Architected systems. Production AI. Connected platforms.

Dealerships often rely on repetitive, incomplete, or generic vehicle descriptions that fail to highlight the unique value of each vehicle. Manually writing descriptions at scale is expensive, slow, and inconsistent. Inventory also changes frequently, requiring a process that can keep up with nightly feed updates while preserving quality and brand voice.

VDP Fusion connects to dealership inventory feeds and generates unique, SEO-optimized descriptions for each vehicle. The system supports dealership-specific messaging, offers, keywords, target audiences, target locations, and platform-specific content for channels such as dealership websites, Craigslist, and marketplace listings.

• check AI-powered description generation from structured inventory data.
• check Nightly inventory feed processing.
• check Dealer-specific AI settings for tone, reading level, sentiment, creativity, offers, keywords, and calls to action.
• check Data enhancement using vehicle-specific information, awards, safety details, and third-party automotive data.
• check Rules-based quality assurance to detect missing requirements and regenerate weak outputs.
• check Multi-language support for broader market reach.

• AI content generation — Vehicle-specific descriptions tuned to dealer voice, offers, audience, and channel.
• AI translation — Multi-language description variants from a single canonical record.
• AI quality rules — Rules-based QA that detects missing required content and triggers regeneration with corrected prompts.
• AI workflow automation — Nightly batch generation, channel-specific publishing, and dealer-level configuration application.

Automotive data from public, commercial, OEM, and internal sources is often inconsistent. Vehicle makes, models, trims, engines, VIN patterns, fuel types, body styles, and configurations may be represented differently across systems. This creates downstream problems for search, service menus, inventory systems, fitment logic, pricing, and customer-facing automotive applications.

AutoResolve is designed as an automotive identity backbone. It ingests source data, normalizes reference values, resolves engines and vehicles, maps source records to canonical entities, and preserves lineage so downstream systems can understand where each resolved data point came from.

• check vPIC/NHTSA-based canonical vehicle data pipeline.
• check ETL workflow with auditable batch execution.
• check Source-to-core lineage mapping.
• check Reference normalization for makes, models, fuel types, drive types, body types, engine configuration, and related attributes.
• check Vehicle and engine identity-resolution logic.
• check SQL Server-based canonical schema using source, reference, core, map, and audit layers.
• check Designed for future expansion into EPA, OEM, service, and parts data sources.

• AI classification — Reference normalization across makes/models/fuel/body/drive/engine attributes when source labels disagree.
• AI extraction & identity resolution — Resolving the same vehicle or engine across sources where naming, casing, or configuration differs.
• AI workflow automation — Auditable batch resolution with confidence-scored matches surfaced for human review when below threshold.

Service departments often manage maintenance packages, labor pricing, advisor recommendations, and customer communication through a mix of legacy systems, spreadsheets, manual judgment, and inconsistent processes. This can create confusion for advisors and customers while reducing trust and consistency.

ServiceDriver is designed to modernize how automotive service recommendations are structured and presented. It connects vehicle data, mileage, maintenance packages, labor logic, pricing, and advisor workflows into a cleaner system that can support both internal service teams and customer-facing experiences.

• check Vehicle-aware service menu structure.
• check Mileage-based maintenance recommendation logic.
• check Service package organization.
• check Labor rate and pricing logic.
• check Support for advisor consistency and customer-facing clarity.
• check Potential integration with canonical vehicle data from AutoResolve.
• check Designed to modernize legacy service workflows into a cleaner platform.

• AI recommendations — Mileage- and vehicle-aware maintenance package recommendations that adapt to canonical vehicle data and service history.
• AI advisor support — Surfacing the right service menu and package combinations to advisors so customer-facing recommendations stay consistent.
• AI workflow automation — Moving advisor decisions out of spreadsheets and tribal knowledge into a structured, auditable system.

Many small and mid-sized businesses need website content, landing pages, blogs, service pages, SEO updates, and brand-specific messaging, but traditional CMS platforms can be too generic, too disconnected from custom applications, or too difficult to integrate with modern AI-assisted workflows.

IntelliCMS is designed as a centralized content platform that can serve multiple websites while allowing each site to maintain its own branding, layout patterns, content structure, and publishing needs. It supports reusable content blocks, structured page management, editorial workflows, and AI-assisted content generation.

• check Multi-site content management.
• check Site-specific branding and layout control.
• check Reusable page and component architecture.
• check API-delivered content for Blazor Server websites.
• check AI-assisted content creation and editing workflows.
• check Blog, service page, landing page, and reusable content support.
• check Designed for centralized management across multiple business websites.

• AI content generation — Long-form drafts, service-page copy, and landing-page variants from structured site context (industry, audience, offer).
• AI editing & rewriting — Tone, length, reading-level, and SEO-aware refinement of editor-written content.
• AI workflow automation — Drafts routed through editorial review states; AI suggestions kept human-in-the-loop, never auto-published.

