01
Knowledge Copilots
Retrieval-backed assistants that help users find, compare, draft, and act on trusted product or operations knowledge.
AI Products
AI systems for real workflows, not isolated demos.
Entropix Systems designs and engineers AI workflows with the product UX, data foundation, integrations, evaluation, monitoring, and governance needed for operational use.
Use cases
Production AI starts with the work people are already trying to complete.
The right automation boundary is discovered from the workflow, not chosen from a model feature list.
02
Workflow Agents
Agent-assisted steps for routing, summarizing, checking, and preparing work with human approval where risk requires it.
03
Document Intelligence
Intake, extraction, classification, validation, and review flows for documents that slow teams down.
04
Predictive Analytics
Decision support built from reliable data models, product events, operational signals, and clear evaluation criteria.
AI architecture
The model is only one layer of the system.
Useful AI needs retrieval, application UX, orchestration, data quality, integrations, permissions, evaluation, fallback paths, and monitoring.
Product Experience
Interfaces that make complex work understandable, fast, and repeatable.
Application Core
Domain models, APIs, permissions, integrations, and business logic designed for change.
AI Layer
Retrieval, agents, model orchestration, evaluation, fallback paths, and human review controls.
Data Foundation
Events, pipelines, warehouses, analytics, quality checks, and AI-readiness.
Cloud Platform
Infrastructure, CI/CD, environments, observability, security posture, and scaling plans.
Delivery process
Map the workflow before engineering the AI.
We validate the use case, confirm the data path, design the product surface, build the automation, then evaluate behavior before scale.
- 01
Map workflow
- 02
Assess data
- 03
Design AI system
- 04
Build product slice
- 05
Evaluate and monitor
Governance
Controls that make AI usable in serious operations.
AI work should include quality gates, privacy boundaries, operational visibility, and clear ownership from the beginning.
01
Workflow scope
Define what AI should assist, automate, or leave to people.
02
Data readiness
Check source quality, access, retrieval paths, and permissions.
03
Evaluation
Measure usefulness, accuracy, safety, and operational fit.
04
Privacy and security
Keep sensitive data boundaries visible from the start.
05
Monitoring
Track quality, failures, drift, and human review signals.
06
Cost control
Plan usage, model routing, fallback paths, and ownership.
Technology map
AI, product, data, and cloud decisions in one system.
Entropix chooses technology around workflow fit, maintainability, evaluation, privacy, and operational cost.
Frontend
Product interfaces, design systems, dashboards, portals, and workflow surfaces.
Next.jsReactTypeScriptTailwind CSS
Backend
Application cores, auth, business logic, integrations, and service boundaries.
Node.jsNestJSAPIs
AI
Retrieval, orchestration, review loops, monitoring, and production AI controls.
LLM AppsAgentsRAGEvaluation
Data
Trusted product data, operational reporting, and AI-ready knowledge foundations.
PostgreSQLVector DBsAnalytics
Cloud & DevOps
Environments, release systems, reliability, monitoring, and deployment maturity.
AWSGCPCI/CDObservability
Map an AI workflow
Have an AI opportunity that needs production discipline?
Bring the workflow, data sources, risk constraints, and integration targets. Entropix will help turn it into an engineered AI system.