Custom AI for SAS Enterprise & Analytics
Cary is the global headquarters of SAS Institute and home to hundreds of data-driven enterprises. Petronella Technology Group, Inc. builds custom AI that integrates seamlessly with SAS ecosystems: predictive models that feed SAS Visual Analytics, AI assistants for SAS programmers, and enterprise AI platforms that connect to Microsoft Dynamics, Salesforce, and legacy ERP systems.
Founded 2002 • BBB Accredited Since 2003 • 2,500+ Clients • Zero Breaches
Why Cary Enterprises Need Custom AI
Cary companies already have sophisticated analytics. Custom AI takes them further—automating what SAS can't, integrating systems that don't talk, and unlocking insights buried in unstructured data.
SAS Ecosystem Integration
We connect custom AI to SAS Visual Analytics, SAS Viya, SAS Enterprise Miner, and SAS Model Manager. AI outputs flow into your existing dashboards, SAS jobs trigger AI inference, and analysts see unified results—no context switching between tools.
Enterprise System Connectors
Cary's mid-market companies run on Microsoft Dynamics, Salesforce, NetSuite, and homegrown ERPs. We build AI that pulls data from all of them, generates cross-system insights, and pushes actions back—all without replacing infrastructure you already paid for.
Advanced Analytics Beyond SAS
SAS is powerful for structured data. AI handles the rest: unstructured text analysis (contracts, emails, support tickets), computer vision (document parsing, quality inspection), and real-time streaming analytics that traditional batch jobs can't touch.
Enterprise Security & Compliance
Cary's finance, insurance, and healthcare tech companies face strict compliance requirements. We architect AI with SOC 2, GDPR, and industry-specific controls—so your analytics stay compliant and your auditors stay happy.
Why Custom AI Matters for Data-Driven Enterprises
Cary is home to SAS Institute, the world's largest privately-held analytics company, and hundreds of data-mature enterprises that have used SAS for decades. These companies have sophisticated analytics capabilities—business intelligence dashboards, predictive models, data warehouses, and teams of analysts who know SQL, SAS, and R.
But traditional analytics has limits. SAS excels at structured data: sales figures, customer demographics, financial transactions. It struggles with unstructured data: support tickets written in natural language, contracts with non-standard clauses, sensor logs from IoT devices, or images from manufacturing quality inspections. AI closes that gap.
Petronella Technology Group, Inc. builds custom AI that extends your existing analytics infrastructure:
- • Analyze unstructured data. Natural language processing extracts insights from customer emails, support tickets, contracts, and RFP responses. Computer vision analyzes product images, manufacturing defects, and document scans. Time-series models detect anomalies in sensor data that SAS forecasting can't catch.
- • Integrate disparate systems. Your data lives in Salesforce, Microsoft Dynamics, NetSuite, SQL Server, and SAS data marts. AI pulls data from all of them, generates unified predictions, and pushes recommendations back—so sales reps see AI insights in Salesforce without learning SAS.
- • Automate decisions in real time. SAS batch jobs run nightly or hourly. AI makes decisions in milliseconds: fraud detection on credit card swipes, dynamic pricing for e-commerce, or predictive maintenance alerts when equipment starts failing.
- • Scale beyond SAS licensing costs. SAS licensing is expensive. For workloads that don't require SAS-specific features (like NLP, computer vision, or streaming analytics), we deploy open-source AI models on your infrastructure—cutting costs while expanding capabilities.
- • Enable non-technical users. SAS analysts are scarce and expensive. AI chatbots let sales managers, marketing directors, and operations VPs ask questions in plain English—"Which customers are at risk of churning?" "What's driving our warranty claims?"—and get instant answers without writing SAS code.
We're not here to replace SAS. We're here to complement it. Your SAS investments stay productive. We layer custom AI on top—so you get the best of both worlds: structured analytics from SAS, unstructured insights from AI, and seamless integration across your entire data ecosystem.
Whether you're a 50-person fintech startup, a 500-employee insurance company, or a 5,000-employee enterprise with decades of SAS history, we build custom AI that fits your environment—not a generic solution that ignores your infrastructure, workflows, and institutional knowledge.
