Data mesh and data products streamline data management and collaboration, enhancing the scalability, quality, and versatility of analysis and generative AI applications while improving security and reducing latency. Tag.bio technology simplifies data preparation, supports iterative development, and ensures reliable, high-quality data for generative AI models, ultimately leading to more effective and efficient AI systems.
Tag.bio data products are the fundamental building block of the data mesh architecture and implement a domain-driven design to ensure that each data product is usable by anyone.
Data products represent a harmonized, composable application layer on top of disparate, multimodal data sources. Along with employing a universal “Smart” API, they also present a simple, clean, standardized data model for apps, data scientists, and AI agents who perform queries and extract data frames. Data products have the following features:
Ownership of functionality is shareable with domain teams and business units.
All data snapshots, mapping, statistics, ML models, visualizations and reports are versioned for analysis provenance and perfect reproducibility.
Each data product presents its data model to algorithms, visualizations, BI tools, and data scientists as a single, sliceable data frame with common vocabularies and ontology concepts.
Disparate data products can map into an archetypal data model, which enables instant transfer of algorithms and data consumption methodologies.
Each data product utilizes its own git repository (or archetype repository) and is deployed as a Docker container into a robust, fully-managed Kubernetes compute infrastructure.
Data products can be designed, managed, and tested independently, minimizing overhead and maintenance costs as data and usage evolves.
Connect to multiple data technologies – CSV, TSV, SQL, Parquet, data warehouses, and data lakes.
For CDISC, OMOP, EHR, RWE, DNA-Seq, RNA-Seq (incl. single-cell).
Using JSON or YAML to deploy a harmonized data product for novel data sources.
Algorithms, visualizations, reports, ML, and LLM apps.
All disparate, composable data products speak the same API language; specific functionality is enabled using callable methods through the API.
Anyone can utilize the Tag.bio web application to create and share cohorts, statistics, visualizations, reports, and machine learning models – with versioning and provenance.
Data scientists and AI agents can access any data product (with authorization) to easily slice and extract data frames for ad-hoc analysis & visualization and building of machine learning and generative AI models.
Data analysts, AI agents, BI tools, and other software clients can connect to a data product’s restful API or SQL API to slice and extract data frames for downstream use.
Bring the compute to the data (pro-code) – implement pluggable algorithms and visualizations with R scripts, Python scripts, RMarkdown templates and Python notebooks.
Enables fast algorithms, visualization, and ML processing of data.
PNG, PDF, HTML, Plotly, RMarkdown, Python notebooks.
Data products can serve API methods and apps for prediction, classification and generative AI from structured queries or exploratory prompts.
Model-agnostic, by design
Tag.bio doesn't lock you into one model. Point any foundation model — OpenAI, Claude, Gemini, or an open model like Qwen or DeepSeek — at a data product, and it can build, query, and reason over it the same way. Biology-specialized models get the same access: protein-structure models like AlphaFold3 and ESMFold, or biomedical language models like BioBERT and ClinicalBERT, can invoke the same protocols and ask the same ad hoc questions. The data product layer is the constant; the model is your choice.
How AI builds and uses data products
Feed AI your raw, disparate sources and it assembles the data product: mapping fields, applying a standard data model, versioning as the underlying data changes. What used to be a data engineering project becomes a starting point.
Once a data product exists, AI can invoke a protocol (a proven, reusable analysis) or run an ad hoc query directly against it. Either path stays inside the governed layer — there’s no separate export, no shadow copy of the data floating around in a notebook.
Agentic AI
As AI moves from answering one question to running a chain of them, that chain needs to run against something governed. Tag.bio's data products are built to be that ground: autonomous agents connect through MCP, invoke protocols or run ad hoc queries, and hand results to other agents — including agent-to-agent communication across a federated network of agents working the same data product layer. Retrieval stays grounded in the data product itself, not a loose index of exported files, so an agent chaining five steps together is still logged, auditable, and tied to the data it actually touched at every step.
Protocols
A protocol is a defined, reusable analysis that runs inside the data product layer instead of inside your AI’s compute budget. Encode a validated analysis once, and every future request for it — whether it comes from a person or an AI agent — runs the same way, on the data product itself. That means faster answers, lower AI compute costs, and one less place for inconsistency to creep in.
MCP
Tag.bio can front all of your data products with a single MCP server, so any AI system that speaks MCP — including Claude — can discover and query them directly. Every connection, every query, is scoped and logged.
Want to see it before you build anything? We host a live MCP server at demo.tag.bio that you can connect to today with a free Claude connector (limited usage) — a real data product, not a sandbox with fake data.
Reproducibility and auditablity
Anytime AI interfaces with a data product — a protocol run or an ad hoc query — it’s logged: the user, the request, the result. That history is auditable and reproducible by design, which matters as much for a regulatory review as it does for a colleague trying to pick up where you left off.
Because the data product sits on top of your existing storage infrastructure rather than duplicating it, it becomes a single ground truth — the version of the data that any analysis, dashboard, or AI agent can be pointed at with confidence.
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Leverage any data source (or combination of sources) to instantiate governed, versioned, containerized, git-backed data products.
Use built-in enterprise CI/CD process to deploy containerized data products in your secure compute environment (AWS, Azure, GCP, Hybrid, On-prem).
Harmonized data product APIs facilitate universal, governed access via frontend applications and data science environments (IDEs, notebooks).
