pulsierlabs

A portfolio of software products

We build what’s worth building.

An agent for events, a decision tool for product teams, a process platform for manufacturing and a bridge for device data — different problems we came across, each one solved with what we know and what we believe.

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The portfolio

Live, on-site, and open source

What we’ve built so far.

Each one started from a problem we noticed and felt we could solve well. Some are live today, and others are still in progress.

Events

At events, attendees ask the same practical questions all day, and there is rarely a quick place to get the answer. We saw that gap and built AskConcierge to fill it, drawing on our experience building AI products.

Live

AskConcierge

A concierge for event attendees

Attendees ask the same practical questions throughout an event. Connect AskConcierge to your programme and share a link: attendees get answers straight away, and you can see what they asked.

  • Answers from your own programme
  • Shows what people asked
  • Passes open questions to you
askconcierge.pulsierlabs.com →
AskConcierge on a phone: an attendee asks what is on in the morning and gets the day's schedule back, each session with its room, speaker and an add-to-calendar link.
The attendee’s view, and the organiser’s inbox of questions still to answer.

Product decisions

For the decisions that benefit from hearing more than one perspective before they are made — in product, engineering or marketing.

Live

Persona Council

Persona skill for product, engineering and marketing

Persona Council gives your agent a panel of distinct perspectives — an engineer, a customer and a marketer, for example. Each is asked separately, then they see each other’s arguments, and you get a summary of where they agree and where they don’t.

  • Perspectives asked separately
  • A second round without names
  • Can try the product itself
github.com/tabreu8/persona-council →
Heard separatelyEngineerCustomerMarketerArguments shared,names removedWhere they agree,and where they don’t

Machine and device data

Both work with the data that machines and devices already produce: one to understand how a plant really runs, the other to make that data easy for any software to use.

Loom

Process intelligence · runs on-site

Loom uses a plant’s machine signals, lot scans and orders to show how work flows: where it waits, where it loops back, and what changes between shifts. A daily summary covers what improved, what needs attention, and the current bottleneck.

  • Finds the bottleneck
  • Shows waiting and rework
  • Compares week on week
Ask us about Loom →
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A process map built from the plant’s own events: the route work took, where it waited, and where it looped back.
Loom’s Flow screen, on the demo plant it ships with.
Live

mqtt-get

REST and MCP for MQTT

A lightweight bridge that connects to your MQTT broker and keeps the latest value of every topic, so dashboards, spreadsheets, scripts and AI agents can read it over REST or MCP. It can also publish messages and forward them to webhooks.

  • Latest value over GET
  • Publish and webhooks
  • MCP server
github.com/tabreu8/mqtt-get →
Devices publish hereMQTT brokermqtt-getGETLatestvaluePOSTPublisha messageHOOKPushedto youMCPAgentsread & actApps, scripts and agents

How we build

The problem first. The tools after.

Every product we make starts from the same few beliefs and follows the same path, from a single question to something people use.

hello@pulsierlabs.com →

What we believe

The best ideas keep finding us.

The ideas we pursue are the ones we keep coming back to, after we have looked into them and understood what makes them hard. Giving an idea time is how we tell a passing thought from a problem worth solving.

Build fast. Learn fast. Improve fast.

We work in short cycles and small steps, and put a working version in people’s hands as early as we can. Seeing it used is how we find out what is worth building next.

The people using it show us the way.

We start with a clear point of view, and we stay open to changing it. Watching how people actually use a product often tells us more about what to do next than the plan we started with.

AI is a tool, and we choose when to use it.

It is a powerful tool, and it is still a tool. We use it where it clearly helps, and choose something simpler where that does the job better.

The path each product takes

  1. 01

    Start with one clear question

    We write down the one question the product should answer, and sketch it on a napkin. If it needs more than a sentence and a drawing, we keep working on it.

  2. 02

    Put something real in people’s hands

    We get a working version to people early, set up so we can see how it is actually used.

  3. 03

    Let feedback guide what’s next

    What people ask for, and what comes up more than once, shapes the next few weeks of work.

  4. 04

    Grow it, or learn from it

    Tools that prove useful grow into products of their own. The ones that don’t still teach us something we carry into the next.

Some of our products started as tools we built for our own work, and grew from there.