AI and ML that changes what operators actually do.
Most enterprise AI programs stall between a working notebook and a production decision. We build the piece that closes that gap. Feature pipelines, model registries, monitoring, evaluation harnesses and reverse ETL into the tools your operators already use.
What actually breaks in this space.
Pilots that demo well and never ship
The POC hit 94% accuracy on a static dataset. Twelve months later it still has not reached a single production decision.
Models that nobody retrains
A data scientist shipped a model and then moved teams. Now it is drifting, nobody owns it, and nobody can measure what it is doing.
Dashboards that nobody trusts
The same KPI shows three different numbers across CRM, ERP and BI. The business stops looking at any of them.
GenAI hype without guardrails
A RAG demo over a wiki. No evaluation, no PII controls, no grounding metric. Exactly how enterprise AI gets a bad name.
Capabilities we bring to this industry.
Computer vision in production
Defect detection, inspection automation and visual QA deployed on edge hardware with measured latency and drift monitoring.
Forecasting and planning
Demand forecasting, maintenance planning and capacity models wired to the ERP and planning systems that act on them.
Grounded GenAI and RAG
Enterprise RAG with vector stores, evaluation harnesses, PII safeguards and hallucination controls. Copilots over CMMS, manuals, policies and contracts.
MLOps pipelines
Training, registry, deployment, monitoring and feedback loops built on MLflow, Kubeflow or SageMaker. CI for models, not just code.
Decision intelligence
Reverse ETL into CRM, ERP, CMMS and marketing so model outputs change what people do, not just what a dashboard says.
AI governance and safety
Data contracts, lineage, evaluation pipelines and privacy controls embedded into delivery, not added at audit time.
Measured, not modeled.
Shapes these engagements usually take.
Defect detection on a live production line
Computer vision on edge GPUs classifying defects in under 50ms. Integrated with the MES to auto-flag bad units before they leave the line.
RAG copilot for a 400-agent support team
Grounded answers over 14,000 internal documents. 95%+ grounded-answer rate, 40% shorter average handle time, full audit trail on every answer.
Predictive maintenance across 320M devices
Anomaly detection and remaining-useful-life models wired into CMMS. 30% lift in maintenance planning accuracy.
Tools and technologies we reach for here.
Common questions
- What AI and ML work does FictiveBox do?
- Computer vision in production, forecasting and planning, grounded generative AI and retrieval-augmented generation, MLOps pipelines, decision intelligence, and AI governance and safety.
- Why do enterprise AI pilots stall?
- They stall between a working notebook and a production decision. The missing pieces are feature pipelines, model registries, monitoring, evaluation harnesses and reverse ETL back into the tools operators already use. Without those, a model that performs well changes nothing operationally.
- How do you stop models going stale?
- By treating retraining and monitoring as part of the system rather than a follow-up project: pipelines that feed the model, monitoring that detects drift, evaluation harnesses that measure quality over time, and operator feedback closing the retraining loop.
- Which AI and ML technologies are used?
- PyTorch, LangChain, MLflow, Kubeflow, Snowflake, Databricks, Kafka, FastAPI, Triton and pgvector.
Have an AI program that stalled after the pilot?
Talk to an ML engineer who has shipped enterprise AI into production, not just into a pitch deck.