Data
you can trust.

Senior data engineers, analysts, and architects as your dedicated team. We build the infrastructure, then make sure your people can run it without us.

Start a conversation See our work

Founded by James Winegar · Serving clients since 2015

AVIXA logoNLx (National Labor Exchange) logoBlitz Medical Billing logoThe Art of Education University logoCaption Partners logo

Services

Eight practices. One team. Senior practitioners who have done this before.

Server room cold-aisle interior: an open 42U rack revealing precise vertical bundles of blue and white CAT6A cabling descending through cable management arms into switches with green LED indicators.

Data Integration

Business-critical data siloed across systems? We unify your sources into a single platform your whole team trusts.

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Analytics & Reporting

Dashboards nobody uses are worse than no dashboards. We build reporting people actually open every morning.

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Office mid-renovation: a removed drop-ceiling panel exposing newly-pulled bundles of color-coded data cabling in fresh cable trays, ladder set up, blueprint rolled across two sawhorses.

Infrastructure Modernization

Legacy systems slowing you down? We migrate, refactor, and rebuild — without disrupting what's working.

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ATX workstation under a bench with a glass side panel revealing two dual-fan GPUs caught mid-rotation, copper heatpipes and braided power cables visible inside, a handheld digital wattmeter on the floor beside it.

Machine Learning & AI

From predictive models to agentic workflows and MCP integrations — we build the data foundation that makes AI possible, then deploy the systems that deliver ROI.

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Cost Optimization

Overpaying for Fivetran, Snowflake, or Databricks? We've saved clients 40-80% through architecture changes.

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Process Automation

If your team spends hours on manual reporting and reconciliation, we can eliminate that permanently.

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Team Augmentation

Need senior data talent without the 6-month recruiting cycle? We embed engineers who ship from week one.

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Strategic Advisory

Assessments, roadmaps, and workshops. We tell you what to build, in what order, and why.

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Tools We Use

Technology-agnostic. We pick the right tool for the problem.

AWSGCPAzureSnowflakeBigQueryDatabricksMotherDuckDuckDBRedshiftPostgres
MySQL
SQL Server
Cloud SQL
RDS
EstuaryDagsterFivetranAirflow
Prefect
dbtdltSQLMeshSparkTableauLookerPower BIOmni
Superset
Evidence
Rill Data
GrafanaTerraform
Pulumi
GitHub Actions
Anthropic
OpenAI
Google Gemini
Self-Hosted
React
Node.js
Django
FastAPI
Docker
Kubernetes
scikit-learn
PyTorch
Hugging Face
MLflow
Optuna

Let's make things happen

Contact us today to learn more about how we can help you take control of your data.

Get your free proposal

Our Working Process

We iterate to maximize value and help you on every step of the way.

01Define Success

We start by getting clear on what matters most. Together we establish your priorities, map a realistic path to value, and define the metrics that will tell us both whether it is working.

02Assess and Plan

We audit your people, platforms, and processes. We tell you what works, what does not, and where the highest-value opportunities are. Then we build a roadmap sequenced by ROI.

03Deliver

Quick wins first. We stabilize critical systems within weeks, then build toward the long-term architecture. You see results before the first invoice.

04Iterate

Every engagement includes feedback loops. We measure outcomes, adjust course, and scale what works. Your team gets more capable with each cycle.

Testimonials

“CorrDyn was instrumental in building out various solutions that reduced back-office operations and allowed our teams to focus effort on more strategic revenue generating activities.”

Will Cooper

CEO, Caption Partners

88% efficiency improvement
“We sought CorrDyn's help in filling the gap with knowledgeable data professionals who could help us break through the more challenging aspects of the data so that the ETL process was more efficient and effective.”

Sean Wargo

Sr. Director, Market Intelligence, AVIXA

“CorrDyn has been an exceptional partner that has efficiently scoped, understood, and delivered on all our requirements for the NLx Research Hub's public-facing dashboard.”

NLx Director

NASWA

100% availability of historical data
“CorrDyn's team integrated seamlessly with our organization. They didn't just fix our pipelines — they built a foundation that lets us make decisions we couldn't make before.”

Director of Strategy & Analytics

Professional Sports Organization

“CorrDyn's roadmap approach allowed for a comprehensive strategy that started to move the needle from the outset. We immediately began to see tangible results.”

Biotechnology Manufacturer

40% reduction in TCO

Case Studies

We're as good as our last project. Explore them below.

Dim office desk with a banded stack of window-envelope paychecks beside an open columnar ledger, calculator, coffee mug, and lamp

Embedded Analytics Modernization for a Payroll SaaS Platform

CorrDyn built an embedded analytics PoC for a payroll platform, achieving sub-second latency and 6-minute data freshness at 75% lower cost.

