Knowledge base

The terms, defined plainly.

A working glossary of the software, AI and infrastructure terms that come up when you are deciding what to build. Written to explain the trade-off, not to sell you the acronym.

21 terms

A

Agentic WorkflowAI & MLA multi-step process where one or more AI agents carry out a task autonomously, coordinating tools and decisions along the way.
AI AgentAI & MLAn AI system that takes actions toward a goal (calling tools, querying systems and making decisions), rather than only producing text.
APIEngineeringAn Application Programming Interface. The defined contract through which one piece of software talks to another.

C

CI/CDCloud & DevOpsContinuous Integration and Continuous Delivery, automating the path from a code change to a tested, deployable release.

D

Data WarehouseDataA central store optimised for analysing large volumes of data across an organisation, separate from the systems that run it.

E

ETLDataExtract, Transform, Load. The pipeline that moves data out of source systems, cleans it into a consistent shape, and loads it into a warehouse for analysis.

F

Fine-tuningAI & MLFurther training a pre-trained model on a focused dataset so it performs better on a specific task or in a specific style.

I

Infrastructure as Code (IaC)Cloud & DevOpsDefining servers, networks and cloud resources in version-controlled files instead of configuring them by hand.

K

KubernetesCloud & DevOpsAn open-source system for running and coordinating containerised applications across a cluster of machines, restarting failures and scaling with demand.

L

Large Language Model (LLM)AI & MLA neural network trained on vast amounts of text to predict and generate language, and the engine behind modern AI assistants.

M

O

ObservabilityCloud & DevOpsThe ability to understand what a running system is doing from the outside, using the signals it already emits: logs of what happened, metrics, and traces.

P

R

Retrieval-Augmented Generation (RAG)AI & MLA technique that grounds a language model in your own data by retrieving relevant documents at query time and feeding them to the model.

S

ServerlessCloud & DevOpsA cloud model where you run code without provisioning or managing servers: the provider scales it on demand and you pay only for what actually executes.

T

Technical DebtEngineeringThe future cost of a shortcut taken today, code that works now but will slow every change until it is addressed.

V

Vector DatabaseDataA database that stores data as numerical embeddings and retrieves by similarity, powering semantic search and RAG.

Z

Zero TrustSecurityA security model that trusts nothing by default and verifies every request, rather than trusting anything inside the network.

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No glossary needed on the call. Tell us the problem in your own words and we will translate.

01A senior engineer reads it. Not a form queue, and not an account manager.
02We reply either with questions or with a straight answer that we are not the right fit.
03If it looks like a fit, a technical call with the person who would actually run the delivery.
04Then scope, effort and risk in writing, before anyone signs anything.

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