Prism by Crossdeck

App Business Intelligence

Category: Data & Analytics
Primary Subcategory: Self-Serve BI & Conversational Analytics

Integration details

Description

Prism reads your live Crossdeck workspace — revenue, entitlements, errors, analytics, read-cost and customer identity — joined BY IDENTITY, so it answers cross-layer questions no single-layer tool can: which paying customers an error hit and what they pay, who costs the most in database reads, and your lifetime-value leaderboard by name. Secure and read-only: OAuth 2.0 sign-in (no key to paste), no write path, and every number is self-describing (it reports 'not instrumented' rather than a fake zero for an un-wired surface).

Integration type: Plugin
Verification status: Not applicable
Platform: ChatGPT
Category: Data & Analytics
Primary Subcategory: Self-Serve BI & Conversational Analytics
Secondary Subcategories: None listed
Brand: Crossdeck
Access: Account required
First tracked: 2026-07-11
Tool count: 15
Geography: US

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Competing in ChatGPT Self-Serve BI & Conversational Analytics

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Tools

15 tools agents can invoke

Render an interactive line chart of unique visitors and page views over time for a host you own. Use when asked to chart, graph, draw, or visualize growth or traffic for a subdomain. The host must be a verified origin of your project.
draw_user_growth
Get an app's revenue AVERAGES from maintained ledgers (point-reads, no scan): ARPU (avg MRR per paying customer) and average lifetime value per paying customer. Lifetime value = combinedLifetimeValueCents (active-subscription annualized run-rate + banked one-offs) — RECONCILES to the dashboard's per-customer LTV, and is NOT recognisedCashToDateCents (actual cash banked, returned separately). Answers 'average lifetime value per user' / 'what's my ARPU'. Averages ACROSS THE PAYING BASE — NOT one customer's pay (use get_customer for one). 'user'=paying customer; a per-all-users average is not_instrumented. not_instrumented when no rail connected — not $0.
get_revenue_averages
Cross-match one customer across every layer Crossdeck joins by identity. Returns what they pay (monthly cents), their active entitlement count, and their database read-cost — in one view. Use for 'how much does this user pay and what do they cost us?'. Returns a no-match result (not an error) if no customer resolves.
get_customer
Get one error's blast radius joined to identity. Returns distinct users affected, how many of those are PAYING customers, and the error's type, status, occurrence count, and first/last-seen — counts only. Use to size an error's impact. For the NAMED paying users behind the counts, call get_error_affected_users.
get_error_impact
Get headless web analytics for one host/subdomain the app owns (e.g. a tenant's subdomain). Returns page views and unique visitors with totals, from the maintained rollup ledger. The host MUST be a verified origin of the project, or the request is rejected (403). Use for per-tenant traffic questions.
get_host_analytics
Get an app's database read-cost, split into per-user reads vs un-attributed overhead, with a breakdown by operation. Per-user attribution works because Crossdeck joins read-cost to the SDK's identity. Returns the per-user-vs-overhead totals and the by-operation breakdown. Use for 'what's driving our database reads' or 'which operation costs the most'.
get_read_cost
Get an app's revenue from its maintained ledgers (point-reads, never a scan). Returns TWO distinct revenue truths, each self-describing: current.mrrCents = recurring SUBSCRIPTION MRR (Stripe/Apple/Google), plus current.recognisedCashThisMonthCents = ALL money collected this calendar month INCLUDING one-off / consumable / cash charges (with a recurring-vs-oneOff split). This matters: a one-off or consumable business has $0 recurring MRR but real monthly revenue — read recognisedCashThisMonthCents and lead with it, NEVER report '$0 revenue' when it is positive. Also returns the paying-customer count and per-rail split. With granularity='day' adds a daily MRR series (`data.series`) for 'is my MRR up/down over time' / trend questions. A null field is a blind spot for THAT metric (read its semantics), not $0.
get_revenue
Answer 'what's my biggest signup source?'. Returns new customers grouped by their TRUE first-touch source — the referrer/utm on their earliest anonymous visit (e.g. chatgpt.com, google), carried forward to the customer at signup, NOT the signup-moment referrer. Counts only, highest-first. Forward-only: coverage.state='not_instrumented' means no signups attributed since first-touch tracking shipped, NOT zero signups historically.
get_acquisition
Get an app's customer census: the PAYING-customer count (measured from the maintained revenue ledger), plus total customers / active people where instrumented. This is the canonical 'how many customers/users does this app have?' answer — never estimate it from other tools' counts. Fields that aren't instrumented come back not_instrumented with an explanation (a blind spot), never a misleading 0 — check data.coverage per field.
get_customers
Get a coverage map of the whole workspace: for each app, which surfaces are instrumented (revenue, read-cost, analytics, customer census) vs a blind spot. Load this FIRST in a portfolio conversation — every later number is then interpreted against a known map, so a not-instrumented surface is never mistaken for a zero. Read-only; takes no arguments.
get_portfolio
Get the top pages for one host the app owns, ranked highest-first. Returns rows of { page, views } from the rollup ledger. The host must belong to the selected app. Use for 'most-viewed pages on this subdomain'.
get_host_top_pages
For one error, get the PAYING customers it actually hit — the row-level moat answer: which paying customer broke, and how much are they worth. Returns a list sorted by revenue at risk, each row with the customer's HUMAN identity (email/name, resolved best-first), their minted monthly revenue (USD cents, reconciles to Revenue — never re-summed from subscriptions), and when they last hit it. The owner's OWN customers, surfaced by name/email. Use when you need the actual customers, not just counts.
get_error_affected_users
List every app (project) in your Crossdeck portfolio that this connector can read — the same set your Pulse dashboard shows. Returns an array of { id, name } per app. Call this FIRST: every other tool needs a project id. Read-only; takes no arguments.
list_projects
Render ONE customer's cross-layer dashboard — what they pay, their active entitlements, and their database read-cost, joined by identity. Requires `project` and `customerId` (any single id you have for the customer — a Crossdeck customer id, your own user id, an anonymous id, or a rail id; Crossdeck resolves it through the identity graph). This is the moat, rendered.
open_moat_dashboard
Pivot the whole customer book by an axis Crossdeck owns and rank it — the leaderboard. by='read_cost' (metered reads), by='mrr' (monthly revenue), by='ltv' (lifetime value — net recognised cash actually paid, succeeded charges minus refunds; the SAME charge ledger the dashboard shows as 'Lifetime'), by='total_spend' (the SAME banked-cash figure as ltv — recognised cash to date, succeeded charges net of refunds), by='errors' (how many DISTINCT issues the customer has hit — breadth, not occurrences), by='activity' (lifetime event volume — total engagement; use 'recency' for 'hot right now'), by='tenure' (when they became a customer), by='recency' (when last active — use order='asc' for the CHURN-RISK queue of dormant customers), by='cost_ratio' (reads ÷ revenue: a RAW-NUMBER efficiency ratio, 'reads generated per $1 of MRR' — who costs most relative to what they pay; paying customers only; Crossdeck never invents a $/read cost). `order`='desc' (default, highest-first) or 'asc' (lowest-first — churn risk, longest-standing, best efficiency). THE RICH ROW: every row carries EVERY metric at once (the customer's HUMAN identity — email/name, resolved best-first — plus mrrCents, ltvCents, netCashCents, readCost, createdAtMs, lastSeenMs, readsPerRevenue, `currency`, paying) regardless of the ranking axis — the cross is pre-done, so you read the row instead of re-joining across tools. Identity is the owner's OWN customers, surfaced by email/name (the same the dashboard shows), never a bare UID. mrrCents is the ONE minted MRR that reconciles to the Revenue page — never re-summed from subscriptions. Use for 'most valuable' (by='ltv'), 'paid me the most' (by='total_spend'), 'top by revenue' (by='mrr'), 'most expensive in reads' (by='read_cost'), 'about to churn' (by='recency', order='asc'), 'oldest' (by='tenure', order='asc'), 'worst read-efficiency' (by='cost_ratio').
list_customers_ranked

