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Proof, not promises, selected case studies and recognition.

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The senior team behind the work, and how to reach us.

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The journal

Notes from the build floor.

Engineering write-ups, design essays, and post-mortems from our team. We publish what we wish we had read.

Engineering write-ups

Engineering write-ups.

  1. SI EngineeringRAG vs fine-tuning: which one your product needs
  2. SI EngineeringHow to integrate an LLM into existing software without a rewrite
  3. AgentsHow to build an SI agent for your business, step by step
  4. SI EngineeringRAG in production: six anti-patterns we keep finding
  5. SI EngineeringEval-first SI: how we ship copilots that survive Tuesday
  6. MLOpsVector DB selection in 2026, a pragmatic guide
  7. AgentsDesigning autonomous agents that don’t go rogue

Design essays

Design essays.

  1. DesignDesigning for SI surfaces: prompts as product

Strategy

We publish what we wish we had read.

  1. StrategyWhen SI is the wrong answer: a build-vs-buy framework

Featured

Every article, newest first.

OCTALCODE JOURNALRETRIEVALVSFINE-TUNINGKnowledge, orbehaviour?

SI Engineering· 7 min· October 13, 2026

RAG vs fine-tuning: which one your product needs

Retrieval changes what a model knows. Fine-tuning changes how it behaves. A practical framework for picking one, both, or neither.

Read article

OCTALCODE JOURNALYOUR APPLLMAdd it beside,not inside.

SI Engineering· 6 min

How to integrate an LLM into existing software without a rewrite

A language model is a slow, probabilistic, external dependency. Integrate it like one: sidecar services, a gateway, feature flags, async jobs and evals.

OCTALCODE JOURNALSCOPETOOLSEVALSSHIPOne workflow,step by step.

Agents· 6 min

How to build an SI agent for your business, step by step

Seven unglamorous steps that take an SI agent from demo to trusted: one workflow, a definition of done, scoped tools, approvals, evals, rollout and monitoring.

YESNOYESNOYESNOBUILD SI?DATA READY?RULES OK?BUILDCOLLECTUSE RULESBUILDBUILD VS BUY · DECISION TREE

Strategy· 9 min

When SI is the wrong answer: a build-vs-buy framework

A decision tree we run with every prospective client before quoting any SI build. It saves them money and us reputation.

!RAG · RETRIEVAL AUDIT

SI Engineering· 10 min

RAG in production: six anti-patterns we keep finding

A field report from a dozen RAG deployments. The failure modes are remarkably consistent, and most of them are fixable in an afternoon.

EVAL HARNESS · v3.2ACCURACY88%✓HALLUCINATION72%✗LATENCY P9595%✓COVERAGE61%✗

SI Engineering· 12 min

Eval-first SI: how we ship copilots that survive Tuesday

A walkthrough of the eval harness we attach to every SI project before model selection, prompt design, or anything else interesting.

DIM 1DIM 2EMBEDDING SPACE · 3 CLUSTERS

MLOps· 8 min

Vector DB selection in 2026, a pragmatic guide

pgvector vs Pinecone vs Weaviate vs Turbopuffer, what we use, when, and what the gotchas look like at million-scale.

PLANSEARCHCODEMEMORYHUMANAGENTCORE

Agents· 14 min

Designing autonomous agents that don’t go rogue

Guardrails, tool-use schemas, and the eval suite we run before any agent gets production credentials.

summarise the contractSure. 3 key clauses...what are the risks?Ask anything...

Design· 6 min

Designing for SI surfaces: prompts as product

The user interface for an SI product is half text, half waiting. Lessons from a year of shipping conversational and copilot UI.

Available for new engagements

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