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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.
Recurring work patterns ranked by frequency × cross-team adoption × complexity fit. Each card surfaces one candidate. Click "Show spec" for input/output shape, risks, and example conversations.
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.
Your team’s AI tools — published in SightLift, and in use outside it.
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.
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. Top 10 by spend — search to reach anyone.
| User | Input tokens | Output tokens | Cost (work / non-work) | % of total | Aligned |
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Sales outcomes from your CRM, attributed to AI usage. Reps are ranked into usage quartiles (Q1 = heaviest AI use) and compared on closed-won, win rate, and deal-cycle time. This is a descriptive association, not a causal claim.
Each conversation is assigned exactly one topic via rules-based pattern matching on title + first message. Non-work is filtered out of all summary views by default.
Within each topic, a finer split into recurring patterns (e.g., marketing → linkedin-social, brand-voice, email-outreach). Click any sub-topic to see its conversations.
What the conversation actually produces (the artifact).
Weights agentic patterns (tool calls, tool variety, extended thinking, attachments). Captures how powerful was the workflow. Independent from effort below.
Weights human input (turns + chars + attachments). Captures how much the user put into it. Two conversations can have very different complexity but similar effort — or vice versa.
Used to estimate cost. Counted per conversation by content type — input tokens (human messages + tool results) vs. output tokens (assistant text + tool calls + thinking).
Published per-1M-token rates × the team's provider/model mix × tokens consumed. Input and output are priced separately because they differ by 3–5× per model.
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 renders an advisory assessment — it never approves; that happens in your budget tool, which is why you enter the “since” date yourself (SightLift can't see the approval).
Efficiency-based, not an ROI verdict: it says whether the spend looks like real work done efficiently — not what it produced (measuring outcomes needs a session→artifact link that doesn't exist yet). Thresholds are calibrated to your tenant's own distribution, not fixed numbers.
The share of a person's tokens spent on topics that match a company.md objective. A conversation on a specifically-named priority topic counts full; one covered only by an org-wide (scope: all) objective counts partial (0.4) — otherwise a single org-wide goal would make every conversation read 100% aligned. Non-work conversations are never aligned, even under an org-wide objective. The rest is orphan spend. Topics are classified from content, so this can't be gamed by self-labeling. No company.md → alignment is omitted. This is a view of where the money went, not a ranking input — it never moves a play up your action plan. The Cost tab's Aligned column and “Share going to priority work” KPI apply this same per-conversation crediting; they differ only in denominator (the Cost tab shows the share of work spend, the review the share of total window spend — personal use counts as orphan here, but doesn't dilute the Cost tab's work-spend share).
Three proxies for “done efficiently,” all relative to your tenant, not fixed cut-offs. Where there's enough history, each also shows the person's prior equal-length window (“was …”) — improvement is judged against themselves, not only the cohort. A thin prior window (fewer than 5 sessions) is never shown as a trend.
The share of a person's output tokens spent on the agentic machinery — tool calls, their arguments, and extended thinking — rather than plain answer text (agentic_out ÷ total_out). High means tool-heavy, autonomous work; low means mostly writing or answering. It's the raw input to the gate's spin signal, which fires only when a person's agentic share sits above your tenant's 75th percentile. (The Cost tab's “agentic share of spend” is the same idea measured over total tokens and cost-weighted.)
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 the gate can't see: whether quiet work succeeded or was abandoned — both make a topic go silent. Telling them apart needs a session→outcome link that doesn't exist yet; until then, “advancing toward a dead end” can read as a hard problem.
The Agent opportunities section at the bottom of the Action plan surfaces recurring work patterns that could be handed to a custom agent or skill. Candidates are found in two stages: a $0 heuristic clusters conversations by topic + sub-topic, then an LLM scores each cluster's fit for automation. Distinct from the Explore tab's Data Signals (management nudges) — this one answers "what could we build?"
shape_in) and produce (shape_out) — the rough I/O contract, shown under "Show spec".The Toolbox tab is every AI skill your team actually uses, in one place — and it deliberately shows two different kinds of thing side by side.
The Action plan is a synthesis layer over the Data Signals on the Explore tab: it bundles the many signals about the same person, topic, or group into a short, ranked list of concrete next steps — "coach this person", "de-risk this topic", "run an AI 101 for this group". Each action cites the signals and conversations behind it (click a card to drill in), names the score dimension it moves, and shows the points it would add — and the plan is ranked by those points alone, biggest first. Actions that name an individual are admin-only.
Every play carries one category (the colored chip), which sets the verb and the kind of follow-up doc you can generate:
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 estimated to take, multiplied by how often we actually observed it. The rate is one number an admin sets in Settings → Monthly AI budget: a blended, fully-loaded figure (salary plus overhead) for the people doing this work. 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 Explore tab surfaces these management signals — deterministic patterns detected across all conversations ($0, no LLM), ranked by severity. Each carries a lane (the colored chip below). A signal points at a topic, a person, or a pattern, and links to the exact conversations behind it (click any card to drill in). Signals that name an individual or touch sensitive content are tagged admin-only for future role-based visibility.
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 tier ribbon; the Weekly / Monthly / Quarterly toggle zooms the lookback (≈6 months / ≈18 months / ≈3 years). It's a directional management signal — read the tier and the direction, not the exact number. There is no individual score, and there never will be (see Privacy).
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 +3 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.
Banding is deliberate, 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.
Shown next to the SightLift logo in the header. Up to 80 characters.
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.
Click a role to change it. Changes apply immediately.
| User | Role | Sensitive data | Status | Last seen |
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Users are SightLift account holders — people who can sign in and view this dashboard. They're managed via Zitadel, 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.
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 Outcomes tab. 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.
Load your org chart in one place — a department and manager for each person, plus Legal / HR / Finance / Security / Leadership tags (someone can hold more than one). Department drives the AI-Use Score and work breakdowns by team; function tags let SightLift check whether sensitive work is handled in the right hands (reported at the team level only). Leave people unset 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 (Team tab → Weekly digest) and can also post to a Slack channel.
Connect Claude Code, Cowork, or any MCP-capable assistant to your team's live SightLift capabilities. You paste a connection token into your own tool — SightLift supplies the capabilities, it never runs your assistant.
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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