What We Build for Cary Enterprises
Every custom AI project is tailored to your business, data, and systems. Here are the most common use cases for Cary's data-driven companies.
AI Integration with SAS Viya & Visual Analytics
Your SAS team spent years building dashboards, forecasting models, and customer segmentation workflows. Custom AI extends those investments—without replacing them.
We build AI that integrates with SAS:
- AI-powered data enrichment: Before data hits SAS, AI processes unstructured text (emails, support tickets, survey responses) and extracts sentiment scores, topic tags, or urgency flags. SAS analysts see enriched data without manual labeling.
- Hybrid model ensembles: Combine SAS's logistic regression or decision trees with AI's deep learning models. SAS handles explainability and regulatory compliance; AI handles unstructured features and high-dimensional data. Ensemble models outperform either approach alone.
- AI results in SAS dashboards: AI models run on separate infrastructure (GPU servers or cloud), but outputs appear in SAS Visual Analytics as calculated columns or KPIs. Executives see unified insights without knowing which system generated them.
- SAS-triggered AI workflows: SAS jobs detect anomalies (unexpected sales drop, inventory spike) and trigger AI deep dives. AI scans related data sources (social media, competitor pricing, supply chain logs), identifies root causes, and alerts analysts within minutes.
Technical approach: We use SAS REST APIs, PROC HTTP, or SAS/ACCESS to connect SAS and AI systems. Data flows through secure APIs with authentication, encryption, and audit logging. No manual data exports or CSV wrangling.
Timeline: 8-12 weeks for initial integration and pilot. 4-6 months for production deployment across multiple SAS workflows.
Enterprise System Integration (CRM, ERP, E-commerce)
Cary enterprises use Microsoft Dynamics for finance, Salesforce for CRM, NetSuite or SAP for ERP, Shopify or Magento for e-commerce, and Zendesk for support. Data is siloed. AI breaks down those silos.
Custom AI integrations we build:
- Lead scoring across systems: AI pulls data from website analytics (Google Analytics), email engagement (HubSpot or Marketo), LinkedIn activity, and Salesforce CRM. Scores leads based on 50+ signals. Top prospects appear in Salesforce with AI-generated "next best action" recommendations.
- Customer churn prediction: Train AI on ERP billing data, support ticket history (Zendesk or ServiceNow), product usage logs, and contract renewal dates. Predict which customers will churn 90 days in advance. Alerts account managers via Salesforce tasks or Slack.
- Dynamic pricing optimization: AI monitors competitor pricing (web scraping or data feeds), inventory levels (ERP), demand signals (Google Trends, past sales), and margin targets. Updates e-commerce pricing in real time to maximize revenue while staying competitive.
- Automated contract review: Sales teams upload customer contracts to Salesforce. AI extracts payment terms, SLAs, auto-renewal clauses, and liability caps. Flags non-standard terms for legal review. Accelerates deal closure by 30%.
- Support ticket triage: AI reads Zendesk tickets as they arrive, categorizes urgency (P1 outage vs. low-priority question), routes to the right team, and drafts initial responses. Reduces first-response time from 4 hours to 10 minutes.
Integration approach: We use REST APIs (Salesforce, Zendesk, Shopify), OData feeds (Microsoft Dynamics), ODBC/JDBC (SQL Server, Oracle), and webhooks for real-time events. Data flows are secured with OAuth, encrypted in transit (TLS 1.3), and logged for compliance.
Timeline: 6-10 weeks for single-system integration (e.g., AI + Salesforce). 4-6 months for multi-system workflows (AI pulling from 4+ data sources and pushing actions back).
Natural Language AI for Business Users
Most business leaders can't write SAS code or SQL. They wait days for analysts to run reports. AI chatbots give them instant answers in plain English—no coding required.
Natural language AI for Cary enterprises:
- Conversational business intelligence: "Show me Q1 revenue by product line." "Which customers haven't ordered in 6 months?" "What's our average deal size for fintech clients?" AI translates natural language to SQL or SAS queries, runs them, and returns charts or tables in Slack, Microsoft Teams, or a web dashboard.
