EngineersGovernance & Compliance
Technical readiness checklist for AI in production
Eight verifiable blocks, each with required evidence, to decide whether an AI system is ready for production — not just for a demo.
5 min read
Engineering track
Architecture, integration and automation — no slide-level abstraction.
Architecture, integration, industrial telemetry and engineering practice: technical decisions explained in enough detail to reproduce.
Reference architectures
Topologies that survive network loss and plant change.
Industrial data
Modeling, cardinality, retention and pipeline observability.
Applied AI
From sensor to alert, with temporal validation and named diagnosis.
Publications in this track
EngineersData Architecture
Internal AI platform: from pilot to product
Golden paths, use case catalog, reusable components, environments, CI/CD, SLOs, and a support model for scaling AI beyond the pilot stage.
4 min read
EngineersGovernance & Compliance
Instrumenting cost and quality in AI production
How to measure tokens and cost per unit of work, attribute spend by team, monitor quality, and back your ROI calculation with real data.
4 min read
EngineersApplied AI
Predictive maintenance: from sensor to the alert someone actually acts on
Why most predictive maintenance pilots don't survive past month six — and what changes when the alert is designed together with operations.
2 min read
EngineersData Architecture
Factory floor data collection architecture with MQTT and time series
From PLC to dashboard: topology, broker selection, topic modeling, and data retention for reliable industrial telemetry.
2 min read
EngineersAI & Agents
Serving LLMs on-premise: GPU, quantization, and throughput
A technical guide to sizing GPU and memory, applying quantization, continuous batching, and KV cache when serving language models inside the company.
5 min read
EngineersGovernance & Compliance
Immutable AI decision log with hash chaining
How to build an append-only, hash-chained record of AI decisions — with periodic anchoring, auditable correction, and vendor-independent export.
5 min read
EngineersGovernance & Compliance
Policy as code for AI governance
How to turn your AI usage policy into executable rules: risk classification, attribute-based authorization, guardrails and CI tests.
4 min read
EngineersGovernance & Compliance
PII detection and masking in AI pipelines
How to detect, mask, and, when authorized, re-identify personal data in AI pipelines without breaking inference or data-subject rights.
5 min read
EngineersAI & Agents
Hybrid retrieval and per-user permissions in the index: how not to leak data in RAG
Lexical plus vector search with result fusion, source-inherited ACL applied at query time, revocation, multi-tenancy, and automated leakage tests.
5 min read
EngineersAI & Agents
RAG engineering: chunking, embeddings, and reranking
Structure-aware chunking, embedding versioning, reindexing, reranking, and retrieval evaluation — the technical decisions that determine RAG quality.
5 min read
EngineersIntegration & Legacy
An anti-corruption layer to connect AI to a legacy ERP
API contracts, CDC vs. batch, idempotency, identity propagation, and load limits: the engineering of an anti-corruption layer between AI and core systems.
5 min read
EngineersGovernance & Compliance
Capture pipeline for AI interactions: from conversation to auditable knowledge
How to design the event schema, partitioning, retention, and indexing needed to turn prompts, retrieved context, and human edits into a reusable knowledge asset.
4 min read
EngineersGovernance & Compliance
AI gateway: DLP and outbound data control
How a corporate AI gateway becomes the single point of egress — identity, routing, DLP on request and response, cost limits and shadow AI blocking.
6 min read
EngineersAI & Agents
Continuous LLM evaluation: regression datasets that survive model swaps
How to build a regression dataset from real cases, pick metrics that matter, and use an LLM-as-judge calibrated against human review to block bad deploys in CI.
5 min read
EngineersAI & Agents
Prompts as code: versioning, testing, and evaluation
Prompts and system instructions treated with the same discipline as code: repository, PR review, automated tests, canary rollout, and rollback.
4 min read
EngineersAI & Agents
Reference architecture for enterprise AI: layers, contracts, and perimeter
Model gateway, orchestration, retrieval, memory, identity, observability, and audit trail: the technical design behind an enterprise AI that isn't locked to a single vendor.
4 min read
Put it to work
Bring this into your company with the e.works platform
Everything we cover here — AI governance, legacy system integration, audit trails and preserved corporate knowledge — is available on the e.works platform at eworks.cloud.
e.works infrastructure on AWS
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On-premises, in your environment
The same platform running in your company's data center or private cloud, when data sovereignty requires that nothing leaves your perimeter.
In either model your data stays yours — with access control, audit logging, configurable retention and guaranteed availability.
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