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Work vs non-work conversations across the selected time window.
Plays that newly fired or escalated since your last upload.
Estimated monthly spend based on activity volume, split work vs non-work with a 3-month forecast.
One row per person — recent activity is the 30-day strip on each row. Click to expand into the full drilldown. Each colored segment is clickable to drill.
What conversations are producing — emails, decks, code, posts, summaries, etc. Click any bar to see the conversations.
Each card expands to show sub-topics with their complexity tier mix and top contributors. Click a sub-topic to see the 5 most complex example conversations. Load your org chart to also break the work down by department.
Skills that would hand specific people time back, based on work they already do a lot — ranked by total time saved. This is about giving time back to people doing good work, not flagging anyone.
Counterfactual re-pricing of routine (Simple/Medium) work onto the cheapest in-family model, over the selected window. Assumes complexity tier reflects required capability. Rows marked ~ are mix-weighted estimates from billing data — an org-level bound, not per-conversation attribution; ● rows drill to the exact conversations.
Manager next-steps for trimming spend — model downgrades, token-burn outliers, and concentrated work worth automating — synthesized from Data Signals and ranked by priority. Click a card to see how it was derived, then drill into the conversations.
Spend per provider over the selected window. Fidelity per provider is shown in the banner above.
Spend per model, stacked by complexity tier. Granularity depends on connected integrations.
Estimated monthly spend based on activity volume.
Where the spend is going — useful for "which work is worth automating?".
If Deep+Complex dominates spend, that's where a stronger model pays off.
What one deliverable costs each team in tokens — a deck, a memo, an analysis — against the org median for that output type, across your full history. A team far above the median is usually a template or prompt fix, not a people problem; teams doing measurably heavier work for an output are noted, never flagged.
| Output | Team | Tokens per artifact | Artifacts | vs org median |
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Tokens spent on tool inputs/outputs and thinking blocks (vs. plain text). High overhead is a clue your team is doing real agentic work — and a clue prompt caching would save money.
Estimated this month. Click a row for conversations, or “Budget review” to assess a cap request. Search to reach anyone.
| User | Input tokens | Output tokens | Cost (work / non-work) | % of total | Aligned |
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Where AI-assisted work goes across your accounts, and what it changes. Accounts are detected from conversations with no setup; uploading your account list or connecting a CRM firms up the matches. Anything comparing outcomes is a descriptive association, not a causal claim.
Want the long version? These guides go deeper than the definitions here.
Every conversation gets exactly one topic, decided by reading what it was actually about rather than by who filed it or what they called it — so nobody can change their numbers by renaming their work. Personal conversations are set aside from every summary by default; the note at the top of the page says how many there were.
A finer split within each topic. Unlike topics, these are not a fixed list — they're drawn from your own work, then near-duplicates are merged so the same thing under three names doesn't look like three things. That means this list is yours and will look nothing like another company's. Click any sub-topic to read its conversations.
What the conversation actually produced — the thing you'd point at afterwards. Each conversation gets one, chosen from the fixed list below, so "we made 40 decks this month" is a question with an answer.
How much the AI actually did — how many tools it reached for, whether it stopped to think things through, and whether it had files to work from. It deliberately ignores how much typing was involved: getting a hard job done in one well-aimed sentence is complex work, not simple work. That's effort, measured separately below.
How much the person put in — how many times they came back, how much they wrote and handed over, and how many files they brought. This is the other half of the picture: two conversations can take the same effort and be worlds apart in complexity, and the pair together is far more telling than either alone.
Tokens are the units AI providers bill in — roughly, pieces of words. Every conversation is counted piece by piece with a real tokenizer over the actual text, not a "characters ÷ 4" rule of thumb, and split by who produced it, because the two directions are priced very differently.
Published per-million-token rates × the tokens your team used. Reading and writing are priced separately because they differ by three to five times.
The three levels say how well we know which model did the work — not how well we know the price. Every dollar we show, at every level, is a published list price applied to your token counts; none of them is read off your provider invoice, which is why each carries a ~ and an est. The Cost tab grades the window you have selected against these levels, and See by source there labels every provider with one of them — per provider, never for the company as a whole.
Levels are per provider, not per company — Claude can sit at Actual model while a second provider is still Estimated, and the tab says so. Connecting a source only ever raises what a provider is capable of; nothing you add takes a level away. Setting these up: the data sources guide ↗.