- AI-assisted report generation: Marketing directors ask: "Generate a monthly performance report with campaign ROI, lead conversion rates, and top-performing channels." AI pulls data from Google Analytics, HubSpot, and Salesforce; generates a 10-page PDF with charts; emails it automatically.
- Predictive Q&A: "Will we hit our sales target this quarter?" AI models historical trends, pipeline data, and seasonality to forecast likely outcomes. Confidence intervals included. Executives get probabilistic forecasts without waiting for analyst reviews.
- Drill-down explanations: "Why did revenue drop 12% last month?" AI scans transaction data, identifies top contributing factors (lost customer, pricing change, regional downturn), and generates root-cause analysis. Leaders understand why metrics changed, not just that they changed.
Security and governance: AI enforces role-based access—sales VPs can't see finance data, regional managers only see their region. All queries are logged for audit trails. Data never leaves your environment (AI runs on your servers or private cloud).
Timeline: 8-12 weeks for pilot with 20 users. 4-6 months for company-wide rollout with training and change management.
Document Processing & Intelligent Automation
Finance, insurance, and legal teams process thousands of documents: invoices, contracts, policy applications, compliance filings. Manual data entry and review wastes hundreds of hours per month. AI automates it.
Custom AI for document automation:
- Invoice processing: AI reads PDFs or scanned images, extracts vendor name, invoice number, line items, amounts, and payment terms. Routes to approval workflows in NetSuite or Microsoft Dynamics. Reduces AP processing time from 8 hours to 30 minutes per batch. Accuracy: 98%+.
- Contract analysis: Legal teams upload contracts (MSAs, NDAs, SaaS agreements). AI extracts key terms: payment schedules, liability caps, termination clauses, auto-renewal dates. Flags deviations from standard templates. Saves 15 hours per contract review.
- Insurance underwriting: Applicants submit policy applications (often PDFs or handwritten forms). AI extracts applicant info, risk factors, coverage amounts. Runs rules-based checks and predictive risk scoring. Underwriters see pre-scored applications with AI recommendations in seconds.
- Compliance document review: Banks and insurance companies must review thousands of regulatory filings annually. AI scans for missing sections, inconsistent data, or policy violations. Flags issues for human review. Reduces compliance review time by 60%.
- RFP response automation: Sales teams respond to dozens of RFPs per quarter. AI reads RFP questions, searches past responses and product documentation, drafts initial answers. Sales teams edit and finalize. Cuts RFP response time from 40 hours to 8 hours.
How it works: AI uses computer vision (OCR) to read documents, NLP to extract entities and relationships, and business rules to validate data. Outputs feed directly into ERPs, CRMs, or workflow tools (Salesforce, ServiceNow, Microsoft Power Automate).
Timeline: 6-10 weeks for single document type (invoices or contracts). 4-6 months for multi-document workflows with ERP/CRM integration.
Predictive Analytics for Supply Chain & Operations
Cary's manufacturing, distribution, and logistics companies face demand volatility, supply chain disruptions, and equipment failures. AI predicts problems before they happen—so you can act instead of react.
Custom predictive AI for operations:
- Demand forecasting: Train AI on 5+ years of sales data, seasonality, promotions, economic indicators, and external events (weather, holidays). Predicts demand 12 weeks ahead with 15-20% better accuracy than traditional time-series models. Optimizes inventory levels and reduces stockouts.
- Predictive maintenance: Install sensors on production equipment (motors, pumps, conveyors). AI analyzes vibration, temperature, and current draw to predict failures 2-4 weeks early. Schedule maintenance during planned downtime instead of emergency shutdowns. Reduces unplanned downtime by 40%.
- Supply chain risk detection: AI monitors supplier performance (on-time delivery, quality defects), geopolitical events, weather disruptions, and financial health signals. Alerts procurement teams to at-risk suppliers before they miss shipments. Enables proactive dual-sourcing.
