Blog
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.
By Ross KatzRead
Everyone wants AI. Almost nobody is ready for it.
From predictive models to agentic AI workflows, we build ML and AI systems that survive contact with real data and real users.
Machine Learning & AI
Most AI projects stall between prototype and production. The Jupyter notebook works, but the model drifts once real data starts flowing, the outputs lose trust with stakeholders, and the team moves on to something else. CorrDyn builds ML and AI systems that survive that transition: predictive models in scikit-learn and PyTorch, LLM applications grounded in your data through RAG, and agentic workflows that connect language models to your business systems through APIs and MCP integrations. We handle evaluation pipelines, model monitoring, and the MLOps infrastructure that keeps production systems accurate over time.
1,000x
Efficiency gain in time-series analysis
6+
ML models deployed to production environments
19,000+
Meetings optimized via genetic algorithms in under 30 minutes
Use Cases
Engagements where this is the right work.
Customer segmentation and propensity modeling
Cluster your customers by behavior, predict churn or conversion likelihood, and generate actionable personas your marketing and sales teams can use immediately.
Predictive analytics and forecasting
Demand forecasting, anomaly detection, time-series analysis, and optimization models that improve operational decisions with data you already collect.
Integrate LLMs and RAG
Ground large language models in your proprietary data so they give useful, accurate answers instead of confident hallucinations.
Build agentic AI workflows
Connect LLMs to your business systems through MCPs, CLIs, APIs, or custom integrations, with human oversight and audit trails from day one.
ML model rebuilds and re-platforming
You have models in production written by people who left. We refactor them onto a maintainable platform with proper testing, monitoring, and CI/CD.
Our Process
A structured approach that delivers results at every stage.
01
Define the Business Case
We start with the business outcome, not the technology. Every ML or AI engagement begins by defining what you need the system to do, evaluating whether ML or AI is the right tool to get there, and assessing your data readiness. Each step has a clear deliverable and a decision point where you can course-correct before committing further.
Output: ROI model with success criteria and decision-quality threshold
02
Prove It With a POC
Before committing to a full build, we develop a proof of concept against your actual data to validate that the model's business objectives are achievable given your existing data quality and volume and your requirements for latency, cost, and accuracy. The POC answers the question that matters: can we get to production with what you have, or do we need to reframe the problem or adjust the constraints first?
Output: Working prototype with measured baseline against current process
03
Build and Validate
Once the POC confirms feasibility, we build. For traditional ML, that means training, evaluating, and iterating on models against your data. For LLM applications, we help organizations evaluate use cases against our proven framework, build RAG systems that ground LLM outputs in your proprietary data, and implement evaluation pipelines that ensure output quality meets your standards.
Output: Production-ready model with offline tests, an A/B framework, and bias audit
04
Engineer for Production
The gap between a working Jupyter notebook and a production ML system is enormous. We bridge that gap with engineering rigor: automated training pipelines, model versioning, A/B testing frameworks, and monitoring systems that alert you when model performance degrades. For agentic AI, that means maintainable integrations with your business systems through MCPs, CLIs, or APIs, with human oversight, audit trails, and guardrails built in from day one. We build ML systems that your team can operate, not black boxes that only we can maintain. For a deeper look at how MCP is reshaping enterprise data architecture, listen to our episode on what MCP means for enterprise data strategy.
Output: Versioned deployments, monitoring, retraining pipeline, and rollback
Insights
Perspectives from our team on machine learning & ai.
Blog
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.
By Ross KatzRead
Blog
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.
By Ross KatzRead
Blog
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.
By Ross KatzRead
Results
Real outcomes from real engagements.
Automotive / Retail
ML Customer Segmentation for an Automotive Dealer Group
300+ features from vehicle sales, service history, and customer survey
Feature integration
8 clustering algorithms with Bayesian hyperparameter optimization
Algorithms evaluated
Enterprise Technology
Open-Source LLM for Automated Document Parsing
Manual transcription to automated parsing with human review
Data entry workflow
Self-hosted, air-gapped from third-party APIs
Model hosting
Entertainment / Live Events
Parking Revenue Optimization for a Major Entertainment Venue
9, with per-lot demand prediction and pricing
Parking lots modeled
Automated price recommendations per lot per event
Revenue optimization
Frequently Asked
Questions
Do we need a large dataset to benefit from machine learning?
Not necessarily. Many high-value ML applications work well with modest datasets. Techniques like transfer learning, Bayesian optimization, and active learning are specifically designed for low-data regimes. We evaluate what you have and build a realistic plan.
How do you handle ML model deployment and monitoring?
We build ML systems for production, not just prototypes. Every model we deploy includes automated monitoring for data drift, prediction quality, and infrastructure health. We use MLOps tools like MLflow and cloud-native services to ensure models stay accurate and reliable over time.
What is your approach to LLM and generative AI projects?
We take a pragmatic approach: start with the business problem, evaluate whether an LLM is the right tool, and if so, build thin integration layers that minimize lock-in. We specialize in RAG architectures, fine-tuning, and evaluation frameworks that ensure LLM outputs meet your quality standards.
Is our data good enough for ML?
Maybe, maybe not. That is what an assessment is for. Most organizations have more usable data than they think, but it is rarely in the right shape. We evaluate what you have, identify the gaps, and build a realistic plan to get your data ML-ready without a multi-year cleanup project.
What is the difference between traditional ML and LLM/generative AI?
Traditional ML models (classification, regression, clustering) learn patterns from your structured data to make specific predictions. LLMs and generative AI work with unstructured text and can reason, summarize, and generate, but they are expensive to run and harder to evaluate. The right choice depends on your problem. We often use both in the same system.
How do you evaluate whether AI is the right solution for our problem?
We start with the business outcome, not the technology. If a rules-based system or a SQL query solves the problem, we will tell you that. AI makes sense when the problem involves pattern recognition at scale, unstructured data, or decisions that benefit from continuous learning. We map your use case against these criteria before writing any code.
Ready to put AI to work?
Whether you need a readiness assessment or a production deployment, we’ll start with what’s realistic for your data and your team.
Book an Intro CallOur Other Services
Or, see what else we do.
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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AI Strategy & Readiness
Find out which AI projects will deliver ROI. CorrDyn assesses your data, infrastructure, and use cases before you spend on models.
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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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Managed Data Team
Get a full data team without the hiring timeline. CorrDyn embeds the specific skill sets you need and owns outcomes, not just hours.
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