01Low-risk first build
AI Prototype Sprint
One real workflow turned into a private AI prototype on your infrastructure, with a documented path to production.
- Typical span
- ~3 weeks
- Commercial shape
- Fixed scope
Services
Private AI systems built around production reality.
Engence builds AI systems that run on client infrastructure, with the evaluation, integration, permissions, and deployment work needed to make them usable in real operations.
Engagement shapes
Start with the smallest step that reduces risk.
The services below are execution domains. Most work begins as one of these four commercial shapes.
02Harden and deploy
Production Build
Take the prototype to production with evaluation, permissions, monitoring, and deployment controls on the client's environment.
- Typical span
- 4-8 weeks
- Commercial shape
- Project scope
03Operate and expand
Embedded AI
Ongoing build and operational support once the first private AI workflow is live and needs to grow safely.
- Typical span
- Monthly
- Commercial shape
- Retainer
04Optional door-opener
AI Audit
A short diagnostic to identify where private AI moves a metric, what has to stay private, and what the safest first build should be.
- Typical span
- ~1 week
- Commercial shape
- Paid diagnostic
Execution domains
Choose the domain after the wedge is clear.
Once the workflow, data boundary, and deployment environment are understood, the build usually sits in one or more of these delivery domains.
01Learn
AI Agents
Agent systems deployed inside client-controlled environments with permissions, audit trails, human review, and measurable reliability.
- Workflow automation with human-in-the-loop checkpoints
- Secure tool integrations across CRMs, support desks, internal systems, and knowledge bases
02Learn
Computer Vision
Detection, recognition, inspection, and monitoring systems deployed at the edge or inside controlled client environments.
- Detection and recognition pipelines tuned to real environments
- Inspection and alerting systems with measurable false-positive controls
03Learn
Custom AI/ML
Custom models, retrieval systems, pipelines, and evaluations for problems that need private deployment, tighter controls, or non-standard behavior.
- Retrieval, ranking, classification, extraction, and prediction pipelines
- Model evaluation tied to product and operational metrics
Delivery method
A simple structure for uncertain AI work.
Every build starts by reducing ambiguity. We define the workflow, respect the data boundary, prove the behavior, and only then expand the system.
Define the system
Map the workflow, data boundary, infrastructure constraints, risks, and success metrics before choosing models or tools.
Prototype the critical path
Build the smallest working slice that proves the behavior and exposes the failure modes.
Engineer for production
Add integrations, evaluation, observability, fallbacks, permissions, and deployment controls on the target environment.
Operate and improve
Monitor quality, review edge cases, tune thresholds, and iterate from real production feedback.
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?