Blog · Engineering

Architecture, tracing, and audit trails.

Engineering deep-dives on document AI architecture: ReAct, multi-LLM consensus, tracing, audit trails, multi-tenancy, and the production-grade patterns behind fluex's platform.

Engineering pillar

ReAct architecture for documents

A technical deep-dive on why fluex's document extraction is built on a ReAct agentic architecture rather than pure RAG, classical OCR, or single-pass LLM extraction — with three worked examples and an honest take on when not to use it.

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Engineering

Prompt injection in document AI: the threat model nobody scopes

Every document your pipeline ingests is untrusted instruction text. The threat model, three real attack patterns, and the four defenses that actually hold.

9 min
Engineering

Audit trails for non-deterministic outputs

How to log AI extractions in a way that holds up to reproducibility, regulatory audit, and customer "why did you extract this?" questions — with the actual schema we use at fluex.

8 min
Engineering

Tracing agentic document extraction

How to make multi-step LLM workflows debuggable. OpenTelemetry span design, sampling strategies, and the structured logs that turn a black box into a flight recorder.

8 min
Engineering

SOC 2 Type II for AI startups: what to build in

The five architectural commitments that turn the SOC 2 audit from a quarter-long cleanup project into an emergent property of your platform.

9 min
Engineering

Multi-tenancy in .NET: how we isolate data by subscription

Tenant-id patterns, EF Core interceptors and automatic auditing we run in production.

8 min
Engineering

OCR is a commodity. Context is the difference.

Why obsessing over OCR accuracy is the wrong metric in 2026.

7 min