01
Trace
Trace representative requests through data sources, retrieval, models, prompts, tools, fallbacks, and human review. Identify where quality and reliability become opaque.
07
AI infrastructure / LLM & RAG systems
Make AI systems measurable before you scale them
A focused technical engagement for fintech teams moving an LLM, RAG, search, or agent system from a promising prototype into reliable production infrastructure.
The trigger
An AI feature is becoming business-critical, but retrieval quality, evaluation, latency, cost, observability, security boundaries, or failure behavior remain difficult to measure and control.
- Engagement structure
- Defined scope and deliverables
What changes
01
An explicit system model covering retrieval, generation, evaluation, fallbacks, and trust boundaries.
02
A quality and reliability baseline tied to real customer scenarios instead of demo prompts.
03
Performance, cost, and observability priorities grounded in production evidence.
04
A staged architecture plan that separates immediate controls from premature platform work.
Working method
02
Measure
Define scenario-based evaluation, telemetry, latency and cost baselines, failure categories, and the operating signals leadership and engineers actually need.
03
Scale
Prioritize architecture, evaluation, observability, security, and performance changes, then sequence implementation around the highest-consequence risks.
Concrete deliverables
- LLM, RAG, or agent-system architecture map
- Evaluation framework and representative scenario set
- Reliability, latency, cost, and observability baseline
- Retrieval and model failure analysis
- Security and prompt-injection control recommendations
- Prioritized production-hardening roadmap
Good fit when
- An LLM, RAG, search, or agent workflow is already in use or approaching production
- The system supports a consequential customer or internal workflow
- Engineering can provide representative traces, evaluations, and architecture evidence
- The goal is measurable reliability—not a generic AI strategy presentation
Related evidence & tools
AI engineering workflows
Define permission, review, security, and measurement controls for AI-assisted software delivery.
Open →AI-agent guardrail proof
Review the public implementation evidence behind our approach to agent safety and completion controls.
Open →Production LLM/RAG readiness
Use a decision checklist for evaluation, retrieval, reliability, observability, cost, and security.
Open →Internet-scale infrastructure proof
See Francisco's attributed operating evidence across routing, CDN, telemetry, and reliability.
Open →Frequently asked
Scope before assumptions.
What does an AI infrastructure consultant review?
The review follows real requests across data sources, retrieval, prompts, models, tools, evaluation, fallbacks, observability, cost, and human review. The goal is to identify which system changes will most improve measurable quality and reliability.
Can you help with LLM and RAG evaluation?
Yes. We define representative customer scenarios, quality and failure categories, evaluation datasets, model comparisons, production signals, and an operating process for reviewing results over time.
Is this an AI strategy engagement or implementation work?
It is a technical operating engagement. Scope may include assessment, architecture, evaluation, observability, and implementation planning. A proposal states the exact systems, evidence, deliverables, and implementation responsibility before work begins.
Next move