Read the full story
Office desk with February calendar showing a red-circled filing date, CMS forms, nurse visit schedule, and a regional map of care routes

Government Reporting Overhaul for a Home Care Platform

CorrDyn stabilized 100 mission-critical CMS reports for a home care company ahead of a hard February deadline, then transitioned to ongoing data team support.

Read the full story
Laptop on a service-desk displaying colored customer cluster scatter, with repair-order folders, keys, and desk lamp; dealership shop bay behind glass

ML Customer Segmentation for an Automotive Dealer Group

CorrDyn built an ML clustering pipeline on Databricks with LLM-powered profiling to segment customers across vehicle brands for a dealer group.

Read the full story
See all case studies

Industries

40+ clients across 17 industries. Cross-sector pattern recognition is our edge.

Biotech and Life Sciences ManufacturingConsumer Packaged GoodsE-Commerce and Direct-to-ConsumerEnergy, Utilities, and CleantechEnterprise TechnologyFinancial Services and Alternative InvestmentsHealthcare TechnologyIndustrial Services and DistributionInsurance and InsurTechLogistics and Supply ChainManufacturingNonprofit and Public Interest ResearchOnline Education and EdTechPharmaceuticals and Clinical ResearchProfessional Services and ConsultingProfessional Sports and EntertainmentSaaS and Subscription Companies

From the Podcasts

Recent conversations from Data in Biotech and Eventual Consistency.

Ross Katz & Jason Bradwell — The Consulting-Lab Land Grab: What Sits Above the Model
Eventual ConsistencyEp. 25

The Consulting-Lab Land Grab: What Sits Above the Model

with Ross Katz & Jason Bradwell

34:15
Ross Katz & Jason Bradwell — Similar Keynote, Different Platforms: Snowflake vs. Databricks
Eventual ConsistencyEp. 24

Similar Keynote, Different Platforms: Snowflake vs. Databricks

with Ross Katz & Jason Bradwell

43:14
Paul Finn — Synthesizable by Design: AI for Small Molecule Drug Discovery
Data in BiotechEp. 73

Synthesizable by Design: AI for Small Molecule Drug Discovery

with Paul Finn, Oxford Drug Design

59:29
Ross Katz & Jason Bradwell — Credence Goods, Junior Cuts, and the Audit Value Chain
Eventual ConsistencyEp. 23

Credence Goods, Junior Cuts, and the Audit Value Chain

with Ross Katz & Jason Bradwell

43:49
Arvind Rao — From Tissue to Mechanism to Decision: AI for Computational Oncology
Data in BiotechEp. 72

From Tissue to Mechanism to Decision: AI for Computational Oncology

with Arvind Rao, University of Michigan

46:54
Ross Katz & Jason Bradwell — If AI Can Do the Work, What Are Clients Actually Paying For?
Eventual ConsistencyEp. 22

If AI Can Do the Work, What Are Clients Actually Paying For?

with Ross Katz & Jason Bradwell

42:41
Sadegh Salehi — Cavities in the Data: Building FDA-Cleared AI for Dental Imaging
Data in BiotechEp. 71

Cavities in the Data: Building FDA-Cleared AI for Dental Imaging

with Sadegh Salehi, Overjet

58:42
Ross Katz & Jason Bradwell — Acceleration Without Stabilization: AI and Data Teams
Eventual ConsistencyEp. 21

Acceleration Without Stabilization: AI and Data Teams

with Ross Katz & Jason Bradwell

39:25
Jesse Johnson — Data as a Moat: Biotech's Most Valuable Asset Lives on a Hard Drive
Data in BiotechEp. 70

Data as a Moat: Biotech's Most Valuable Asset Lives on a Hard Drive

with Jesse Johnson, Merelogic

42:52
Jamil Bou Khair & Brian Manifold — Supply Chain Risk Became Everyone's Problem
Eventual ConsistencyEp. 20

Supply Chain Risk Became Everyone's Problem

with Jamil Bou Khair & Brian Manifold, Firezone

34:25
Kevin Brown — The Patient Is Not a Document: Foundation Models for Biomedical AI
Data in BiotechEp. 69

The Patient Is Not a Document: Foundation Models for Biomedical AI

with Kevin Brown, Standard Model Biomedicine

49:54
Ross Katz & James Winegar — What MCP Means for Enterprise Data Strategy
Eventual ConsistencyEp. 19

What MCP Means for Enterprise Data Strategy

with Ross Katz & James Winegar

54:34

From the Blog

Practical perspectives on data engineering, analytics, and AI.