Many dealer websites rely on rigid templates, weak inventory presentation, poor SEO structure, and disconnected lead capture experiences. Dealers need websites that can present inventory clearly, support local search visibility, and convert shoppers into leads.

The platform supports inventory listing pages, vehicle detail pages, filters, lead forms, recent vehicle sections, specials pages, dealer-specific content, and SEO-aware vehicle presentation. It is built to support dealership branding while keeping the underlying architecture reusable across multiple inventory-driven websites.

• check Inventory feed integration.
• check Vehicle listing and detail pages.
• check Make, model, trim, year, price, body type, and color filters.
• check Lead capture forms.
• check Dealer-specific branding and content.
• check SEO-ready page structures.
• check API-driven architecture.
• check Reusable Blazor Server components and service-based design.

• Vehicle descriptions (consumed) — Listing and VDP copy comes from VDP Fusion's AI content generation pipeline.
• Vehicle identity (consumed) — Fitment, configuration, and trim accuracy comes from AutoResolve's AI classification and identity resolution.
• Service surfaces (consumed) — When integrated, service-related content comes from ServiceDriver's recommendation logic.

Many marketplace businesses need more than a simple listing website. They need reusable architecture that can support multiple categories, vendors, locations, account types, search experiences, monetization models, and branded vertical sites without rebuilding the entire system for each market.

Listmill is designed as a reusable marketplace foundation. It supports marketplace listings, structured categories, vendor profiles, search and discovery experiences, map-based browsing, e-commerce-style surfaces, and modernization across multiple branded sites.

• check Category-specific marketplace architecture.
• check Classified and e-commerce style listing support.
• check Vendor discovery and account workflows.
• check Search and mapping features.
• check Reusable platform foundation for multiple vertical markets.
• check Modernization approach for legacy marketplace systems.
• check Designed for branded marketplace expansion.

• AI-assisted listing creation — Vendor onboarding helped along by AI-drafted titles, descriptions, and structured attributes from minimal vendor input.
• AI semantic search — Discovery that goes beyond keyword match to understand buyer intent across categories.
• AI classification — Automatic category routing for new listings, reducing miscategorized inventory.
• AI recommendations — Buyer ↔ vendor matching and "you might also like" surfacing.
• AI moderation / safety — Spam and scam-pattern detection on listings and accounts to keep the marketplace trustworthy.

Sales professionals often track goals, activity, pipeline, opportunities, and performance manually or across disconnected systems. This makes it difficult to see what actions are producing results, where performance is slipping, and what needs to change to hit sales targets.

Money Mind Mapp connects sales goals, activity metrics, opportunity tracking, forecasting, and performance accountability into a structured system. It is designed to help automotive sales professionals and managers better understand how daily actions connect to monthly results.

• check Sales goal and activity tracking.
• check Forecasting and projection logic.
• check Opportunity and pipeline visibility.
• check Performance accountability workflows.
• check AI/ML-ready structure for recommendation and trend analysis.
• check Designed for sales coaching, planning, and performance improvement.

• AI/ML forecasting — Projection of monthly performance from current activity, pipeline state, and historical conversion patterns.
• AI recommendations — Next-best-action signals for salespeople and managers — opportunities that need attention, activity gaps versus goal pace.
• AI trend analysis — Surfacing what activity patterns correlate with hitting versus missing target.

Many businesses using QuickBooks still depend on manual processes for invoice follow-up, payment reminders, recurring billing, and customer communication. These workflows are often fragmented across accounting software, email, SMS tools, spreadsheets, and payment processors.

GreenBacks is designed as a multi-tenant SaaS platform that connects with QuickBooks Desktop and QuickBooks Online, imports financial and customer data, and enables businesses to automate invoice reminders, payment links, recurring billing workflows, and customer-facing payment communication.

• check Multi-tenant architecture supporting clients, companies, users, and company-level roles.
• check QuickBooks Desktop integration through QuickBooks Web Connector.
• check QuickBooks Online integration roadmap and provider-aware design.
• check Payment provider abstraction for Fortis, Payabli, and future processors.
• check Email/SMS communication workflows.
• check Cadence rules for invoice reminders and customer follow-up.
• check Company-specific provider configuration.
• check Strong focus on auditability, security, role-based access, and operational accountability.

• AI message drafting — Context-aware invoice reminders and customer follow-ups that adjust tone based on cadence stage and customer payment history.
• AI classification — Payment-behavior classification that flags at-risk receivables and surfaces customers who need a different cadence or escalation path.
• AI summarization — Condensing long customer communication threads (email + SMS) into a single readable status so company users see the situation without scrolling.