Cutting Edge Artificial Intelligence
PiSrc engineers build AI systems that go beyond prototypes and demos to production deployments that run inside your infrastructure. We work across the full stack: retrieval-augmented generation, agentic orchestration, embedding pipelines, guardrails, and the classic machine learning foundations that still power the best solutions. Large language models opened the door. We help enterprises walk through it. The question has shifted from "What can AI do?" to "What should AI do first?" We help organizations answer that question by identifying where AI creates the most leverage, then building and deploying it within existing infrastructure. No new servers to provision. No shadow IT. AI that works inside the platforms your teams already use.
Agentic AI: The Next Operating Layer
Agentic AI systems do not just respond to prompts. They plan, reason, use tools, and execute multi-step tasks autonomously within governed boundaries. The difference in practice is substantial: translating a million-page product library with domain accuracy, generating catalog content with zero hallucinated specifications, converting a PDF archive into a multilingual web presence in a single workflow step. These are production deployments, not pilots. And the organizations that get there first build compounding advantages that late adopters cannot shortcut. We make the case in detail, with results from real deployments, in our white paper. [Read: The Agentic Enterprise: More Human, Not Less](/insights/agentic-enterprise)
Our AI Product Offerings
Prism
Prism is our AI conversation platform for enterprise websites. It ingests your full knowledge base and answers visitor questions with depth and citation before ever asking for contact information. As conversations develop, agentic playbooks qualify leads through natural dialogue, and a Bayesian analysis layer converts browsing behavior into a quantified customer acquisition probability that gives sales teams something more useful than a lead score. One deployment surfaced a non-obvious correlation between compliance documentation views and sales qualification — a signal that conventional analytics had missed entirely. The sales team received lead briefs with browsing context, conversation history, and a probability grounded in historical outcomes. [See how Prism works](/products/prism) or [read the case study](/insights/from-chat-to-customer-probability-ai-driven-sales-intelligence)
Metaphora
Metaphora is our AI content transformation suite. It converts PDFs into responsive HTML, maps that HTML into authored AEM components, and translates content across languages with brand voice and embedded business rules intact. Each tool in the suite runs on your existing AEM infrastructure with no intermediary processing servers. In production, Metaphora has translated hundreds of thousands of technical SKUs across a dozen languages, handling domain-specific terminology and ISO formatting that general-purpose translation tools consistently get wrong. It has also converted entire PDF libraries into web-native content, unlocking multilingual publishing, site search, mobile access, and SEO indexing through a single transformation step.
Our Approach
Governance first
We design review workflows, escalation paths, guardrails, and audit trails before deploying AI that generates, translates, or acts. MIT research found that 95% of enterprise AI pilots fail to scale. The constraint is operational fit, not the technology itself. This is where our engagements are built differently.
Infrastructure integration
Our AI runs on the platforms and data pipelines your organization has already built. Cloud environments, content management systems, API integrations — these are precisely the tools that agentic AI needs. You likely have most of what is required already in place.
Human oversight at the right moments
Every deployment we have described shifted operational and repetitive work to AI while preserving human judgment where it matters: editorial approval, sales strategy, content governance. The work that stays is not residual. It is the work those teams were hired to do.
Full stack capability
We work across retrieval-augmented generation, Bayesian probabilistic models, embedding pipelines, deep learning, and classical machine learning. The right technique depends on the problem. We do not default to the newest approach when a proven statistical method delivers a better result.
Machine Learning Capability Areas
Not every problem is a nail, so a hammer is not always the right tool. Large language models and agentic AI are genuinely powerful, but many of the most effective solutions we have built rely on traditional statistics, regression models, and collective intelligence techniques that are faster, more explainable, and better suited to the problem at hand. We choose the right method for the problem, not the most talked-about one.
Auto Classification
Identify hidden groups within and clusters to better anticipate everything from a user's movie preferences to suggested items in a product catalog.
Pattern Matching
We employ everything from regression models to deep learning solutions to identify patterns and help predict expected outcomes.
Visual Recognition
Facial recognition, image auto-tagging, video analysis, and text recognition are all part of this growing field of visual recognition propelled by modern AI.
Anomaly Detection
AI can alert us of anomalies in normal business activity and user behavior that can help us get ahead of disruptions and be ready for opportunities.
Predictive Optimization
Advanced algorithms and sophisticated methods of collective intelligence can help save time and money, and make decisions that are ahead of the curve.
Propensity Scoring
Website analytics can benefit from AI to develop propensity scores for potential prospects to buy a product or become a customer or client.