- Route optimization: For distribution or field service companies: AI analyzes historical delivery data, traffic patterns, and customer time windows. Generates optimized routes that reduce fuel costs by 12-18% and improve on-time delivery rates.
- Quality prediction: Train AI on production parameters (temperature, pressure, material batch IDs) and quality outcomes (defect rates, yield). Predicts quality issues before products leave the line. Enables real-time process adjustments to prevent scrap.
Data sources: ERP transaction data, MES/SCADA sensor logs, IoT device streams, weather APIs, and external market data. AI handles high-frequency data (sensor readings every second) and batch data (daily sales reports).
Timeline: 10-14 weeks for model development and validation. 4-6 months for production deployment with real-time dashboards and alert workflows.
Custom AI for Financial Services & Insurance
Cary's fintech startups, insurance agencies, and wealth management firms need AI that handles regulated data, passes audits, and integrates with core banking or insurance platforms.
Custom AI for financial services:
- Fraud detection: AI analyzes transaction patterns, user behavior, device fingerprints, and network activity to detect fraudulent transactions in real time. Blocks suspicious payments before they clear. Reduces false positives by 50% compared to rule-based systems.
- Credit risk modeling: Train AI on borrower financials, payment history, alternative data (rent payments, utility bills), and macroeconomic indicators. Predicts default probability with 10-15% better accuracy than traditional FICO-based models. Expands lending to underserved markets.
- Customer lifetime value prediction: Wealth management firms use AI to predict which clients will generate the most revenue over 10+ years. Prioritize high-CLV clients for personalized service and upsell opportunities. Increases AUM growth by 20%.
- Regulatory compliance monitoring: AI scans transactions, communications, and trades for patterns that indicate insider trading, market manipulation, or AML violations. Generates Suspicious Activity Reports (SARs) for review. Reduces compliance team workload by 40%.
- Insurance claims automation: AI reads claim forms (PDFs, photos, emails), extracts loss details, cross-references policy coverage, and estimates payout amounts. Simple claims auto-approve in minutes. Complex claims route to adjusters with AI pre-analysis. Cuts claims processing time by 60%.
Compliance and explainability: Financial services AI requires explainability for regulators. We use interpretable models (XGBoost with SHAP values, rule-based ensembles) and generate audit-ready reports that show why AI made each decision.
Timeline: 12-16 weeks for model development and regulatory validation. 6-9 months for production deployment with audit documentation and compliance review.
How We Build Custom AI for Cary Enterprises
Custom AI for data-mature companies isn't about flashy demos. It's about rigorous integration, governance, and ROI measurement. Here's our 4-phase process.
Systems Audit & Use Case Definition (2-4 weeks)
We start by mapping your data ecosystem: where data lives (SAS, SQL Server, Salesforce, ERP), how it flows between systems, who owns it, and what governance policies apply. We interview stakeholders (analysts, IT, business leaders) to identify high-impact AI use cases and define success metrics.
Deliverable: 35-page assessment with data inventory, system architecture diagrams, prioritized use case roadmap, and ROI projections. You'll know exactly what we're building and why it matters to your business.
Data Integration & Model Development (8-14 weeks)
We build secure data pipelines that pull from your CRM, ERP, SAS data marts, and external sources. Data is cleaned, transformed, and validated for model training. We develop custom AI models (fine-tuned transformers, gradient boosting ensembles, deep learning architectures) and test them on historical data to measure accuracy, precision, and recall.
For SAS integration: we use SAS REST APIs or PROC HTTP to call AI inference endpoints. For CRM/ERP integration: we use OAuth-secured REST APIs or ODBC connectors. All data flows are encrypted, logged, and monitored.
Deliverable: Trained AI models with validation report (performance metrics, failure analysis), data pipeline documentation, and API specifications for system integration.
Pilot Deployment & User Testing (6-10 weeks)
We deploy AI to a pilot group (20-50 users in one department or region). AI outputs appear in familiar tools: Salesforce dashboards, SAS Visual Analytics reports, Slack channels, or custom web portals. We train users, collect feedback, and iterate on the UI and model outputs.
