Blog
Tokenmaxxing Is Not Efficiency: How to Measure AI Productivity
Token counts and lines of code are easy to count and easy to game. Goodhart's law explains why AI productivity metrics fail and what to track instead.
By James WinegarRead
Most AI projects fail because they start with the model, not the problem.
We assess your data, infrastructure, and use cases to find the AI projects worth building and tell you which ones are not ready yet.
AI Strategy & Readiness
Every company is being told they need an AI strategy. Few are being told what that means: evaluating which problems AI can solve profitably, ensuring the data foundation exists to support it, and building the organizational muscle to govern AI systems over time. The starting point is the same whether the board is asking about AI, a previous project stalled, or LLM costs are spiraling: a structured assessment of what is realistic before you invest.
2-6 weeks
Typical readiness assessment turnaround
UC Berkeley
Where our co-founder teaches ML at Scale and MLOps
12
Dimensions audited across CorrDyn's assessment framework
Use Cases
Engagements where this is the right work.
The board says "do AI"
Your leadership wants an AI strategy. You need a way to separate the projects with real ROI from the ones that sound good in a pitch deck. We build that evaluation framework.
AI pilot stalled or failed
You built a proof of concept and it never made it to production. The model worked in a notebook but fell apart on real data, or the team lost confidence and moved on. We diagnose what went wrong, whether the use case is still worth pursuing, and what needs to change before you try again.
Build vs. buy evaluation
Should you fine-tune a model, use an API, or buy a SaaS product? The answer depends on your data, your team, and your tolerance for maintenance. We map the tradeoffs so you choose with data, not hype.
Prioritize across competing AI use cases
Your team has a dozen AI ideas and no framework for deciding which ones to fund first. We score each use case against data readiness, expected ROI, implementation complexity, and organizational capacity so you invest in the projects most likely to deliver.
AI vendor evaluation
You have AI tooling pitches stacking up but no rubric. We score vendors against your data, your team, and your actual use cases, not the demo script.
Our Process
A structured approach that delivers results at every stage.
01
Score Your AI Readiness Across Twelve Dimensions
We use the CorrDyn Data Operating Model (CDOM) to evaluate your organization across twelve dimensions of data maturity, viewed through an AI & ML Readiness lens. The primary dimensions are AI readiness (do you have prioritized use cases and evaluation frameworks?), data strategy (is data being deliberately collected as fuel for AI, or is it an accidental byproduct?), and governance and quality (AI amplifies bad data). Supporting dimensions include storage and modeling, security and privacy, and internal capability readiness. Each dimension is scored against three maturity levels, and we run both technical and executive versions of the assessment to surface alignment gaps between leadership vision and operational reality.
Assessments typically take 2-6 weeks, though complex organizations with many stakeholders or broad scope can take up to 12 weeks.
Output: AI readiness scorecard with dimension-by-dimension write-up
02
Deliver a Prioritized Roadmap
The assessment produces a scorecard with current and target maturity per dimension, a gap analysis, and a written report with specific findings: which AI use cases have the data foundation to support them today, which ones need infrastructure work first, and which ones are not worth pursuing. We apply A Map of Where AI Creates Value in Your Business to identify which value mechanisms matter most for your business, then our 7 Principles framework for evaluating LLM business cases to prioritize specific use cases within those mechanisms by ROI and feasibility. Each recommendation includes a realistic timeline, resource requirements, and a cost-benefit analysis. For a candid look at where AI claims fall short in practice, listen to our conversation with Ben Locwin on separating AI hype from reality in biotech.
Output: Phased AI roadmap with use-case shortlist and budget bands
03
Bridge to Implementation
Strategy without a path to execution is a slide deck. We hand off directly to our data engineering and machine learning teams, or we work alongside your internal team to execute the highest-priority projects. The architectural side of that handoff follows the staged framework in The Context Layer: What to Build, What to Skip, and Where to Start. For organizations that need ongoing guidance, we provide advisory support through the first implementation cycle and quarterly reassessments through the same CDOM lens to track maturity progression. The assessment is designed to be actionable whether you engage us for the build or not.
Output: Pilot scope, success metrics, and go/no-go gates
Technologies
We pick the right tool for the problem, not the other way around.
AWS
GCP
Azure
Snowflake
BigQuery
Databricks
MotherDuck
DuckDB
Dagster
dbt
Anthropic
OpenAI
Google Gemini
Self-Hosted
scikit-learn
PyTorch
Hugging Face
MLflow
OptunaInsights
Perspectives from our team on ai strategy & readiness.
Blog
MotherDuck Guides: What to Put in Them, and What to Push Down
MotherDuck Guides are a routing layer for analytics agents, not documentation. What to put in them, what to push down, and how to keep them correct.
By Ross KatzRead
Blog
The Context Layer: What to Build, What to Skip, and Where to Start
A staged framework for deciding which data architecture components AI agents need, when to build them, and why, based on the value you are creating.
By Ross KatzRead
Blog
A Map of Where AI Creates Value in Your Business
Use case lists are not strategy. A mechanism-based framework for identifying where AI creates value, organized by what the AI is doing.
By Ross KatzRead
Frequently Asked
Questions
How do you evaluate whether AI is right for our use case?
We start with the business outcome, not the technology. We map your use case against criteria like data availability, decision frequency, error tolerance, and maintenance capacity. If a rules-based system or a well-designed SQL query solves the problem, we will tell you that and save you the AI investment.
What does a readiness assessment include?
We evaluate four areas: data quality and accessibility, infrastructure and compute capacity, team skills and organizational readiness, and use case prioritization. You get a written report with specific findings, a prioritized list of AI opportunities ranked by ROI and feasibility, and a roadmap for closing any gaps.
We already have data scientists. Why do we need help with strategy?
Data scientists are great at building models. Strategy is about deciding which models to build, in what order, and how to measure success. We work with your data science team to connect their technical capabilities to business priorities and make sure projects get the organizational support they need to go to production.
How do you handle AI cost management?
LLM costs are usage-based and can grow unpredictably. We audit your current AI spend, identify patterns like redundant API calls, oversized models for simple tasks, and missing caching layers. Then we restructure for cost efficiency by choosing the right model size for each task, implementing prompt optimization, and adding cost controls.
What is the relationship between AI strategy and data engineering?
AI is only as good as the data it runs on. Most AI strategy engagements surface data quality or infrastructure gaps that need to be addressed first. We often work on both in parallel, building the data foundation while identifying and prioritizing AI use cases so you are ready to move as soon as the data is.
Do you recommend building custom models or using APIs?
It depends on the use case. API-based models (GPT-X, Claude, Gemini) are right for general-purpose tasks like summarization, classification, and content generation. Custom or fine-tuned models make sense when you need domain-specific accuracy, data privacy, or cost predictability at scale. We evaluate both paths and recommend based on your specific constraints.
Want to invest in AI without wasting the budget?
We’ll assess your data, your use cases, and your team — and tell you what’s worth building today.
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Machine Learning & AI
From predictive models to agentic AI workflows and MCP integrations, CorrDyn builds ML and AI systems that deliver measurable business outcomes.
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Data Engineering
Build reliable, cost-effective data pipelines on AWS, GCP, and Azure. CorrDyn designs and implements data infrastructure that scales.
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Data Quality & Governance
Fix the trust problem in your data. CorrDyn implements testing, validation, governance frameworks, and metric definitions your organization can maintain.
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Technology Strategy
Get the technology leadership you need without a full-time hire. CorrDyn provides fractional CIO services, compliance guidance, and data strategy.
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