into a specification pack.
- The specification pack is A2K's proprietary, AI-optimized schema: deterministic rules, plus the principles and examples that guide the AI model.
- Each source then delivers the same information in its own structure: different tables, layouts, and column names.
- Deterministic rules settle what the standard defines.
- The AI model handles what differs between sources: what each column holds and how each table is laid out.
- The specification pack bounds every model decision. What cannot be settled is returned as unknown and flagged for review.
- Every change carries its source, the method applied, and a confidence grade.
- Decisions that need your judgment are listed for your review.
- A2K builds a specification pack for your standard, pairs deterministic rules with an AI model inside its limits, and records the provenance of every change.
A2K translates any data model, ontology, or standard into an AI-optimized schema and set of specifications.
It uses those specifications to map and harmonize every file, variable, and value across your source data.
It returns harmonized, analysis-ready data, with a full audit and provenance trail and the decisions that need review flagged.
Makes your data models actionable
A2K takes the data models, ontologies, and controlled terminologies you've already invested in and turns them into a working harmonization capability, without your teams building or maintaining an AI pipeline.
Consistent application
A2K applies your data model the same way across every source and dataset, so you get comparable, analysis-ready data instead of local variants and drift.
Runs in your environment
A2K can run inside your own cloud environment, so your source data stays within your compliance perimeter. A2K retains no data between runs, never trains on your data, and can be configured with your model provider of choice.
Built for scale and security
Scale and architecture
- Extensible without engine changesA new data model, ontology, or data type is added as a specification pack. The core agentic pipeline is data-type agnostic, so engine code stays separate from the specifications that run the pipeline.
- Scalable to hundreds of datasetsThe stateless, containerized engine runs pipelines in parallel against a shared, read-only specification set and common tools. Workers spin up on demand to match volume, then release when done.
- Intelligent model routingDecisions escalate by difficulty to the appropriate model reasoning level. To protect privacy and client confidentiality, the model sees only the information needed to make each decision, never an entire dataset.
- Audit trailEvery platform output carries a per-cell provenance record: the decision made, the method applied, and an audit trail back to the source for every value.
Trust and security
- No client data retainedThe engine processes each run, returns the output, and keeps no client data between runs. Client data is never used to train models.
- Model-provider abstractionA2K's engine can be configured to use your organization's enterprise model provider. For hosted and API access, A2K uses Anthropic's Claude models.
- Runs in your compliance perimeterA2K can deploy inside your own cloud account, with encryption in transit and at rest. Inference runs against a managed model endpoint in your environment, such as Amazon Bedrock or an equivalent service on another cloud, so nothing leaves your environment, including model traffic.
- Governed engineeringEvery change is specified, reviewed, and tested before it ships. Engine work is built against written specifications and checked by adversarial design review. Every change is approved by a person before it merges and must pass CI-gated regression against a synthetic answer key. New builds are validated by rerunning full synthetic datasets and comparing results before and after. Architecture decisions are documented.