Pilot phase includes: performance monitoring (latency, accuracy, uptime), user interviews (is AI helping or frustrating?), and ROI tracking (time saved, revenue increased, errors prevented).
Deliverable: Working AI system in pilot production, user training materials, feedback summary, and go/no-go recommendation for full rollout.
Production Rollout & Optimization (4-6 months)
After pilot validation, we scale to hundreds or thousands of users. This phase includes: infrastructure scaling (adding GPU capacity, optimizing API response times), change management (company-wide training, executive dashboards), and governance setup (data access policies, model versioning, audit procedures).
Post-launch optimization continues: quarterly model retraining as new data arrives, drift monitoring (model accuracy degrading over time), and feature enhancements based on user requests. Monthly ROI reports track business impact.
Deliverable: Production AI system serving all users, comprehensive documentation, 90-day post-launch support, and optional ongoing support contract (three tiers available).
Why Cary Enterprises Trust Petronella Technology Group, Inc.
Most AI consultants are data scientists who've never managed enterprise IT infrastructure. Petronella Technology Group, Inc. is different: we're a 25-year-old technology firm with deep expertise in enterprise systems, data security, and the SAS ecosystem. We've built the networks, secured the data centers, and managed the IT operations for Cary's most data-mature companies.
When you hire us for custom AI development, you get:
- • SAS ecosystem expertise. We've worked with SAS clients for 20+ years. We understand SAS licensing, deployment architectures (Viya vs. 9.4), and integration patterns. We know how to extend SAS without disrupting existing workflows.
- • Enterprise integration experience. We've integrated dozens of CRMs, ERPs, and data warehouses. We know the APIs, authentication schemes, and edge cases that cause projects to fail. We've done this before—so we know where the bodies are buried.
- • Security and compliance rigor. Our founder is a CMMC Certified Registered Practitioner and Licensed Digital Forensic Examiner. We've built SOC 2-compliant systems, passed PCI-DSS audits, and secured environments for financial services and healthcare tech companies.
- • Full-stack infrastructure capability. We don't just build models—we deploy the GPU servers, optimize the networks, and manage the cloud environments where AI runs. On-prem, cloud, or hybrid: we handle all of it.
- • Local presence. We're based in Raleigh, 15 minutes from Cary. We meet in person, tour your facilities, and embed with your teams. We're not a remote consulting firm that vanishes after the contract ends.
If you're a Cary enterprise that needs AI built right—with integration, security, and ROI accountability—you want Petronella Technology Group, Inc..
Our Cary Track Record
Ready to Extend Your SAS Ecosystem with AI?
Let's discuss your data architecture, integration requirements, and AI use cases. We'll propose a custom solution that complements your existing analytics investments.
Founded 2002 • BBB Accredited Since 2003 • Cary, NC
Custom AI Development FAQs
Can AI replace our SAS infrastructure?
No—and we don't recommend it. SAS is excellent for structured analytics, regulatory reporting, and explainable models. AI complements SAS by handling unstructured data (text, images, sensor streams), real-time predictions, and use cases where deep learning outperforms traditional statistics.
We integrate AI with SAS so you get the best of both worlds: SAS for structured analytics and compliance, AI for unstructured insights and automation. Your SAS investments remain productive while you expand capabilities into areas SAS wasn't designed for.
How long does custom AI integration take?
Timeline depends on complexity:
- Simple pilot (one use case, single-system integration): 8-12 weeks
- Multi-system integration (CRM + ERP + SAS): 4-6 months
- Enterprise-wide platform (multiple use cases, company-wide rollout): 9-12 months
Phases include: systems audit (2-4 weeks), data integration and model development (8-14 weeks), pilot deployment (6-10 weeks), and production rollout (4-6 months). We deliver working prototypes early so you see progress throughout.
What does custom AI development cost?
Costs vary by scope:
- Pilot project: $100K-$200K (single use case, 20-50 users, cloud-based)
- Full production system: $300K-$800K (multi-system integration, 200-500 users, on-prem or hybrid)
- Enterprise platform: $1M-$3M+ (multiple use cases, company-wide, includes infrastructure)
Infrastructure costs (GPU servers or cloud compute) are additional. We provide detailed TCO analysis during the audit phase so you understand upfront costs, ongoing expenses, and expected ROI.
