Retrieval, ranking, classification, extraction, and prediction pipelines
Bespoke model systems
Custom AI/ML for real operations.
We build AI/ML systems around the data, constraints, metrics, and deployment realities of the product or operation.
What Engence builds
Production custom AI/ML with controls around the model.
The model is only one part of the system. The production work includes data paths, integrations, evaluation, monitoring, and the human decisions around automation.
Model evaluation tied to product and operational metrics
Serving, observability, and iteration loops for production systems
Deliverables
Enough structure to build, verify, and operate.
The exact scope depends on the system, but the first production path usually includes these working pieces.
Problem and metric definition
Data pipeline
Model or retrieval system
Serving and observability layer
Questions
Common questions about custom AI/ML.
When is custom AI/ML better than a standard tool?
When the data, workflow, accuracy target, latency, privacy, or integration constraints are specific enough that a generic product cannot deliver the required behavior.
Do you handle evaluation?
Yes. Evaluation is part of the build, not an afterthought. We define the metrics and failure modes before expanding the system.
Have a workflow, model, or vision system that needs to ship?
Email the problem, the current system, and what a useful first production version would need to prove.
Helpful context
- What workflow or decision are you trying to improve?
- What systems, data, or cameras does the AI need to connect with?
- What would make the first production version successful?