For CDOs, CAOs & data leaders
The board wants safe AI. Your PHI data isn’t governed for it.
You’re measured not just on infrastructure, but on trusted insights and safe AI. We close the gap between your current data reality, silos, quality issues, PHI/PII constraints, and the AI promise your executives are asking for.
For healthcare, life sciences & other regulated data environments
Fragmented source data
→ EHRs, warehouses, lakes, spreadsheets
Map & classify
→ PHI/PII tagged, lineage & owners
AI-ready data products
→ Curated, governed views, not raw tables
Safe LLMs & agents
→ Scoped retrieval, logged, defensible
The reality
What data leaders tell us they’re actually facing.
The same realities, every week, from CDOs, CAOs, and data leaders in regulated industries.
Data isn’t ‘AI-ready’ yet
Clinical, operational, and financial datasets are fragmented and locked inside legacy systems. Everyone wants AI, but you can’t build it on a foundation you don’t trust, and the longer it slips, the more credibility your data team loses.
PHI/PII everywhere
Healthcare and regulated datasets are full of PHI/PII. One ungoverned prompt and that data lands in a model you can’t audit, and you own the breach, the disclosure, and the board conversation that follows.
Demand for AI outpaces data-team capacity
Requests for dashboards, models, AI POCs, and agents arrive faster than you can govern them. So shadow AI ships without you, and you inherit the governance debt long after the demo.
Shadow pipelines & one-off integrations
Teams stand up parallel pipelines and spreadsheets to move fast, quietly eroding any ‘single source of truth.’ Decisions then get made on numbers no one can reconcile, and no one can defend in an audit.
Pressure to show value, not just infrastructure
Eighteen months of platform spend, and the board still can’t point to one AI outcome. ‘We modernized the data stack’ stops being an answer, and the question lands on you.
This page is about how we help you move from that reality toward AI-ready, governed data that underpins sustainable AI.
How we help
Make your data strategy the enabler of responsible AI.
One path, three steps: clarify what’s ready, design the data products, then enable AI on top, governed end to end.
Key questions
The questions that decide whether AI delivers value, or disappoints.
Which data domains can safely power AI in the next 6–12 months?
We map your current data landscape against AI readiness, risk, and value to prioritize where to start.
How do we expose data to LLMs without losing governance?
We design controlled interfaces, retrieval, APIs, curated views, so models never see more than they should.
What is our minimum viable AI governance model for data?
We define practical policies, controls, and processes that fit your size and regulatory context.
How do we reduce duplicated pipelines and shadow data?
We identify where AI and automation can reinforce standard patterns rather than introduce new silos.
How do we give leadership credible, realistic AI roadmaps?
We structure AI roadmaps grounded in your current data maturity, not in generic hype.
From silos to data products
We build AI-ready data products on the platform you already run.
Map & classify → design AI-ready data products → expose safely to AI & agents. Pick your platform to see how that path runs.
Databricks lakehouse
We map and classify what lives where, define AI-ready data products with owners and lineage in Unity Catalog, then expose them safely to LLMs and agents via scoped retrieval, with logging and controls.
- Unity Catalog governance
- Delta tables & lineage
- Feature & vector stores
- Curated, access-controlled views
Privacy, PHI/PII & regulated data
Your data environment isn’t just complex, it’s regulated.
We design with that as the starting point, and we’ve done it at scale.
118
locations of operational data mapped & access-scoped before any model call
1,940+
associates whose data access we scoped for safe AI enablement
3
weeks to the first governed data-access pattern, live
A multi-billion-dollar distribution enterprise (2,000+ associates · 122 locations), 2025, we mapped and access-scoped the operational-data estate, then stood up the first governed data-access pattern so AI could be used safely. How we govern regulated data.
- Regulated-first, HIPAA-aligned by design, BAA available. We assume your data is sensitive until proven otherwise, especially in healthcare and life sciences.
- PHI/PII minimization, de-identification or pseudonymization before model exposure, and data stays in your environment.
- NIST AI RMF-aligned delivery with audit logging on every model call, mapped to your data classification, retention, and access policies, not a separate universe of rules.
- Joint design with your security, legal, and compliance stakeholders from early on, backed by SOC 2 readiness documentation.
Our governance posture
- NIST AI RMF-aligned delivery
- HIPAA-aligned, BAA available
- SOC 2 readiness documentation
Working together
You own the data strategy. We help unlock its AI potential safely.
Partner, not compete
We don't replace your BI, analytics, or data-engineering teams. We add specialized governed-AI and automation expertise on top.
Use your platforms, not ours
We prefer to work with the warehouses, lakes, and tools you already have: Databricks, Snowflake, Azure, and the rest.
Co-create reusable patterns
We define reusable patterns for retrieval, feature preparation, and PHI handling that your team can apply across projects.
Documentation & handover
We leave behind architectures, diagrams, and how-tos your team can maintain and extend.
Go deeper
Resources for data leaders.
Representative use cases
- Preparing clinical data for LLM-driven summarization
- AI-ready data products for operational analytics & automation
- Governed agentic workflows across a Databricks / Snowflake platform
Straight answers
CDO & data-leader questions
Do you help us define an AI data strategy, or just build?
Both. We start with clarity on AI-ready data domains and governance, then move into hands-on design and implementation where it makes sense.
Can you work with our existing data platform (Databricks, Snowflake, etc.)?
Yes. We prefer to build on your existing platforms rather than introducing new core data stacks.
How do you ensure that LLMs don't see more data than they should?
We rely on controlled retrieval patterns, scoped queries, and alignment with your data classification and access policies.
Can you help us with de-identification or PHI/PII handling?
We support pattern and architecture design, and work with your teams to implement de-identification and minimization strategies that fit your policies.
Do you build dashboards and standard BI, or only AI/agents?
Our primary focus is governed AI, agentic automation, and the AI-ready data those use cases need. Traditional BI is usually covered by your existing teams or partners.
What size organizations do you typically work with?
Primarily mid-market and regulated organizations, roughly in the $50M–$500M revenue range or equivalent complexity.
Do you require long-term retainers?
We often start with a scoped assessment or focused implementation. Longer-term governance or optimization retainers are an option, not a requirement.
How will this impact our existing data roadmap?
We aim to align with and accelerate your roadmap, not derail it, prioritizing AI-related foundations that also benefit analytics and reporting.
Start here
Make your data truly 'AI-ready', not just stored.
PHI/PII, data quality, pressure to deliver AI outcomes, bring them to a 30-minute working session and we'll map a realistic, governed path from your current data landscape to high-impact AI use cases.
+1-732-433-5564 · info@kriv.ai · East Brunswick, NJ