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, including client-owned runtime environments.
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 on client-controlled infrastructure with observability and iteration loops
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 or hosted API 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.
Start the conversation
Tell us what private AI
needs to do_
What workflow or decision are you trying to improve?
What systems, data, cameras, or infrastructure does the AI need to connect with?
What needs to stay inside your environment, and what would make the first production version successful?