Stuck pilots
AI experiments exist, but they are not reaching real users or workflows.
Production AI
Fitzroy builds AI-backed workflows that connect to real data, tools, permissions, and human decisions—and can be evaluated and operated after launch.
Where we help
Document intake and structured extraction
Retrieval and model orchestration
Deterministic business actions
Approval and permission boundaries
Evaluation, audit, and monitoring
Existing application integration
When to engage
Manual bottlenecks
Teams still rely on repetitive review, routing, and reporting work.
Fragmented data
Useful information lives across tools, systems, documents, and teams.
Production concerns
Security, reliability, governance, and human review need to be designed in.
AI systems architecture
Foundation Layer
Enterprise Data
Documents
Communication
External Sources
Data Pipeline
Connectors
Chunking
Embeddings
Indexing
Model Layer
Use the right model for each job.
FITZROY
OpenAI
Agent Layer
Memory
Planning
Tool Use
Guardrails
Evaluation
Tools & Actions
Search
Code Execution
APIs & Services
Databases
Automation
Outcomes
Automated Workflows
Insights & Reports
Customer Responses
Human Review
Observability & Control
Logging
Monitoring
Evaluation
Alerts
Audit
01 · Context
Connect the operating environment.
Enterprise Data
Documents
Communication
External Sources
02 · Intelligence
Route each task through the right model.
FITZROY
OpenAI
03 · Execution
Give agents secure access to tools and systems.
Search
Code Execution
APIs & Services
Databases
Automation
04 · Outcomes
Turn intelligence into measurable operations.
Automated Workflows
Insights & Reports
Customer Responses
Human Review
Observability & Control
Logging
Monitoring
Evaluation
Alerts
Audit
Production AI architecture
01
Ingest and understand
Connect internal documents, APIs, databases, and operational systems. Structure the data layer so AI workflows can retrieve the right context reliably.
02
Reason and orchestrate
Route requests across models, tools, and business rules. Apply retrieval, validation, and workflow logic before outputs reach users.
03
Deploy and optimize
Run inference through cloud-native infrastructure with observability, latency controls, cost visibility, and production safeguards.
04
Integrate into operations
Embed the system into the actual workflow: approvals, reporting, customer operations, internal tools, and backend services.
Practical diagnostic
Is your AI system ready for production?
Use the Fitzroy AI Production Readiness Checklist to identify gaps across workflow design, data, infrastructure, governance, and operations.
Deliverables
Integrated application
A bounded workflow with a clear user and system interface.
Evaluation and safeguards
Test cases, validation, access rules, and human review for consequential actions.
Production path
Deployment, telemetry, incident handling, and a handover plan.
AI production-readiness diagnostic
Can this use case operate safely and usefully inside the real workflow?
Typically two working weeks; fee, access, deliverables, and acceptance criteria agreed in the statement of work.
Review scope and deliverables →Maria Ann, Fitzroy AI Labs’ bid-operations product in development, applies these patterns to industrial RFQs, clarifications, tasks, and submission readiness.