DataFramer Advisory
Establish how AI should work across your business.
Work directly with our team to define the business outcomes, quality standards, auditability and operating model for important AI workflows.
YOUR AI SYSTEM
You build it. You own it. It keeps running as-is.
Traces · User & workflow signals · Business outcomes
Insights & improvements
YOUR AI OPERATING LIFECYCLE
Business outcomes • Expert standards
Auditability • Measurement • Improvement
What we help you establish
The questions leadership needs answered
AI changes how software is operated. Quality is probabilistic, models and workflows keep changing, and important decisions often require business judgment. Companies need a repeatable lifecycle that brings technical teams, domain experts, and leadership into the same operating model.
01
Business impact
What should this AI workflow measurably improve?
02
Automation priorities
Where is AI worth applying next?
03
Auditability
What needs to be recorded and reconstructable?
04
Expert judgment
Who defines what correct and acceptable means?
05
Change control
How do we know a model or workflow change helped?
06
Quality ownership
What should remain reusable across projects and vendors?
How it works
Establish the lifecycle once, starting with a workflow that matters. Then reuse the model across teams & projects.
01
Select
Choose the workflow and business outcome
02
Baseline
Understand how it works and how success is measured
03
Define
Set the audit trail, expert standard and controls
04
Handoff
Leave with a repeatable model your team can run
At the end of Advisory, the approach is yours. Your team can implement it internally, or bring us in to help put it into practice.
Credibility
We've built these systems ourselves.
Not a general AI consultancy. Our team has built monitoring systems, ML models, AI evaluation infrastructure and production AI quality systems at Fortune 200 companies.
Enterprise systems
ML models we trained
HDM-1
Hallucination detection
HDM-2
Enterprise hallucination evaluation
IFE
Instruction-following evaluation
Production deployment
Deployed in production at a Fortune 500 streaming company.
The team
Who you work with
Puneet Anand
Founder & CEO
Built monitoring products at AppDynamics, Salesforce and VMware.
Focus: Business outcomes, workflow intelligence, enterprise systems
Alex Lyzhov
Head of AI
NYU LLM Alignment Group. Built proprietary evaluation models and AI quality systems.
Focus: Evaluation, correctness, expert standards
Gabriel Marrocos
Infrastructure & DevOps
Ex-AWS.
Focus: Enterprise architecture, deployment and infrastructure
What you leave with
A working model your team can use
Five artifacts your team owns, runs and reuses across projects and vendors.
Business outcomes
What to measure
Workflow baseline
What happens today
Quality standard
What “good” means
Audit model
What to retain
Operating model
How it runs ongoing
Want help putting it into practice?
Bring us one AI workflow that matters.
We'll work with your team to establish how its business impact, quality and accountability should be measured.
Discuss a workflowNo platform commitment required.