Automation without a developer — a connector and its limits
Between hiring a developer and pasting data into a chat sits a third path: a ready-made connector in an automation platform. What it does and where it stops.
6 min read
Articles
Analysis, case breakdowns, and answers to the questions company owners actually ask us.
26 articles
Remote work
Comments on the work, not messages in the chat
An agreement from three weeks ago is technically in the chat and practically gone. Why a comment on the job stays findable and a channel message does not.
6 min read
AI and your data
How to connect company data to Claude and other AI clients over MCP
What happens when you connect an MCP server to an AI client: what the model sees, what it can do, whose account it runs on, and what to check beforehand.
6 min read
Field teams
Five views of the same job — map, table, kanban, timeline
The dispatcher wants a map, finance a table, the manager a timeline, the engineer today's list. What each of the five views is for, and what each one hides.
6 min read
AI and your data
MCP vs an API integration — who decides what happens
An API integration is a path a developer fixed in advance. MCP hands the model a set of operations and lets it choose. What that changes in cost and testing.
5 min read
AI adoption
Metrics worth tracking — and the ones that just look smart
Dashboards die because they show what was easy to count, not what anyone reacts to. What separates a real metric from a chart, and how to cut the list down.
6 min read
Remote work
Onboarding a remote employee — the first thirty days
Remote onboarding breaks on the knowledge nobody wrote down. What to prepare before day one, why a buddy matters, and checkpoints at days 7, 14 and 30.
5 min read
AI adoption
Who sees what — access permissions before you automate
Access granted person by person quietly drifts out of step, until nobody can say who sees the price list. Fixing it with roles, structure and quarterly review.
6 min read
Field teams
Photos and files from the job — findable a year later
A photo sent to a group chat stops existing once the thread scrolls. How to attach files to the job so they can still be found a year later, not just today.
6 min read
Remote work
Running projects without buying a project tool
A project is a view of data you already hold: tasks, dates, people, files, decisions. What has to be recorded, what is decoration, when a separate tool pays.
5 min read
AI and your data
When AI has too many tools — what a long list costs you
Every tool is a description the model reads on every single request. What breaks at several dozen of them, how to spot it, and three ways to shorten the list.
6 min read
AI agents
Which tasks to hand an agent first — a five-question test
The first task an agent gets decides whether anything survives. Five questions that pick it, examples that pass and fail, and the order worth working in.
6 min read
Field teams
Offline work in the field — a real requirement or a convenient excuse
Three levels of working without a network and what each one costs. How to check whether your crews really lose signal, before full offline delays the rollout.
6 min read
Remote work
Making decisions without meetings — when a thread beats a call
When a meeting is cheaper than a written thread, what a decision request has to contain, and why a decision with no recorded reasoning comes back later.
6 min read
Field teams
The service work order workflow — from first call to invoice
Six stages of a service work order and the three places information goes missing. What each stage must record so the next one does not start with a call.
6 min read
Remote work
How to cut down your tool stack without paralysing the company
An inventory, a criterion for dropping a tool and the right migration order. How to consolidate the tool stack in a company of 11–200 people, step by step.
6 min read
AI and your data
Where your company data goes when you send it to AI
What happens to a prompt sent to a model: who processes it, in which region, and how long they keep it. Plus the questions to put to a vendor in writing.
6 min read
AI agents
AI agent permissions — what it may do and how to limit it
An agent inherits some account's permissions, and the whole question is whose. How to split read from write, handle irreversible actions, and what to check.
6 min read
Field teams
What field technicians need in an app — four conditions that decide it
One free hand, a screen in direct sun, gloves and a four-year-old phone. What those four conditions mean for the interface of a field app, and how to test it.
5 min read
AI and your data
Preparing company data for AI — four things to fix before you connect a model
A model will not clean up your data, it will repeat the mess and make it sound authoritative. Four things to sort out first, and how to do it in stages.
5 min read
AI adoption
Why AI pilots stall — five reasons they never reach production
The pilot works and production never arrives. Five mechanisms that stall AI adoption at the demo stage, and what to do differently in a company of 11–200.
6 min read
AI agents
AI agent, automation and chatbot — three different things, one name
Automation follows a path someone wrote down, a chatbot answers, an agent picks its own steps. How they differ, which to choose, and what the wrong pick costs.
6 min read
AI adoption
Where to start with AI — how to pick the first process
The first process matters more than the tool. Four conditions a good candidate meets, three choices that reliably fail, and how to set success upfront.
6 min read
AI agents
Human approval for AI actions — what it looks like in practice
Human-in-the-loop sounds like jargon but describes a specific mechanism. What belongs on an approval screen, why bulk acceptance fails, and when to loosen it.
5 min read
AI adoption
What AI adoption actually costs — the bill, item by item
Licences are the smallest part of it. What else makes up the cost of adopting AI in a company of 11–200 people, and how to work out whether it pays.
4 min read
AI and your data
What is MCP — the protocol connecting AI to company data, without jargon
Model Context Protocol in practice: what it solves, why it became the standard in 2026, and what it changes for a company wanting to connect AI to its own data.
5 min read
Hypris
See what this looks like in practice
Hypris is a work platform with a built-in AI agent that acts only after you approve it. Your whole company, its data and its agents in one place — field crews included.
No strings attached. We show a working product, not a slide deck.