Crystalline iceberg with a small luminous peak above the waterline and a massive fractured structure submerged beneath. The visible accelerated layer of data work above the compounding maintenance burden below.
aidata-teams

The Maintenance Trap: Why AI-Accelerated Data Teams Feel Slower

AI accelerates the 21% of data work that is code generation. The other 79% is maintenance, and every new asset makes it worse.

Ross Katz
Industrial rail bridges at dawn with luminous data orbs flowing through the infrastructure
aimcp

MCP: What It Costs, Where It Breaks, and When to Use It

MCP is now infrastructure. This is what it costs at enterprise scale, where the security model breaks, and how to route workloads deliberately.

Ross Katz
Governed data infrastructure versus ungoverned agent access — data flowing through a structured pipeline versus scattering across a broken bridge
aidata-engineering

Skip the Data Stack, Get the Wrong Answer Faster

AI agents querying raw source systems inherit every data quality problem the transformation layer solves — then present wrong answers with confidence.

Ross Katz
SaaSpocalypse diagnostic framework
saasai

SaaSpocalypse: Who Deserves the $300B Software Selloff

An 8-question diagnostic framework for assessing which SaaS businesses AI threatens, validated against 2026 YTD stock performance.

Ross Katz
Modular geometric structures docking together in mid-air — composable LLM architecture
llmmachine-learning

LLM Tools for Data Apps: Architectural Components

How to decompose LLM workflows into task components, break down complex RAG systems, and select tools using 10 evaluation principles.

Ross Katz
A beam of light split through a dark crystal prism into seven distinct wavelengths
llmmachine-learning

7 Principles: Which LLM Business Cases Will Work and Which Will Fail

A practical framework for evaluating LLM use cases in enterprise settings, separating hype from high-ROI opportunities.

Ross Katz
A fork in a luminous pathway — an open bridge diverging from an enclosed tunnel, data orbs choosing their route
llmmachine-learning

11 Questions: Open LLM vs Third-Party LLM API

A framework of 11 critical questions to evaluate whether your use case needs an open-source LLM or a third-party API like OpenAI or Claude.

Ross Katz
Light-painting long exposure of white and red streaks looping and crossing through darkness, suggesting tangled motion
biotechdata-strategy

How Biotechs Can Squeeze Value from Lazy Data

Why biotech data sits underutilized, why off-the-shelf solutions fall short, and how an external data team delivers quick wins under $150K.

Ross Katz
Long-exposure photograph of red and blue sparkler trails fanning outward from a single bright point against a black sky
biotechdata-engineering

6 Ways Biotech Manufacturers Can Unlock Data Value

A practical guide for biotech CMOs to unlock value from manufacturing data through process optimization, compliance, digital twins, and predictive maintenance.

Ross Katz

Frequently Asked
Questions

What if we decide to stop working together?
You can walk away at any time with no penalties or unwinding fees. Everything we've built is yours, and we'll make sure your team has what they need to maintain it. We'd rather earn your business every month than hold you to a contract.
How do you evaluate your success?
We evaluate every engagement by ROI and time-to-value. Our goal is to sequence work so that each step can be evaluated independently, as soon as possible. We can work with clients for years and be evaluated on our work every 2–8 weeks.
How do you keep clients updated?
We meet with our clients regularly (weekly, bi-weekly, monthly) to share progress, resolve blockers, review budget, and prioritize next steps. We establish chat-based partnerships in Slack or Teams for asynchronous communication and strive to respond within one business day.
What are common types of projects you take on?
Our work falls into three categories: optimization (improving performance, cost, security, and workloads of existing data systems), implementation (building new data pipelines, migrations, upgrades, and infrastructure as code), and augmentation (architecture advisory, training, and operations support to complement your team).
What differentiates you from other data consultants?
Over 80% of our business comes in by referral. Our average client relationship is roughly 5 years, and over 90% of our clients do multiple engagements with us. We are tech agnostic and solution-oriented: requirements drive technology choice, not vendor relationships.
How do you think about AI?
The companies getting the most out of AI are those with clean, well-structured, accessible data. That is exactly what we build. We help clients evaluate which AI use cases will actually deliver ROI, avoid the hype-driven projects that won't, and build the data infrastructure to support AI in production.
How long have you been in business?
We've been in business for over 10 years. We have 20+ people across 5 countries with an average of 9 years of experience, including 2 university professors on our team.
How do we get started?
The best way to get started is to have a conversation with us. We'll do a discovery call where we understand your needs and discuss the best way to work together.

Get your free
proposal.

Tell us about your challenges. We will be honest about whether we can help.

  • No pitch decks. We start by listening.
  • Discovery calls are free.
  • We respond within one business day.

Or email us directly at [email protected]

No sales scripts. No commitments.