New product development

Deliver Data Products - Faster, Smarter, and AI-Ready 

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Make your data usable, traceable, and ready for delivery across teams, systems, and partners with trusted, reusable data products designed for business impact

Why data infrastructure alone falls short

Enterprise data projects fail when they stop at pipelines.

Traditional approaches focus on ingestion and modeling but leave teams rebuilding logic, reconciling mismatched fields, and duplicating effort across functions. Without a product mindset, data remains underused and difficult to trust. 

Common problems: 

 

Point-to-point pipelines
Point-to-point pipelines tailored for every team and use case
Poor metadata
Poor metadata and documentation across projects
Fragile codebases
Fragile codebases without ownership or lifecycle
No audit trail
No audit trail for transformations or source provenance
Data teams
Data teams overwhelmed with endless bespoke requests

The result? Inconsistent outputs, slow time-to-value, and rising data debt. 

Where Datavid fits

Deliver AI-ready data products with built-in governance and reuse.

Datavid helps enterprises transform raw data into reusable, scalable data products designed to support AI, foster collaboration, and ensure compliance. 

Built for the challenges you face: 

Fragmented pipelines and ungoverned data
Fragmented pipelines and ungoverned data
Regulatory pressure
Regulatory pressure and data traceability
Poor reuse
Poor reuse across domains and projects
LLM readiness gaps
LLM readiness gaps
Costly rebuilds
Costly rebuilds across business units
Arrow pointing to consequences

Our solution:

Define, document, and deliver reusable data products with shared contracts, owners, and lifecycle support
Define, document, and deliver reusable data products with shared contracts, owners, and lifecycle support
Include lineage, metadata, validation logic, and version history in every product by default
Include lineage, metadata, validation logic, and version history in every product by default
Semantic normalization, unified data models, and APIs designed for discovery and interoperability
Semantic normalization, unified data models, and APIs designed for discovery and interoperability
Pre-curated outputs with embedded context, explainability, and prompt-ready metadata
Pre-curated outputs with embedded context, explainability, and prompt-ready metadata
Modular templates, validation rules, and automation from Datavid Rover accelerators
Modular templates, validation rules, and automation from Datavid Rover accelerators
Quote
We needed a partner who could move fast, collaborate deeply, and adapt with us. Datavid proved to be the right partner helping us replatform and launch a production-grade product in 90 days.
Rodney Fulford, Assistant Director, Content & Technology Strategy - CAS
CAS logo

Want to see AI and semantics powering real-world innovation?

Discover how CAS and Datavid launched BioFinder in 90 days.

An AI-powered discovery tool built on enriched, reusable data products.

Practical Use Cases

Clinical Trial Harmonization

Clinical Trial Harmonization

Standardize CRF and non-CRF data across studies for faster analytics and regulatory reporting.
Read the case study
Scientific Publishing Search Products

Scientific Publishing Search Products

Deliver enriched, FAIR-compliant content products for literature search, recommendation engines, and GenAI.
Read the case study
Regulatory Data Products

Regulatory Data Products

Package compliant, lineage-rich data assets for regulatory submission and audit scenarios.
Read the case study
R&D and Real-World Evidence

R&D and Real-World Evidence

Structure and version observational datasets for internal use, partner APIs, or downstream ML.
Read the case study

From Flow to Value

From Raw Pipelines to Ready-to-Use Data Products. Here's what makes our approach work:
Explore Datavid Rover
Explore Datavid Rover
Book a new data product delivery strategy session 
Let's talk!
Semantic Data Architecture

Semantic Data Architecture

Harmonize messy data into structured, interoperable formats using ontologies and graph-based design.
Datavid Rover logo

Datavid Rover Accelerator

Prebuilt components for ingestion, validation, enrichment, and output packaging tailored to your domain.
Built-in Explainability

Built-in Explainability

Every product is delivered with lineage metadata, validation logic, and human-readable documentation.
FAIR by Default

FAIR by Default

Findable, Accessible, Interoperable, and Reusable: every product meets FAIR standards out-of-the-box.
Event-Driven Deployment

Event-Driven Deployment

Update and republish products based on new data, schema changes, or business triggers.
Fast Delivery

Fast Delivery

deliver structured outputs to regulators, or share domain-specific APIs - in weeks.

Real-world proof

CAS-2

AI-ready scientific publishing data products

Datavid partnered with CAS to launch BioFinder, an AI-powered scientific discovery product, built on enriched, reusable data products and delivered in just 90 days. 
What we delivered: 
  • Integrated 14 new data sources, including an $8.3M biomarker dataset, into structured, enriched content.
  • Applied large-scale semantic enrichment with MeSH, PubChem, & custom ontologies.
  • Introduced predictive analytics, such as metabolite predictions, using AI & semantic graphs.
  • Created reusable APIs & intelligent search endpoints to accelerate discovery & collaboration.
CAS Case Study case study

Frequently Asked Questions

How can Datavid make my enterprise data AI-ready for LLMs and generative AI?

Datavid transforms fragmented pipelines into governed, reusable data products enriched with semantics, metadata, and lineage. This ensures your data is structured, explainable, and “prompt-ready,” enabling LLMs and generative AI applications to deliver more accurate, trustworthy insights.

What’s the difference between raw pipelines and AI-ready data products?

Raw pipelines ingest and move data, but they lack governance, reuse, and explainability. Datavid’s AI-ready data products include built-in lineage, semantic normalization, validation logic, and FAIR compliance. This makes them discoverable, interoperable, and directly usable for AI, analytics, and regulatory needs.

How does semantic enrichment improve AI performance?

Semantic enrichment adds context by mapping data to ontologies, taxonomies, and knowledge graphs. This improves search, classification, and retrieval for AI systems, enabling more relevant responses, reduced hallucinations, and faster time-to-value in applications such as scientific discovery, clinical trial harmonization, and regulatory submissions.

Ready to build your new data product?

Whether launching a new AI search tool or submitting harmonized trial data to regulators, Datavid can help you define, build, and operationalize high-value data products faster than you thought possible.
Schedule a data product strategy session Book a Datavid Rover demo