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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.
Decisions
openAdopt PostgreSQL as the primary DB for the payments service
Action items
openSet up the CI/CD pipeline for the staging environment
Risks
openAuth service has no redundancy — single point of failure before launch
Open questions
openWho owns onboarding email sequences after the product redesign?
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.
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
Backlog by type
open signals
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.
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.
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.
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.
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
P1Action 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
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
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
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.
Go deeper
Written for the specific version of this problem you probably have.
- A decision log for Slack, without anyone keeping oneWhat was decided, by whom, in which thread — assembled from the conversation you already had.
- Pulling action items out of SlackWho agreed to what, by when, lifted from how people actually phrase it — not from a form nobody fills in.
- What Slack AI cannot seeWhy a summary of one channel is not the same as knowing what is going wrong across a quarter.
- Decided in chat, never filed in JiraThe gap between what the team agreed and what the tracker knows — measured, with the items named.
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.