A provider can be part-way, and the label says so rather than rounding up. A level describes the dollars, and a conversation only reaches its provider's level if we can price it that way — Actual model needs that conversation's own model to be one we hold a rate for, Model mix needs its day to be in your provider's usage reporting. Anything that misses falls to the provider's fallback rate. So a provider whose conversations only partly qualify reads ◐ Actual model for 2 of 4 conversations — the half-filled mark, because part of it is, and the count and dollars behind it in the line beside it. If none of them qualify, it reads ○ Estimated and the line says which connection it has and why nothing landed: the connection is still there, but no dollar under it was priced any better than Estimated, and showing you the higher badge would be claiming an accuracy these numbers do not have.
Published list prices, kept current with each provider's pricing page. This table is today's — a past conversation keeps the list price we have on record for its own date, so a rate that changed since will not match the row here. If your company has negotiated rates these will read high; the comparisons between topics, teams and months hold either way.
These dollars are measured AI usage, priced at list. They are a different thing entirely from the labor dollars on the Action plan and Toolbox, which value people's time at the hourly rate you set.
A focused, per-person read for one decision: should this employee get more tokens? Opened from the Cost tab's by-user table when someone hits a cap. SightLift measures the spend and offers an advisory read — it never approves anything; that happens in your own budget tool, which is why you type the “since” date in yourself (we can't see the approval).
It reads efficiency, not return on investment: whether the spend looks like real work done efficiently — not what that work produced, which we can't yet trace from a session to the thing it shipped. Every threshold comes from your own team's numbers, never a fixed industry figure.
The share of a person's tokens that went to work matching one of your company objectives (you write these in Settings → Company profile). A conversation on a topic an objective names specifically gets full credit; one that only falls under a goal you set for the whole company gets partial credit — otherwise one company-wide goal would make everything read as perfectly aligned. Personal conversations never count as aligned, whatever the goal says. Whatever's left over is unclaimed spend. Topics come from what the conversation was actually about, so nobody can improve their own number by labeling their work differently. No company profile, no alignment figure. This shows where the money went; it never pushes anything up or down your action plan. The Cost tab's Aligned column and its “Share going to priority work” tile credit each conversation exactly the same way — they only differ in what they divide by (the Cost tab compares against work spend, this review against every dollar in the window, so personal use lowers the number here but not there).
Three stand-ins for “done efficiently,” all measured against your own team rather than a fixed cut-off. Where there's enough history each one also shows the same person's previous stretch of equal length (“was …”), so improvement is judged against themselves and not only against colleagues. Fewer than five earlier sessions is never shown as a trend.
Of everything the AI wrote for this person, the share that was machinery — tool calls and extended thinking — rather than plain answer text. High means tool-heavy, autonomous work; low means mostly writing and answering. Neither is good or bad on its own; it only becomes a warning sign when someone's share sits in the top quarter for your team, which is what feeds the “spinning” read below. (The Cost tab shows the same idea across all tokens, weighted by dollars.)
Everyone reviewed is already a heavy spender, so raw size separates no one. The read looks at the shape of the spend on two axes — signs of progress vs signs of spinning — and routes into three outcomes.
What this can't see: whether quiet work finished or was given up on — both look the same from here. Telling them apart needs a link from a session to what it produced, which doesn't exist yet. Until it does, steady progress toward a dead end reads as a hard problem.
Work your team does over and over that a custom agent or skill could take on. We find them in two passes: first by grouping conversations that cover the same ground, then by having a model read each group and judge how well it would suit automation. Every candidate lands in the Detected column of the Capabilities tab with its full write-up; the strongest also become Automation plays on your Action plan. Both come from the same detection. This answers "what could we build?" — the Data Signals under Explore → Signals answer "what should we do about the people?"
The Toolbox tab is every AI skill your team actually uses, in one place — and it deliberately shows three different kinds of thing side by side. The full walkthrough is How your Toolbox works ↗.
A capability is one repeatable piece of work, packaged so your team can reuse it instead of redoing it. The Capabilities tab is where they're proposed, proven and published; the Toolbox is where you see how the published ones are doing. You don't build or configure any of this yourself — SightLift proposes them from work your team already repeats and asks you to approve the ones worth keeping. The full walkthrough is How capabilities work ↗.
Nothing is ever proposed off one person's habit: the same work has to show up several times, across more than one person, before it becomes a candidate.
Capabilities move left to right, becoming more trustworthy at each step. Nothing publishes itself — a person signs off before anything is served. A column only appears when something is sitting in it, so you won't see all of these at once.
The proof is the whole point — it's what lets you trust a capability without reading its internals. We say plainly which kind you're getting, and never dress one up as another.
Testing runs in a sealed sandbox: no data leaves it, and nothing runs against anything live. Every proof keeps a de-identified copy of the examples it passed, so it stays valid even if the original conversations are later deleted — and the person who approved it and the date are always attached.
