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
Engineering ∩ Intelligence
Private AI systems,shipped on your infrastructure.
Engence builds private, production-grade AI systems that run on client infrastructure, so data stays inside the client's environment.
Client infrastructureData stays inside your environmentAI agentsComputer visionCustom AI/MLEvaluation and monitoring
Engagement shapes
Start with the smallest step that reduces risk.
The wedge stays the same across every engagement: private AI on your infrastructure, with a documented path to production.
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
One private-AI wedge, three delivery domains.
AI agents, computer vision, and custom AI/ML are how Engence executes once the workflow, data boundary, and deployment environment are clear.
01Tool-using systems
AI Agents
Agent systems deployed inside client-controlled environments with permissions, audit trails, human review, and measurable reliability.
Learn more02Physical-world intelligence
Computer Vision
Detection, recognition, inspection, and monitoring systems deployed at the edge or inside controlled client environments.
Learn more03Bespoke model systems
Custom AI/ML
Custom models, retrieval systems, pipelines, and evaluations for problems that need private deployment, tighter controls, or non-standard behavior.
Learn moreHow we work
A method, not a pitch.
The same four moves on every system: from first scope to live monitoring.
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.
Patterns we ship
Private AI patterns, not fabricated case studies.
NDA-safe shapes of production AI systems, with the controls and deployment realities visible even when client names stay private.
Workflow intelligence
Agents in the loop
Agents that retrieve context, prepare work, call tools, and keep review points visible inside the client's environment.
Physical-world AI
Vision as a sensor
Vision pipelines for detection, recognition, inspection, monitoring, and alerting on edge, on-prem, or controlled cloud runtimes.
Model systems
Measured by outcomes
Custom retrieval, ranking, classification, extraction, and prediction systems with clear data boundaries and measurable outcomes.
Private deployment is designed in
If the system needs to run on your infrastructure, the data boundary, permissions, and deployment target shape the build from day one.
Evaluation before scale
Every serious AI system needs a way to measure quality, edge cases, and regressions before it grows into the workflow.
Small senior team
The people scoping the work are close to the code, architecture, and deployment path.
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?