Automation & AI work companion
Research & monitoring agent
Some information you'd need to check regularly: what competitors change, what reviews say, which tenders appear, which funding runs out. You'd need to. In practice no one looks – until someone else knew first.
Research & monitoring agent
01Intro
A monitoring agent takes over the looking: it checks defined sources on a fixed rhythm, compares with the last state and distils what has changed into a short report – once a week in your inbox instead of twenty open tabs. What gets watched is up to you: competitor websites, review portals, industry sources, tender and procurement platforms. Meant as a project and contract scout, it becomes a quiet colleague who says on Monday: these two opportunities are new, you should take a look.
Straight up: this isn't perfect market observation – what sources don't give up (legally or technically) the agent can't see either, and source rights are checked before setup. The results need human judgement: the agent gathers and distils, the assessing is yours. That's why this building block is usually the deepening after the first projects, not the entry point.
02Demo
The order scout of a fictional carpentry firm watches tender portals, two competitors and its own reviews. Press play – a week in half a minute.
2 new opportunities: tender for the sports-hall roof truss (deadline 28/07) · wave of inquiries after the storm in the district – check capacity.
Competition: Holzbau Maier is now actively advertising “extra storey in 4 weeks” – overlaps with your core offering.
Reputation: new 2-star review (missed deadline) – a draft reply is attached. The week's other 4 reviews: positive.
No action needed: timber-construction grant programme unchanged · material prices stable · website check of competitors with nothing else notable.
23 observed events became 4 lines – two of them potentially worth money. The judging stays with the human: only the business knows whether the sports-hall tender is worth it.
The workflow behind it: a schedule wakes the agent, which reads the sources, compares them with the last state and sorts by relevance. Only what matters ends up condensed in the report – the rest goes quietly to the archive. You do the judging at the end.
The rhythm comes from the schedule (daily, weekly), not from your attention – that's exactly why it works.
03How it works
- 01
Sharpen the question
What do you really want to know – and what would you do with the answer? "Everything about the market" isn't a question. "Which tenders over €50k appear in our area" is.
- 02
Set and check the sources
Which websites, portals and feeds give up the answer – and may they be read automatically? What isn't cleanly accessible flies out or is fed in manually.
- 03
Set rhythm and format
Daily, weekly, monthly – and where to: email, Slack, Notion? The best report is the one you actually read. Short beats complete.
- 04
Set up and calibrate the agent
The first reports are too loud or too quiet – that's normal. Relevance filters and thresholds are readjusted until the signal-to-noise ratio is right.
- 05
Build it into everyday work
The report gets a fixed place: the Monday round, weekly planning, the acquisition block. A monitoring no one acts on gets switched off – that's a result too.
04Stages
The value shows before any infrastructure – Tier 1 is deliberately a single move.
Entry: the report at the push of a button
A set-up research workflow you trigger manually – say on Fridays with a click. Shows the value before any tech is built.
Extended: the scheduled agent
Fixed sources, fixed rhythm, an automatic email or Slack report with a comparison to the last state.
Full build: history and alerts
A dashboard with history, trends over months, instant alerts on defined events – when the monitoring carries everyday work.
05Technical implementation
- Source access to suit the situation: RSS/feeds and APIs first, website crawling where allowed, manual uploads for the rest.
- A scheduler triggers the runs: cron, n8n, Make or a small worker of your own.
- The language model distils: what's new, what has changed, what's relevant – with a comparison to the last state instead of a flood of raw data.
- Output to where you read: an email report, Slack/Teams, Notion/Obsidian or a plain dashboard.
- A small data store remembers history, sources and reports – trends grow from it later.
- Hosting: a worker on Vercel/Render/Hetzner or an automation platform – deliberately lightweight.
for context · This shows how something like this usually gets built – not how it has to be built for you. What actually fits is worked out in the project: around your existing setup, your team, and what stays easy to maintain.