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.
Eight ways to read the same evidence
Signals are the raw material. The real value is what SignalOps does with a whole period of them. Point any of these eight analytical lenses at your last week, sprint or quarter — each answers a different leadership question, each cites the exact signals it is built on, and each runs on a schedule so you are told, not left to ask.
Signals trend
openedresolved
Cycle-time
p50 · last 8 weeks
Backlog by type
open signals
Org Health
A short executive read across everything that happened.
- What it answers
- What is the one thing that most deserves my attention right now, and what is quietly going wrong underneath?
- Why it matters
- The founder or lead who cannot hold five channels and three projects in their head gets the single most important thing surfaced first, not buried.
Delivery Flow
Where work is getting stuck, and who or what is the bottleneck.
- What it answers
- What is blocking us, where is work piling up, and how long are things actually taking?
- Why it matters
- With a tracker connected, this uses real cycle-time — not a feeling about which project is slow, but the evidence of it.
Risk Register
The real threats separated from routine standup noise.
- What it answers
- Which risks actually matter this period, and why — as opposed to everything anyone flagged?
- Why it matters
- A risk register nobody has to keep by hand. It is built from what was said, ranked by recurrence and severity, and never depends on someone remembering to log it.
Decisions & Alignment
What was decided, what is stuck, and whether the team is pulling one way.
- What it answers
- What did we decide, what is waiting on someone, what keeps getting re-opened, and are we scattering?
- Why it matters
- Decisions that re-open every few weeks are the most expensive kind. This shows them, with the thread each one came from.
Client & Stakeholder Health
Which external dependencies and client launches are blocked.
- What it answers
- Who outside the team are we waiting on, and which client is most at risk this period?
- Why it matters
- For studios and agencies, the launch that slips is usually blocked on someone outside the team. This finds those before the client does.
Early Warning
Leading indicators — what is heating up before it becomes a fire.
- What it answers
- What is escalating, recurring, or aging into a problem I have not been told about yet?
- Why it matters
- The point of leading indicators is time. This buys it, by naming the issue rising on 14 of the last 20 days while it is still small.
Quality & Rework
The recurrence radar — what keeps coming back after it was "handled".
- What it answers
- What are we silently redoing, and which problems did we think we fixed but did not?
- Why it matters
- Rework is invisible in a status update and obvious over a quarter of signals. This is the report a daily summary structurally cannot produce.
Momentum & What-Changed
This period versus last — better or worse, and specifically what moved.
- What it answers
- Compared to last time, what is new, what resolved, and what is still dragging?
- Why it matters
- The question a founder actually holds reading serially. It answers it with specifics, not a vibe.
Recurrence Radar (chronic issues)
A standalone view of issues that will not go away.
- What it answers
- What has been raised again and again — how many times, and across how many days?
- Why it matters
- The same risk raised on 14 of the last 20 days, with occurrence counts and provenance. No summary tool, in Slack or Teams or a tracker, can see this — because none of them holds the whole period the way SignalOps does.
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.
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 eight analytics reports (org health, delivery flow, risk register, decisions and alignment, client health, early warning, quality and rework, and momentum). 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.