These are the small labels that appear on individual cards. Each one is meant to read on its own — this is the reference, not the explanation you need to make sense of them.
The Action plan takes the many Data Signals about the same person, topic or group and bundles them into a short, ranked list of things to actually do — "coach this person", "spread this topic beyond one owner", "run an AI 101 for this group". Every action shows the signals and conversations it came from (click a card to read them), says which part of the AI-Use Score it would move, and how many points it would add. The list is ordered by those points alone, biggest first. Actions that name an individual are shown only to admins.
Each one carries a single category — the colored chip — which decides what kind of thing it's asking of you and what document you can generate from it:
Some automation plays carry a dollar figure ("~$4,200/mo of manual time", "~$800 of avoidable rework"), as do the savings on a published capability. These are labor dollars, and the arithmetic is deliberately simple: estimated time × your loaded hourly rate. The time comes from your own data: how long this work is reckoned to take, multiplied by how often we actually saw it happen. The rate is a single number an admin sets in Settings → Monthly AI budget — an average hourly cost for the people doing this work, salary plus everything on top of it. Until someone sets it, a default is used, and every figure that depends on it says which rate it used when you hover.
This is money your team could redirect, not money that appears in a bank account — it values hours at what you told us an hour costs. It is also entirely separate from the dollars on the Cost tab and in an action's score impact, which are measured AI spend and carry no wage assumption at all.
The Revenue tab asks where AI-assisted work goes across your customers, and what it changes. Accounts are picked up from conversations with no setup at all; uploading your account list or connecting your CRM firms up the matches and unlocks the deal views. It appears for admins and for people an admin has given revenue access (Settings → Users) — and only once there's account or deal data to show, so a new grant on an empty tenant isn't broken, there's just nothing to put in it yet.
Everything here that compares outcomes is a description of what went together, not proof that one caused the other. Rows are your customers — this tab never ranks, totals or compares your own staff. "Worked by" is a fact about the account, the same way your CRM shows who touched it.
Three ways an account gets identified, and the difference is always visible on the row — a guess never gets styled like a certainty.
Under Explore → Signals you'll find these — patterns found by counting, the same way every time, across every conversation, and sorted by how much they matter. Each one is grouped into a family (the colored chips below). A signal points at a topic, a person or a habit, and always links to the exact conversations behind it; click any card to read them. Signals that name someone, or that touch sensitive work, are shown only to admins.
These are the raw findings. The Action plan is where many signals about the same person or topic get bundled into one thing to actually do.
The signals below join AI usage with sales outcomes from your CRM (HubSpot) — they appear only when a CRM is connected and the data is matched. All are descriptive associations, not causal claims, and are admin/RevOps-only.
A number from 0 to 100 for how well your team uses AI — not just how much. It's the average of the five dimensions below (each also scored 0–100), using the weights you set. Every point traces back to something we counted in your own data — conversations, seats, dollars, shared-tool use — and the score page always shows the handful of numbers behind each dimension. Shown per team and for the whole org as a ribbon; the Weekly / Monthly / Quarterly toggle zooms the lookback (≈6 months / ≈18 months / ≈3 years). It's a steer, not a measurement instrument — read which way it's moving, not a 1-point difference between two teams. There is no individual score, and there never will be (see Privacy). The full walkthrough is How your AI-Use Score works ↗.
A dimension we can't measure yet is skipped, never guessed — the average is taken over the rest, so an unactivated Reuse or Governance dimension can't drag the number down.
The score uses your weights — equal by default (each dimension counts the same). Set them in Settings → Company profile → Score weights to match the behaviors you want to drive, and change the balance as your priorities evolve. Weights are a strategy lever, not a way to inflate the number: raising a weight makes that dimension matter more and makes its gaps cost more.
Every action on your Action plan names the dimension it moves and shows the points it could add — computed from your own numbers, with the math on the card (e.g. "re-activating 4 quiet seats raises Activation from 67% to 100% — about +4 points"). The plan is ranked by those points, biggest first. Two kinds of projection, always labeled:
Some cards also show a dollar figure (like the monthly cost of manual work an automation would remove). Points rank the plan; dollars size the prize.
We report where the score is and what it did, and we don't grade it. A composite out of 100 is a measurement; a grading word laid over it is a verdict we'd be handing you about your own team — so the number stays plain and the change sits beside it. Movement is deliberately banded, so a 1-point wiggle can't read as a trend:
Scores exist at the team and company level only. There is no individual score, and there never will be — managers drill into examples of work patterns, never into a ranked list of people. CRM outcomes, where connected, only help validate the weights — they are never an input to the score.
Names this workspace at the foot of the left rail. Up to 80 characters.
