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AI that actually works — built for your business.

Generic AI tools give you generic results. We build machine learning models and AI agents trained on your data, for your specific business problems — cash flow forecasting, demand prediction, customer churn analysis, and LLM-powered automation that saves your team real hours every week.

⏲ 6–10 weeks per model · Trained on your data

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What We Build

Six categories of AI implementation. Each one tailored to your business.

We don't deploy off-the-shelf models and call it AI consulting. Every implementation is trained on your data and built for your specific business problem.

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Predictive Analytics

Cash flow forecasting, demand prediction, customer churn analysis, and revenue projections — models trained on your historical data, not generic industry benchmarks.

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LLM & AI Agent Integration

Claude and GPT-powered agents built into your operations — document processing, customer inquiry handling, contract analysis, and automated reporting that works on your actual workflows.

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Process Automation

ML-driven workflows that replace manual data entry, report generation, document categorization, and quality control — saving your team hours per week on tasks that don't need a human.

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Custom Model Training

Models trained on your proprietary business data — transaction history, customer behavior, operational patterns — for accuracy that generic SaaS tools can't match.

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Price & Inventory Optimization

Dynamic pricing recommendations and inventory reorder points based on ML demand signals, supplier lead times, and your specific margin targets.

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Monitoring & Optimization

Ongoing model performance tracking, automated retraining schedules, drift detection, and accuracy improvement cycles. Models get better over time, not worse.

How It Works

From problem definition to deployed model.

Every AI implementation starts with a business problem, not a technology selection.

Problem Definition & Data Assessment

We define the specific business problem the model needs to solve — not "we want AI" but "we need to predict which customers will churn 60 days before they leave." Then we assess your data to confirm we have what we need to build it accurately.

Weeks 1–2

Feature Engineering & Model Development

We build and test multiple model approaches against your data, selecting the architecture that performs best for your specific problem. You see benchmark results before we move to production.

Weeks 2–6

Validation & Business Testing

We validate model performance against held-out historical data and run parallel tests against your current decision-making process. You see exactly how much better the model performs before it goes live.

Weeks 6–8

Production Deployment & Handoff

We deploy the model into your production environment, integrate it with your existing workflows, train your team on interpreting outputs, and set up monitoring and retraining schedules.

Weeks 8–10

Often Paired With

AI works best when the data foundation is solid.

Most AI implementations require clean, structured data flowing through a reliable pipeline. If you don't have that yet, we can build it first.

Data & AI Strategy Assessment

Not sure what to build first? The assessment maps your AI opportunities, prioritizes by ROI, and gives you a concrete roadmap before any implementation begins.

Learn more →

Document Intelligence

If your business runs on contracts, compliance documents, or dense records, our document intelligence platform turns them into a searchable, queryable knowledge base.

Learn more →

See it in action — view client results →

Get Started

Tell us the business problem. We'll tell you if AI can solve it.

Schedule a 45-minute discovery call. We'll talk about the specific decisions you wish you could make faster or more accurately, and whether a machine learning model is the right tool. No pitch, no obligation.

Schedule a Discovery Call