We add intelligent features to existing applications — recommendation engines, invoice parsing, automated code generation, AI chatbots, and predictive suggestions — trained on your specific data, not generic models. We built custom MCU infrastructure to train proprietary AI for PrintPlanr and Element IQ without sharing sensitive data with third-party platforms.
AI adds value when it removes friction from a decision a human makes repeatedly — or when it processes information at a scale or speed that humans can't match.
A customer browses your product catalogue to find what they need. A recommendation engine surfaces what they need before they finish searching — based on their history, their requirements, and patterns from similar customers. PrintPlanr's product recommendation engine does exactly this.
Invoices checked against contracts by hand. Purchase orders compared to agreed terms manually. This takes time and misses things. AI invoice parsing — as we're building for Element IQ — validates invoices against contract terms automatically, flags discrepancies, and catches duplicate charges before payment is processed.
A significant proportion of support tickets ask the same questions that are already answered in your documentation, knowledge base, or previous tickets. An AI chatbot trained on your specific product and support history answers these automatically — reducing ticket volume and freeing your support team for genuinely complex issues.
Production codes that follow rules based on job specifications. Job descriptions that use consistent language patterns. Form fields that are almost always populated the same way for similar job types. AI auto-generation and auto-suggestion removes the repetitive entry while keeping the human in control of the final decision.
Which press is optimal for this job's specifications? Which supplier is best for this order's requirements? Which resource should handle this job type? These matching decisions follow patterns that AI learns from historical data — and can apply in milliseconds at the point of decision.
Job scheduling with multiple resources, varying job durations, deadline constraints, and competing priorities is difficult to optimise manually. AI scheduling — as we're building for Element IQ — factors in all constraints simultaneously and produces an optimised schedule that a human would take hours to calculate.
All reference the real work we've done on PrintPlanr, Element IQ, and other platforms.
PrintPlanr is our most complete AI implementation to date. Here's exactly what we built, how, and why.
PrintPlanr had years of production data — job specifications, press assignments, production outcomes, customer orders, product selections. That data was a training resource sitting unused. Rather than wrapping a generic third-party AI API around the platform, we built our own MCU (Model Compute Unit) server infrastructure to train models specifically on PrintPlanr's domain. The models understand print production in a way no general-purpose AI does — because they were trained exclusively on print production data.
Element IQ is a mature field service ERP — 20+ years of production data, complex operations, enterprise clients. Adding AI to a system this established requires surgical precision — the AI must enhance workflows without disrupting the existing operations that enterprise clients depend on. We're adding two AI capabilities: invoice parsing to automate contract compliance checking before payment, and intelligent job scheduling to optimise resource allocation. Both address genuine operational pain that manual processes currently handle inefficiently.
A general-purpose AI doesn't know that a 4-colour offset job on 150gsm coated stock should go to press 3 not press 1. A model trained on your production history does. Domain specificity is the difference between AI that helps and AI that gives plausible-sounding wrong answers.
We don't send your production data to OpenAI or any other third-party platform for training. We built MCU server infrastructure specifically to train models on client data in a controlled environment. The model is trained on your data, owned by you.
We add AI as an enhancement layer, not a replacement of existing functionality. Users can accept or override every AI suggestion. The system works without the AI if a model needs retraining. No workflow dependency on a single model.
Adding AI to a mature production system requires understanding how that system works deeply. We've been building and maintaining complex applications for 23 years. We know how to add AI without breaking what's already working.
Data quality assessed first. Model trained and validated second. Deployed surgically into your existing application third.
We assess your historical data quality and volume, and map where in your application the AI feature integrates.
Model trained on your domain data. Accuracy validated against holdout sets. Benchmarked against the current manual baseline.
AI feature integrated into existing application. All existing workflows preserved. UAT with real users on real data.
Model performance monitored. Accuracy tracked against actuals. Retraining scheduled as new data accumulates.