AI Solutions & Optimization | Pictured Rock Advisors

What We Build

Four Categories of AI That Delivers

We don't recommend tools and leave you to figure it out. We design, build, deploy, and continuously improve AI solutions — owning the outcome alongside you from day one.

Custom AI/ML Solutions

Generic AI tools are trained on generic data. We build models trained on yours — capturing the patterns, nuances, and domain knowledge specific to your business to deliver accuracy that off-the-shelf solutions simply can't match.

Common Use Cases

Demand Forecasting Churn Prediction Anomaly Detection Customer Segmentation Recommendation Engines

90%+ Accuracy on production custom models vs. 60–70% typical for generic tools

Generative AI Implementation

Large language models are transformative — when they're grounded in your data, connected to your systems, and governed by your business rules. We implement practical generative AI solutions that deliver real productivity gains, not just demos.

Common Use Cases

Internal Knowledge Assistants Document Summarization Customer Support Copilots Content & Report Generation Code Assistance

40%+ Productivity gains for knowledge workers with well-implemented AI copilots

Predictive Analytics

Stop reacting to what already happened. Predictive models surface the signals in your data — identifying what's likely to happen next so your team can act before problems escalate or opportunities pass.

Common Use Cases

Sales Forecasting Predictive Maintenance Risk Scoring Supply Chain Disruption Alerts Cash Flow Prediction

Weeks Earlier warning on risks and disruptions vs. discovering them after the fact

Continuous Improvement

AI models degrade over time as data distributions shift. We implement model monitoring, automated retraining pipelines, and performance dashboards that keep your AI running at peak accuracy — and improve it as more data accumulates.

What We Monitor & Optimize

Model Accuracy Tracking Data Drift Detection Automated Retraining A/B Model Testing Performance Dashboards

Always Improving — models retrain automatically as new data arrives

How We Work

From Business Problem to Production AI

We start with the outcome you need, not the technology we want to use. Every engagement follows a structured process that ensures what we build actually works in your environment.

1

Problem Definition & Data Assessment

We define the exact business problem to solve, evaluate the data available to solve it, identify gaps, and determine which AI approach will deliver the highest-confidence results.

2

Model Design & Development

We select and build the right model architecture, train it on your historical data, validate it rigorously against held-out test sets, and iterate until accuracy meets your performance thresholds.

3

Deploy & Integrate

We deploy the model into your production environment and integrate it with your existing systems — so outputs flow directly into the workflows and tools your team already uses.

4

Monitor, Retrain & Optimize

We implement ongoing monitoring to detect accuracy drift, automate retraining on new data, and continuously tune the model — so it gets more valuable over time, not less.

Proven Results

What This Looks Like In Practice

Manufacturing

Predictive Supply Chain Engine That Pays for Itself Every Quarter

A national manufacturer was managing inventory reactively — ordering based on instinct and historical averages, routinely running into stockouts and overstock situations that cost millions in expediting fees, lost sales, and carrying costs.

We deployed a custom AI forecasting model that ingests sales history, seasonal patterns, supplier lead times, and external market signals to generate demand predictions with 94% accuracy — and automatically triggers procurement recommendations before shortages occur.

Built a custom gradient-boosting model trained on 36 months of SKU-level sales data
Integrated real-time supplier lead time feeds and external demand signals
Automated procurement recommendation engine connected directly to ERP
Implemented model monitoring with weekly accuracy reporting and quarterly retraining
Delivered a business intelligence dashboard so planners see every signal and recommendation

$2.1M Annual cost savings in year one

40% Reduction in stockout events

94% Forecast accuracy in production

<5 mo Time from kickoff to production

Who It's For

Built for Teams That Need AI That Actually Performs

Custom AI delivers the highest value where precision, scale, and domain specificity matter most. Here's where we see the greatest impact.

Operations & Supply Chain

Predictive models for demand forecasting, inventory optimization, supplier risk scoring, and logistics planning. Stop managing supply chain by gut feel and start managing it with AI-driven foresight.

Finance & Risk

Custom models for credit scoring, fraud detection, cash flow forecasting, and risk classification. Replace slow, manual credit and risk processes with AI systems that operate at scale with consistent, auditable logic.

Sales & Revenue Operations

Predictive lead scoring, churn prediction, deal outcome forecasting, and next-best-action recommendations. Give your revenue team the intelligence to focus effort where it drives the most impact.

Healthcare

Predictive models for patient risk stratification, readmission prediction, resource planning, and clinical pathway optimization. Deliver better outcomes with AI that surfaces the right insight at the right time.

Retail & E-Commerce

Personalized recommendation engines, dynamic pricing models, demand forecasting, and customer lifetime value prediction. Use AI to make every customer interaction smarter and every inventory decision more precise.

Knowledge-Intensive Teams

Generative AI copilots and internal assistants for legal, compliance, HR, and consulting teams. Automate research, summarization, and drafting so your experts spend more time on the high-value thinking that only they can do.

FAQ

Common Questions About Custom AI Engagements

Do we need a data science team in-house to work with you?

No. We serve as your AI/data science team for the engagement — and often beyond it. We handle model design, development, validation, deployment, and ongoing monitoring. We do involve your subject matter experts heavily during problem definition and model validation, because the people who understand your business are critical to building AI that makes sense in context. But we don't need an in-house data science team to get started.

How much data do we need to build effective AI models?

It depends on the problem. Most supervised ML models start producing useful results with 1,000–10,000 labeled examples, though more complex problems need more data. For time-series forecasting, 12–24 months of history is typically the minimum for reliable patterns. For generative AI applications, you don't need to train a model at all — we use retrieval-augmented generation (RAG) to ground LLMs in your existing documents and data. We always assess your data during the discovery phase and tell you honestly what's feasible before any commitment.

How do you ensure AI models stay accurate over time?

Model degradation is one of the most common and underappreciated risks in AI deployments. We address it by implementing production monitoring that tracks accuracy metrics continuously, detects data drift (when the incoming data looks different from training data), and triggers alerts when performance drops below defined thresholds. For most models, we set up automated retraining pipelines that incorporate new data on a regular schedule — so the model improves as your business evolves rather than slowly becoming less relevant.

What's the difference between building a custom model and using an off-the-shelf AI tool?

Off-the-shelf tools are trained on general data and optimized for average use cases. They're fast to deploy but typically deliver 60–70% accuracy — and they can't capture the specific patterns in your customer base, your product mix, your operational rhythms. A custom model trained on your data regularly achieves 90%+ accuracy for the same task, because it's learned your specific reality rather than a generalized version of it. We recommend off-the-shelf tools when they're genuinely the right fit — and custom builds when precision matters enough to justify it.

How do you approach responsible AI and model explainability?

We build explainability into every model from the start. For regulated industries — financial services, healthcare — we prioritize interpretable model architectures and implement tools like SHAP values that explain why the model made a specific prediction. We also conduct bias audits to identify whether models perform differently across demographic or categorical groups. For generative AI applications, we implement guardrails, content policies, and human review workflows for high-stakes outputs. AI that can't be explained or trusted won't be adopted — and we design for adoption from the beginning.

Get Started

Ready to Build AI That Actually Works?

Tell us about the problem you're trying to solve. We'll tell you whether AI is the right tool for it — and if so, exactly what it would take to build a solution that delivers.

info@picturedrockadvisors.com