Replaces the lettered square beside that name. A square mark reads best, because a wide logo is cropped to its middle to fit. PNG, JPEG or WebP, up to 256 KB.
Controls who can publish a capability a teammate added to the team.
Sets the spend target the Cost tab projects against for the current month.
We price your usage at each vendor's published rates. If you have an agreement, tell us what you actually pay and the Cost tab re-prices.
Click a role to change it. Changes apply immediately.
| User | Scope & teams | Role | Sensitive data | Modules | Status | Last seen |
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Users are SightLift account holders — people who can sign in and view this dashboard. They're managed in Settings, separate from the people whose conversations appear in the data above.
To invite someone, click Invite user above. They'll get a sign-in email; nothing happens automatically.
Prove you own a domain before connecting it to your identity provider.
Let your team sign in with Okta, Entra, Google, or any SAML provider.
Every access to your organization's data that SightLift recorded — your own team's, and SightLift's own staff. Reading this page is itself an access, so each read you make is recorded here too.
Each upload becomes a dataset. New uploads add to your existing data — they don't replace it. Delete a dataset to remove every conversation it brought in; the dashboard refreshes automatically.
Upload an export from the integrations below to create your first dataset.
The dashboard couldn't reach the imports API. Check your connection or look at the api logs.
| Source / Filename | Data as of | Conversations | Status |
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Connect upstream AI providers to extract your team's conversation history — by API, a one-time export upload, or (for Anthropic) live telemetry, the only way to capture Cowork. Pulled data feeds every panel in this dashboard.
Connect your CRM to attribute sales outcomes to AI usage. SightLift pulls closed-won, win rate, deal-cycle time, and activity counts per owner, then matches owners to people by email — powering the rep lens of Revenue → Impact. Outcome data is admin/RevOps-only and never leaves your tenant.
Your AI-Use Score combines five dimensions of how your team uses AI. Weight the ones that matter most to your team, and change the balance as your priorities evolve. This is the one setting that shapes the number — it takes effect on your next data refresh.
A short company.md describing what the business is optimizing for. Objectives (with their topics and levers) label what work is for: each action shows the objective it advances, and the Cost tab credits spend against them — they never change what ranks first. Leave unset for no attribution.
A short acceptable-AI-use policy — which tools are approved, that sensitive work stays on company accounts, and what must not be pasted into a chat. SightLift scores governance as compliance against exactly what you declare here. Don't have one? Start from the template. Leave unset and governance simply isn't scored.
The customers and prospects your team's AI work is about. Detection runs by default — recurring company names and web/email domains in conversations become account tags, caveated until you confirm them. Upload your list (or connect a CRM) to make the matches firm, unlock coverage, and see which accounts are getting no AI-assisted work at all. Nothing on this page is ever written back to your CRM.
Who is in which department, and who they report to. Departments are how the AI-Use Score and every team view are grouped, so a team of fourteen counts as fourteen even where only nine have used AI. Load the whole chart from a CSV file, then correct anyone it got wrong — your corrections are kept the next time you load a file. This is also where you set each person's Function Sensitivity: the kinds of sensitive work they're cleared to handle (Legal, Finance, Personnel, Security, Strategy — someone can be cleared for more than one — or Not Sensitive), which lets SightLift check whether sensitive work is handled by cleared people (reported at the team level only). Leave a person with no department and SightLift falls back to grouping by their work topic.
Override which LLM provider handles classification, opportunity evaluation, and Explore queries for your tenant. Leave unconfigured to use the SightLift platform default.
Continuous monitoring: scheduled syncs and the weekly digest. Digests email subscribers (People tab → Weekly digest) and can also post to a Slack channel.
Sent Friday mornings. Everyone can turn their own off.
Connect the agent you already work in: ask about your own SightLift numbers, and reach the skills and automations your team has proven — without opening the dashboard. You sign in as yourself, so you see exactly what you can see here — nothing more.
One block in your Claude managed settings puts your team's proven skills in front of everyone who uses AI here — including the people who never sign in to SightLift and never hold a token of their own. You issue one shared token, paste it in once, and leave it running.
Decides whether an agent connected to SightLift can read this organization's configuration — and, separately, whether it can change it. Both start off. Turning either off takes effect the next time an agent connects.
Claude Code session files don’t include an account. Choose the team member to map this work to so it lands on the right person.
Each incremental sync run that brought in conversations. To remove this data, delete the whole feed — the sync then re-pulls from scratch.
| Synced | Conversations | Status |
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What moved this score, and when — formula changes we shipped, and weight changes your admin made. History is recomputed on the current formula, so past months stay comparable.
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| Title | Person | Topic | Output | Complexity | Effort | Msgs | Tools | Score | Cost | Date |
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