Engineering Insights
Deep dives on production AI systems, DevOps patterns, and the hard problems we've solved in the field.
Self-Hosting LiveKit at Scale: Architecture from 90K+ Calls/Month
The complete production architecture for self-hosting LiveKit — standalone server, Python agent workers, LiveKit Egress on ECS, and multi-metric autoscaling from a team running 90K+ calls/month with five selectable AI pipelines.
Cloudonix Core Concepts: CXML, Sessions, and Building Voice Agents
What CXML, sessions, the Converse verb, and Cloudonix's SDKs actually are — and how the pieces fit together to build a production voice agent on the Cloudonix platform.
Air-Gapped LLM Deployment: Run Private Models with Zero Egress
A practitioner's guide to air-gapped LLM deployment — the two architectures that work (self-hosted open-weight models vs Bedrock via VPC endpoint), GPU sizing, model ingestion, and the security controls regulated buyers require.
Connect LiveKit to the Phone Network with Cloudonix SIP Trunking
A practitioner's guide to connecting a self-hosted LiveKit voice stack to the public phone network with Cloudonix SIP trunking — SIP URI registration, CXML inbound routing, outbound BYOC, and the SBC work you skip.
AI Engineering Consulting Startups: What They Are and When to Choose One
A definition guide for CTOs evaluating AI engineering consulting startups versus large agencies — covering what they build, how they differ, when to choose one, and what to look for before signing.
RAG Pipeline Chunking Strategies: Split Documents for Better Retrieval
A practitioner's guide to RAG pipeline chunking strategies — covering fixed-size, semantic, structural, and hierarchical approaches with chunk size guidance and a decision matrix for each corpus type.
How to Hire AI Engineers in 2026 (Build vs Partner)
A decision framework for CTOs and engineering leads evaluating whether to hire AI engineers in-house or partner with an AI engineering firm — covering salary benchmarks, hiring timelines, delivery speed, and when each path wins.
LLMOps in 2026: AI Demo to Production Guide
A 2026 LLMOps guide for teams stuck at the demo stage — the six-layer production stack (serving, evals, observability, CI/CD, cost control, governance), a phased rollout, and the mistakes that keep AI systems out of production.
How to Add AI to Your SaaS Product Without Hiring a Machine Learning Team
A practical guide for SaaS founders and CTOs on adding AI features to an existing product — covering the 4-phase framework, RAG vs agents vs fine-tuning decision guide, minimum viable LLMOps, and build vs consult trade-offs.
Questions to Ask an AI Consulting Firm Before You Sign: A CTO's 8-Point Checklist
A practical buyer guide for CTOs and engineering leaders evaluating AI consulting firms — eight questions that separate credible AI engineering partners from over-promising vendors, with red flags and green flags for each.
AI Agents in Production: 7 Architecture Mistakes That Sink Your System
The 7 most destructive AI agent architecture mistakes in production: god agents, stateless memory, missing tool-call guardrails, no observability, absent eval loops, unbounded cost spirals, and no human escalation path — with before/after fixes for each.
LLM Fine-Tuning vs RAG: A Production Decision Framework for Engineering Teams
A practical decision framework for engineering teams choosing between RAG and LLM fine-tuning in production — with real cost comparisons, a decision flowchart, and a guide to LoRA, QLoRA, SFT, and DPO.
How to Build a Clinical Trial Dashboard Teams Actually Use
A practitioner's guide to clinical trial dashboards — the four metric families to track, real-time vs batch reporting, natural-language-to-SQL access, and when a custom build beats off-the-shelf CTMS reporting.
LLM Cost Optimization: Cut AI Inference Costs 47–80% Without Sacrificing Quality
A practical guide to LLM inference cost optimization in production — covering model routing, prompt caching, semantic caching, quantization, batch inference, context compression, output length control, and OSS models for narrow tasks.
Why Your RAG Pipeline Is Failing in Production (And How to Fix It)
A diagnostic guide to the 5 most common RAG pipeline failures in production — bad chunking, missing reranking, stale indexes, no hybrid retrieval, and no eval loop — with code snippets and fixes for each.
Building Production Voice AI Agents: Latency, Architecture, and What Nobody Tells You
Why voice AI agents fail in production has nothing to do with model quality — it is the architecture. A complete guide to latency budget, WebRTC transport, LiveKit SFU, security, and observability for voice AI at 2000+ calls per day.
How to Evaluate LLM Outputs: Building Evals That Actually Catch Regressions
A hands-on guide to building LLM evaluations that catch silent regressions — the three failure modes of naive evals, the four-layer eval stack, golden dataset rot, LLM-as-judge bias, and how to wire evals into CI.
Resource Planning for AI Development Teams: A Practical Guide
A practical guide to resource planning for AI consulting and development teams — covering role composition, capacity planning across simultaneous engagements, hiring vs. contracting decisions, and retaining AI talent.
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