AI and ML systems wired to operational outcomes.
We build lakehouses, real-time pipelines and ML and GenAI systems that drive decisions, work orders and revenue. Not notebook demos. Not dashboards that nobody opens. Systems that show up inside the tools your operators already use.
What we deliver
Lakehouse architecture
Iceberg or Delta on S3 or ADLS with governed access and unified batch plus streaming.
Real-time pipelines
Kafka, Flink, Spark Structured Streaming and CDC pipelines with end-to-end SLOs.
ML and MLOps
Training, registry, deployment, monitoring and feedback loops with MLflow, Kubeflow or SageMaker.
Grounded GenAI
Enterprise RAG with vector stores, evaluation harnesses, PII safeguards and hallucination controls.
Decision intelligence
Reverse ETL into CRM, ERP, CMMS and marketing so models actually change what people do.
Data governance
Lineage, contracts, quality SLOs and privacy controls embedded into delivery, not added at audit time.
Why teams choose FictiveBox
How we work
Frame
Use case discovery with operators, not just data scientists. Success metric written in business language.
Ground
Data contracts, feature pipelines, governance and evaluation harnesses before any model is trained.
Model
Training, evaluation, registry and deployment with CI for models, not just code.
Operate
Monitoring, drift detection, retraining and reverse ETL into the systems of action.
AI/ML Solutions We Build
Production-grade AI products we deliver as accelerators on top of your data and operations stack.
Predictive Maintenance Engine
Anomaly detection and remaining-useful-life models wired into CMMS work orders for industrial fleets.
View DetailsEnterprise RAG Copilot
Grounded GenAI copilots over manuals, contracts and CMMS with evaluation harnesses and PII safeguards.
View DetailsComputer Vision QA
Edge-deployed defect detection and visual inspection wired into MES with measured drift and latency.
View DetailsDemand & Capacity Forecasting
Hierarchical forecasting and capacity models wired into ERP planning runs and S&OP cycles.
View DetailsTools & technologies
Common questions
- What does FictiveBox's AI and ML engineering service include?
- Lakehouse architecture, real-time data pipelines, applied machine learning with MLOps, grounded generative AI and retrieval-augmented generation, decision intelligence, and data governance. The emphasis is on systems that appear inside the tools operators already use rather than standalone dashboards.
- When should a company move from AI experiments to AI engineering?
- When a model works in a notebook but nothing downstream changes. The gap is rarely the model: it is feature pipelines, a model registry, monitoring, evaluation harnesses and getting predictions back into operational systems. That is the engineering layer this service builds.
- What kinds of AI projects does FictiveBox take on?
- Predictive maintenance wired into CMMS work orders, enterprise retrieval-augmented copilots grounded in your own content, computer vision for quality inspection, and forecasting and demand planning. Each is delivered as a production system with retraining and monitoring, not as a proof of concept.
- How do you keep generative AI answers grounded?
- Retrieval-augmented generation over your own governed sources, with evaluation harnesses and monitoring around the model so answer quality is measured rather than assumed, and with governance applied to what the system is allowed to retrieve and say.
- Which data and ML technologies are supported?
- Snowflake, Databricks, BigQuery, Iceberg, Kafka, Flink, dbt, MLflow, LangChain and PyTorch, with delivery running Frame, Ground, Model and Operate stages.
Ready to engineer your next platform?
Book a 30-minute consultation with a senior solutions architect. No slide deck. Just answers.