Can you integrate with our existing CRM and ERP?
Yes. We integrate with:
- CRMs: Salesforce, Microsoft Dynamics 365, HubSpot, Zoho, Pipedrive
- ERPs: Microsoft Dynamics, NetSuite, SAP, Oracle EBS, Infor, Epicor
- Analytics: SAS Viya, SAS 9.4, Tableau, Power BI, Qlik
- Support/ticketing: Zendesk, ServiceNow, Freshdesk, Jira Service Management
- E-commerce: Shopify, Magento, WooCommerce, BigCommerce
Integration methods include REST APIs, ODBC/JDBC, webhooks, and file-based exchanges (SFTP). All data flows are encrypted, authenticated, and logged for compliance.
How do you ensure data security during integration?
Security is built into every integration:
- Encryption: All data in transit uses TLS 1.3. Data at rest uses AES-256 encryption.
- Authentication: OAuth 2.0 for API access, MFA for user logins, service accounts with least-privilege permissions.
- Network segmentation: AI workloads run on isolated VLANs or VPCs with firewall rules restricting access.
- Audit logging: Every API call, data access, and model inference is logged with timestamps, user IDs, and IP addresses. Logs retained per compliance requirements (SOC 2: 1 year, GDPR: as needed for breach investigation).
- Penetration testing: Before production launch, we conduct penetration tests to identify vulnerabilities.
We've helped clients pass SOC 2 Type II audits, PCI-DSS assessments, and customer security reviews for financial services and healthcare tech companies. Zero breaches in 25 years.
Do we need a data science team to work with you?
No. We bring the AI/ML engineering expertise. You provide:
- Domain knowledge: What problems need solving? What outcomes matter?
- Data access: Credentials to CRM, ERP, SAS, and databases
- Business validation: Review AI outputs to ensure they make business sense
Many Cary clients have SAS analysts but no data scientists—and that's fine. We handle model development, training, deployment, and optimization. Over time, we can train your team to maintain models if you want to build internal capabilities, but it's not required to get started.
What happens if the AI model's accuracy degrades over time?
Model drift is normal—customer behavior changes, products evolve, markets shift. We monitor for drift and retrain models to maintain accuracy:
- Automated monitoring: AI systems track prediction accuracy, confidence scores, and input data distributions. Alerts trigger when metrics fall below thresholds.
- Scheduled retraining: Most models retrain quarterly on the latest data. High-velocity models (fraud detection, demand forecasting) retrain monthly.
- Human-in-the-loop validation: Before deploying retrained models, we validate on test data and run A/B tests against the current model to ensure improvements.
All support contracts (Basic, Standard, Premium) include drift monitoring and retraining. You'll never wonder if your AI is still working—you'll get monthly reports with accuracy trends and retraining logs.
Can we deploy AI on-premises or do we have to use the cloud?
Yes, we deploy AI on-premises, in the cloud, or hybrid. Choice depends on:
- On-prem: Best for sensitive data (financial transactions, healthcare records), compliance requirements (data residency), or organizations with existing data center capacity. Higher upfront capital cost, lower long-term cost.
- Cloud: Best for variable workloads, rapid prototyping, or organizations without data center infrastructure. Lower upfront cost, higher long-term cost. We use AWS, Azure, or Google Cloud depending on your existing footprint.
- Hybrid: Train models in the cloud (where GPUs are cheap and abundant), deploy inference on-prem (where data stays secure and latency is low). Best of both worlds for regulated industries.
We model TCO over 3-5 years for all three options so you can make an informed decision.
Let's Build Custom AI for Cary
Whether you're a SAS shop, a mid-market enterprise, or a fintech startup, we'll build AI that integrates with your systems, complements your analytics, and drives measurable ROI.
Founded 2002 • BBB Accredited Since 2003 • 2,500+ Clients • Zero Breaches • 25+ Years in Cary