Insights
AI & Machine Learning
Agents, LLM pipelines, RAG, forecasting and computer vision — how they are built and what breaks in production.
AI & Machine Learning
AI & Machine Learning ·
Workflow or agent? How we decide before writing a line of code
If every step of a process can be written down before it runs, build a workflow and put an LLM call only where something has to be read and interpreted. Use an agent only when the next step depends on what the last one found, and cap it hard.
8 min read
AI & Machine Learning ·
AI Agent Costs: Budgets, Caching and Routing That Work
Agent bills grow with calls per task, not with the token price. We measure cost per completed task, keep the prompt prefix cacheable, route steps to the cheapest model that passes our evals, and cap every task by tokens.
9 min read
AI & Machine Learning ·
RAG, Fine-Tuning or Long Context: How We Choose
Start with the simplest setup that passes an eval on your own questions: a cached long prompt for a small, stable corpus, RAG when knowledge changes, must be cited or is access-restricted, and fine-tuning only for behaviour. Here is the order we check things in and where each option breaks.
9 min read
AI & Machine Learning ·
Why Hybrid Search Beat Pure Embeddings in Our Invoice AI Pipeline
Pure dense embeddings failed on alphanumeric serial numbers in our Invoice AI pipeline. Combining PostgreSQL tsvector full-text search with pgvector cosine distance lifted top-1 matching accuracy from 61.4% to 94.8%.
6 min read
AI & Machine Learning ·
Why pgvector Replaced Qdrant in Our LLM Feedback Pipeline
We migrated 5 million vector embeddings from a standalone Qdrant cluster to PostgreSQL 16 with pgvector 0.7. Here is how operational overhead dropped while maintaining sub-50 ms search latencies.
5 min read
AI & Machine Learning ·
How AI agents work: the architecture of a production agent
An agent is a model wrapped in perception, memory, tools, orchestration and monitoring. A practical walk through the six layers, what breaks in each one, and what we do about it.
6 min read
AI & Machine Learning ·
RAG in production: the checklist we run before go-live
Retrieval-augmented generation demos in a day and fails in a month. The twelve checks — data, retrieval, generation, operations — that separate a pilot from a system people rely on.
5 min read