Data Strategy

The right data strategy turns internal data into revenue, makes AI initiatives actually work, and protects the organization when regulators or buyers ask hard questions. We have built and governed large-scale data pipelines ourselves, and we bring that experience to designing data governance frameworks, managing model lifecycles, and implementing AI model escrow for our clients.

We have sourced data from dozens of providers, built ingestion pipelines processing hundreds of terabytes, designed quality validation systems, negotiated licensing terms, and shipped the results to Fortune 500 customers. When we advise on data strategy, the recommendations come from having done the work firsthand — not from a framework we read about.

Starting at $25K | 2-12 weeks

Services

Service	Description	Timeline
Data Governance & Quality	Assessment and implementation of data governance frameworks, data quality and observability systems, and AI pipeline preparation. Structure that scales with your AI ambitions.	4-8 weeks
AI/ML Model Governance	Lifecycle management for AI models — inventory, risk tiering, validation, monitoring, drift detection, and retirement processes. Model cards and evaluation benchmarks included.	Ongoing or project-based
AI Model Escrow	Model weights, training data manifests, inference pipelines, and governance documentation held in escrow. Protects your organization when vendor relationships change. We pioneered this concept in 2022.	Setup + ongoing
Data Product Development Advisory	Architecture, monetization strategy, licensing, and quality standards for treating internal data as a product. Defined SLAs, versioning, and consumer support.	4-12 weeks
AI Model Documentation	Model cards, system descriptions, and training data summaries meeting EU AI Act and ISO 42001 requirements. Documentation that satisfies regulators and auditors.	2-4 weeks

Data Governance & Quality

Assessment and implementation of data governance frameworks, data quality and observability systems, and AI pipeline preparation. Structure that scales with your AI ambitions.

4-8 weeks

AI/ML Model Governance

Lifecycle management for AI models — inventory, risk tiering, validation, monitoring, drift detection, and retirement processes. Model cards and evaluation benchmarks included.

Ongoing or project-based

AI Model Escrow

Model weights, training data manifests, inference pipelines, and governance documentation held in escrow. Protects your organization when vendor relationships change. We pioneered this concept in 2022.

Setup + ongoing

Data Product Development Advisory

Architecture, monetization strategy, licensing, and quality standards for treating internal data as a product. Defined SLAs, versioning, and consumer support.

4-12 weeks

AI Model Documentation

Model cards, system descriptions, and training data summaries meeting EU AI Act and ISO 42001 requirements. Documentation that satisfies regulators and auditors.

2-4 weeks

Why us

We have done the hard parts firsthand

Finding reliable data sources. Building pipelines that handle inconsistent formats at scale. Designing quality checks that catch problems before customers do. Negotiating licensing across dozens of providers. We have been through every stage of data product development — the recommendations we give come from solving these problems ourselves.

AI Model Escrow is ours

Software escrow has existed for decades. AI model escrow — covering weights, training data manifests, inference pipelines, and governance documentation — is a concept we pioneered in 2022. As organizations depend more on third-party AI, escrow protects against vendor lock-in, acquisition disruption, and compliance gaps.

Data governance and AI governance are one problem

Most firms treat data governance and AI governance as separate workstreams with separate teams. We treat them as one continuum — because your data quality, lineage, and provenance decisions directly determine your AI risk profile. One team, one engagement.

Why licens.io?

	Big 4	licens.io
Data experience	Advise from theory	Built 132M+ doc dataset for KL3M
Model escrow	No offering	Pioneered the concept in 2022
Copyright context	General awareness	Track 70+ lawsuits, built compliant data
Integration	Separate practices	Data gov + AI gov as one continuum
Pricing	Hourly, $200-400/hr	Fixed-fee, $25K-$100K

Data experience

Big 4

Advise from theory

licens.io

Built 132M+ doc dataset for KL3M

Model escrow

Big 4

No offering

licens.io

Pioneered the concept in 2022

Big 4

General awareness

licens.io

Track 70+ lawsuits, built compliant data

Integration

Big 4

Separate practices

licens.io

Data gov + AI gov as one continuum

Pricing

Big 4

Hourly, $200-400/hr

licens.io

Fixed-fee, $25K-$100K

Who this is for

✓ Data-rich enterprises adopting AI that need governance frameworks before their data becomes a liability
✓ AI-native companies needing model lifecycle management, documentation, and drift monitoring
✓ Organizations dependent on third-party AI that need model escrow to protect against vendor risk
✓ Companies treating data as a product that need architecture, quality standards, and monetization guidance
✓ Boards evaluating vendor AI risk across their organization's AI dependencies

Frequently asked questions

What is AI model escrow vs. software escrow?

Traditional software escrow stores source code. AI model escrow stores model weights, training data references, configuration files, and deployment specifications. If a vendor fails, is acquired, or discontinues a model, escrow ensures you maintain access to the AI capability your business depends on.

Do I need data governance before adopting AI?

Yes. AI systems amplify data quality problems — garbage in, garbage out at scale. Without data governance covering quality, lineage, access controls, and lifecycle management, AI initiatives consistently underdeliver or introduce unmanaged risk.

How does AI model escrow work in practice?

A neutral third party holds copies of model artifacts (weights, configs, training data references) under defined release conditions. If the vendor triggers a release event (bankruptcy, discontinuation, material breach), the escrowed materials are released to the customer.

What should a data governance framework include for AI?

At minimum: data quality standards and metrics, data lineage and provenance tracking, access controls and privacy compliance, retention and lifecycle policies, and specific provisions for AI training data including copyright and consent documentation.

What is a data product?

A curated, governed, and documented dataset or data service designed for repeated use by multiple consumers. It treats data with the same product management discipline applied to software — defined quality SLAs, versioning, and consumer support.

How do you manage model lifecycle?

Model lifecycle management covers inventory and registration, risk tiering, validation and testing, deployment approval, ongoing monitoring (drift, performance, bias), retraining triggers, and retirement processes. We establish these processes and the governance structures to sustain them.