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Team operations intelligence

You decided it in chat. Nobody wrote it down.

SignalOps reads your team's conversation and your work trackers, joins them into one evidence base, and pulls out the decisions, action items, risks and open questions — then tells you what is actually going on. Automatically. No /decide, no tagging, no notetaker in your calls.

Reads Slack, Microsoft Teams, Discord, Linear, Jira, Asana and ClickUp.

Signals · Engineering · this week
All typesDecisionsAction itemsRisksOpen questions

Decisions

open

Adopt PostgreSQL as the primary DB for the payments service

#eng-backend · Slack

Action items

open

Set up the CI/CD pipeline for the staging environment

#devops · SlackOwner: MariaDue: Fri

Risks

open

Auth service has no redundancy — single point of failure before launch

#incidents · Slackhigh severity

Open questions

open

Who owns onboarding email sequences after the product redesign?

#product · Slack

Slack is where context goes to die

A decision gets made in four messages and is buried under the next thousand. The same question resurfaces every few weeks. A risk is raised once, in passing, and nobody sees it again until it is an incident. Nobody remembers what was agreed last sprint.

Every fix for this has the same flaw: it needs someone to remember. Type /decide. Right-click and track. React with an emoji. That works right up until the week you are busy — which is exactly the week the decisions get lost.

One evidence base, from two kinds of source

This is what makes SignalOps different. The AI you already pay for is stuck in one room: Slack AI only sees Slack, your tracker only sees tickets. SignalOps reads both your conversation and your work tracker, and — crucially — joins them. The decision argued out in a Slack thread and the Linear issue it concerns become one connected story, not two disconnected fragments.

CONVERSATIONSlackMicrosoft TeamsDiscordWORK TRACKERSLinear · JiraAsana · ClickUpSignalOpsdedup · link · correlateDecisionsAction itemsRisksOpen questions

Joined, not just collected

Deduplicated across sources

The same issue raised in a Slack thread and filed in Jira collapses into one signal — you see the problem once, with both sources attached, not twice.

Message linked to ticket

SignalOps knows that this conversation is about that Linear issue, and links them deterministically. The context and the work item travel together.

Correlated into groups

Related signals across channels and projects are correlated and grouped, so a problem that shows up in three places reads as one thing, not three.

Real delivery metrics

Connect a tracker and the delivery lens uses actual cycle-time — how long work really took — instead of a guess.

Four things get pulled out of every conversation

SignalOps reads what your team already wrote and turns loose discussion into structured, typed records you can act on. No tagging, no slash commands, no bot in your calls.

Decisions

What the team actually settled — with a link straight back to the message where it happened. The decision that lived in four messages and then disappeared under the next thousand is now a record you can find in six months.

Action items

Who agreed to do what, by when. Owner hints and due dates are lifted from how people really phrased it ("I'll take this by Friday"), not from a form nobody fills in.

Risks

The concern someone raised in passing and everyone moved past. SignalOps keeps it, so a risk mentioned once in a thread does not have to become an incident before anyone looks at it again.

Open questions

Things asked and never answered — the cheapest problems to fix while they are still just questions. Unanswered blockers are surfaced instead of quietly aging.

Four reports, and each one counts something different

Signals are the raw material. The value is what a whole period of them is read for. These four reports are not four summaries of one analysis — each one counts a different thing: the company, the counterparty, the work item, the threat. That is what makes them four documents instead of the same document with four titles, and each cites the exact signals it is built on.

Signals trend

openedresolved

Cycle-time

p50 · last 8 weeks

6.2days1.4

Backlog by type

open signals

Decisions12
Action items15
Risks8
Open questions6
1

Org Health

One short read on the company, ending in a verdict.

What it answers
What is the state of things, what is the single thing to do first — and what looks alarming but should be left alone?
Why it matters
It is the only report that tells you what NOT to spend attention on this period. A list of everything wrong is easy to produce and impossible to act on; naming the one thing that matters, and the things that do not, is the work.
2

Client & Stakeholder Health

Your outside relationships, grouped by the party rather than by the task.

What it answers
Which client relationships are at risk, and who is waiting on whom?
Why it matters
It labels every stalled item in one of two ways: they owe us, or we owe them. Those are opposite problems with opposite fixes, and a report that mixes them sends you to chase a client for something your own team has not finished.
3

Delivery Flow

Where work sits, how long it has been there, and who is holding it.

What it answers
Is our delivery actually working, and what is the one constraint to fix?
Why it matters
It names exactly ONE bottleneck and says why it and not the runner-up. Six bottlenecks is zero bottlenecks — the reader cannot choose, so they choose nothing. With a tracker connected this runs on real cycle time, not on a feeling about which project is slow.
4

Risk Register

What is open right now, plus what changed this period.

What it answers
What could still go wrong, who owns it, and what has nobody touched in weeks?
Why it matters
A register is a standing artifact, not a weekly recount — a risk opened months ago is still open today. It flags the two ways a register rots: entries with no owner, and entries nobody has looked at. Both are counted, not guessed at.

