Your Pick/MultiValue system is sitting on decades of transactional data — sales history, customer behavior, inventory movements, production records, purchasing patterns. AI doesn't replace any of that. It reads it, finds what humans miss, and does the tedious work that nobody has time for.
We connect AI services to your Pick system through the same PHP integration layer we use for everything else. No data warehouse required. No six-month ML engineering project. Practical AI that solves a specific problem, connected to the data you already have.
Product descriptions & content generation
If you have thousands of SKUs with thin or missing descriptions, AI can write them. We feed your Pick product data — part numbers, specs, categories, attributes — into an LLM and generate consistent, well-written descriptions for your catalog, website, or e-commerce platform. What used to take a marketing intern three months takes an afternoon.
The same approach works for customer-facing content: product comparison summaries, technical spec sheets, category landing page copy, and SEO-optimized product pages. All generated from the structured data already in Pick.
Demand forecasting
Your Pick system has years of sales and inventory history. AI models can read that history and predict what's going to sell, when, and how much. Seasonal trends, customer ordering patterns, product lifecycle curves — patterns that are invisible in a spreadsheet become actionable forecasts. The output feeds back into your Pick purchasing and inventory planning modules.
Anomaly detection
Unusual transactions, pricing outliers, inventory discrepancies, sudden changes in customer ordering patterns, AP fraud indicators. AI models trained on your historical data can flag the exceptions that a human reviewing reports would miss. The alerts route into Pick or email so your team investigates the right things instead of reviewing everything.
Customer segmentation & analysis
Decades of transaction history means you know more about your customers than you think. AI can cluster customers by purchasing behavior, identify at-risk accounts based on declining order patterns, flag cross-sell opportunities based on what similar customers buy, and score leads based on how closely they match your best existing accounts. All derived from data that's been accumulating in Pick for years.
Document processing
Purchase orders, invoices, packing slips, RFQs — documents that arrive as PDFs or emails and need to be keyed into Pick. AI-powered document extraction reads the fields, maps them to your Pick data structure, and either auto-creates the transaction or queues it for a quick human review. Cuts data entry time dramatically for high-volume shops.
Intelligent search
Parts lookup across thousands of SKUs with inconsistent naming, misspellings, and multiple part number formats. AI-powered search understands what your user meant, not just what they typed. "3/8 hex bolt stainless" finds the right part even when the catalog says "SS HEX CAP SCREW .375-16." We connect the search interface to your Pick inventory files so results are always current.
How we integrate AI
The pattern is straightforward. Your Pick system exports the relevant data (product records, transaction history, customer files). A PHP script calls an AI service — OpenAI, Anthropic, or a locally-hosted model — with that data. The AI returns its output (descriptions, predictions, classifications, extracted fields). Pick consumes the result and acts on it.
For real-time use cases (search, document processing), the AI call happens inline. For batch use cases (descriptions, forecasting), it runs as a scheduled job. Either way, Pick stays the system of record. AI is a tool it calls, not a system it's replaced by.
Platforms
We integrate AI services with jBASE, Rocket UniVerse, Rocket UniData, Rocket D3, and mvBASE.