Prism by Crossdeck FAQ

How do I improve a ChatGPT Plugin's discoverability?

The levers are the listing surface agents actually read: names, descriptions, keywords, tool metadata, and registry health. Which lever matters depends on where discovery breaks, which is what continuous measurement shows.

What are Prism by Crossdeck alternatives on ChatGPT?

As of 2026-09-28, Prism by Crossdeck competes with ADECI, Alteryx Insights, Answer Charts, Azadea One, Bark AI, BlazeSQL, Bloom Profit Analytics, Book Report, ChartMogul, Cleverbridge, Coupler.io, DataAssist-IO, DATAHILL, Deepnote, Evidence Studio, Ezoic Analytics, Flourish, Fr8Labs Analytics, Fullmetrix, Hex, Hologrow, Keypup, Krewss BI, Menu Integrado, Metorik, Omni, Orion, OWOX Data Marts, Peaka, Perlucem.ai, PublisherChamp, Sales Compass by Luzmo, Steep, Sweet Analytics, Tableau (MCP only; EOL soon), Tenzo, ThoughtSpot Spotter, ThoughtSpot SpotterCode, TitanSigma, VODER, Winnow, WisdomAI, WitCloud, XAPP Analyst in ChatGPT Self-Serve BI & Conversational Analytics, ranked by public Discoverability Score.

Where is this profile measured?

This profile uses the geography attached to the latest public registry snapshot: US. Locale tags are intentionally omitted.

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15 tools agents can invoke