The problem map: what is causing what

Signals and reports tell you what is going on. The map answers the next question — which of these is causing the others. It reads the relationships the analysis found between signals and lays them out left to right: the root you can act on, the steps in between, and what it ends up costing you.

Root cause — act here
What it set off
What it costs you

Signals with no causal link are kept in their own tray, not forced into a chain.

Fix this → this many go away

The top root causes, each with the number of downstream signals that hang off it. Only signals with no cause of their own are listed, because something with a cause of its own is not where you intervene. It is the difference between a list of forty complaints and the three you should spend Monday on.

Chains you can read

Each chain is a row of columns — root, step, what it leads to — with the real signal in every card and the message it came from behind it. Click any card for what happened, what it caused, and what to do, with the model's one-line reason for every link.

A timeline, when it matters

The same problems on a date axis, so you can see the one that has been sitting there since May next to the one that appeared on Tuesday. Dependency arrows stay hidden until you hover a card, so the picture is readable before it is detailed.

What it does not do: invent causality. An arrow is drawn only where the analysis found a directional relationship — causes, blocks, follows up, part of — and the weaker "these two are related" links are shown as plain tethers, never arrows. In a typical run that means a handful of short chains, plus a tray of signals with no causal link at all, shown openly rather than hidden. A map that connected everything would be a map that means nothing.

Every recommendation shows its work

SignalOps does not just say "there's a problem." Each recommendation is structured and evidence-bound — it is refused if it cannot point at the real signals underneath it. That means you can trust it, and act on it, without re-reading the whole channel yourself.

Recurring: staging deploys fail on the migration step

P1
Confidence72%
ImpactDelivery· 5 signals · raised on 6 of the last 14 days

Action plan

EngineeringRight-size the staging DB instance for the index build

OpsAdd a scheduled infra-parity check between staging and prod

Expected outcome: staging deploy failures drop to zero within two sprints.

Problem
What is wrong, in plain language.
Evidence
The exact signals and metrics it rests on — links back to the real messages, not a summary you have to take on faith.
Impact area
Delivery, quality, security, product, communication or operations — so you know whose problem it is.
Priority
P0 / P1 / P2, calibrated against how much evidence there actually is, not inflated to look urgent.
Confidence
Capped by how much data supported it. Sparse week, lower confidence — stated honestly, never dressed up.
Action plan
Concrete steps, each with a role (PM, Engineering, QA, Security, Ops, Leadership) and an effort estimate.
Expected outcome
What should change if you act — and how you would validate that it did.

How it works

  1. 1

    Connect your sources

    Slack, Microsoft Teams or Discord for the conversation; Linear, Jira, Asana or ClickUp for the work itself. Pick the channels and projects worth watching. Two minutes.

  2. 2

    It runs on your schedule

    Daily, weekly, or whenever you ask. Nobody writes a status update. Nobody tags a message. The analysis simply runs over what your team already wrote.

  3. 3

    You review an inbox, not a firehose

    Signals land in one place, filterable by type, owner, status or channel. Pick a lens, read the report, act on what is yours.

What SignalOps is not

Not a meeting notetaker

No bot joins your calls. It reads what your team typed — because the decisions that matter were written, not spoken.

Not another place to write updates

It asks nobody for a status report. It reads the work that already happened.

Not a search box

You do not have to know the question in advance. It brings you the answer on a schedule.

Questions teams ask

Does SignalOps join my meetings or record calls?

No. It never joins a call and records nothing. It reads the written conversation your team already has — in Slack, Microsoft Teams or Discord — plus your task trackers. The decisions that matter were typed, not spoken.

How is this different from Slack AI?

Slack AI can only see what is inside Slack. If the call was made in a Teams channel, argued out in Discord, or half-lives in a Linear comment, it is blind to it. SignalOps reads chat AND your trackers, joins them into one evidence base, and analyses a whole period — not just summarises a window.

Do we have to tag or log anything?

No. There is no slash command, no emoji, no manual capture step. That is the point — every tool that relies on someone remembering to log a decision loses the ones logged during a busy week. SignalOps reads what was already written.

Which tools does it connect to?

Today: Slack, Microsoft Teams and Discord for conversation; Linear, Jira, Asana and ClickUp for work. You can connect one source or several — the more it reads, the more it can join together.

What does it actually produce?

Typed signals — decisions, action items with owners and due dates, risks and open questions — in a filterable inbox, plus four analytics reports: org health, client health, delivery flow and risk register. Each reads the same signals through a different lens, and every recommendation cites the real signals it is built on.

Is our data used to train a model?

SignalOps analyses your data to produce your reports and stores the structured signals it derives. You choose the language model it runs on. It is an analysis tool, not a training pipeline.

The decisions already happened. Start keeping them.

Connect one workspace and run the first analysis. You will see what your team decided last week, what is at risk, and what nobody wrote down.