Data 360 centralizes the records. Endgame turns them into cited facts, linked to the accounts, people, and deals they concern — ready for Agentforce Coworker, Claude, or any agent on top.
© 2026 Endgame Labs, Inc. · Confidential & Proprietary
01 TL;DR
“Data 360 successfully centralized the evidence. It did not turn that evidence into sales context.”
— Codex, an OpenAI model, reviewing the results
The hard part isn’t storing the records — it’s pulling the facts out of calls, emails and Slack and linking them to the accounts, people, and deals they concern. That work is a layer of its own. Data 360 centralizes the records. Endgame turns those records into agent context. Every question tested exactly that seam.
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02 The stack
bakeoff agent: Claude Desktop · same records · same questions
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03 What Endgame is
Endgame builds and maintains a context graph — a single model, per tenant, of every account, person, deal, and signal across the stack — that people and agents read, and teach.
In the evaluation org, the graph resolved to 2,575 entities connected by 4,153 relationships, carrying 17,822 cited facts — built from the same records Data 360 saw.
© 2026 Endgame Labs, Inc. · Confidential & Proprietary
04 How it works
Connectors bring CRM objects and the engagement record — calls, emails, Slack — into one store. In the evaluation dataset: 308 accounts, 1,395 contacts, 535 opportunities, 2,399 engagements, identical on both sides.
Endgame resolves every engagement to the accounts, people, and opportunities it involves — including engagements that arrive with no CRM identifiers, the way real calls and emails do. The evaluation org resolved to 2,575 entities connected by 4,153 relationships.
An extraction pass reads every transcript, email, and thread and produces discrete facts — 17,822 in the evaluation org, each anchored to the exact source record it came from, and every one carrying at least one link into the graph. A fact in an answer traces to a specific email, call, or message.
Scheduled agents read the graph and keep account perspectives current — a headline briefing and the account’s active work streams, refreshed every half hour across all 215 accounts with open pipeline. The synthesis runs once, on a schedule, from the extracted facts — a specific claim checks against the facts behind it, and every user who asks gets the same answer.
At question time there is no archaeology left to do. When an agent calls an Endgame tool, Endgame retrieves across the graph and its semantic index, ranks against the ask, right-sizes to a token budget, and enforces the calling user’s permissions — the agent gets back pre-ranked, right-sized results. In the evaluation org, the production API returned 90 ranked facts for one account in 218 ms.
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05 How we tested
Two question sets, two objections
Three factual questions any system connected to the records should answer.
They settle the first objection: “why not just connect the agent to Data 360?” Results are final — the scorecard and the full runs are in this deck.Seventeen questions at the working difficulty of real deal work.
They settle the second objection: “easy questions can’t show what the context layer adds.” In verification now; questions and per-tier results land here when sealed.Models — the easy set ran on all three tiers
Fable 5
Reasoning effort: HighSonnet 5
Reasoning effort: MediumHaiku 4.5
Extended thinking: off© 2026 Endgame Labs, Inc. · Confidential & Proprietary
06 The scorecard
The easy set is settled: Endgame answers correctly at every tier; Data 360 lands one clean answer in nine. The representative set lands on this grid when sealed.
| Data 360 | Endgame | |||||
|---|---|---|---|---|---|---|
| Fable 5F5 | Sonnet 5S5 | Haiku 4.5H4.5 | Fable 5F5 | Sonnet 5S5 | Haiku 4.5H4.5 | |
| Renewalthe Databricks renewal date | ||||||
| Last contactwhen Eli Lilly last reached out | ||||||
| Economic buyerwho owns the expansion budget | ||||||
| Q4–Q20the representative set — seventeen questions | in verification — results land here when sealed |
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© 2026 Endgame Labs, Inc. · Confidential & Proprietary
07 The easy set
Every question had a correct answer in the data and a wrong answer sitting in the obvious field. An agent on the data layer read the field; an agent on the context layer got the linked fact.
The CRM close date said Sep 30. The customer’s June 10 email corrected it to Aug 31.
Endgame had extracted the correction as a fact linked to the account — every model tier answered Aug 31 in one turn and flagged the stale field.
The activity rollup read June 2026. The last real inbound was an email of April 3, 2025 — a year of silence.
Endgame’s graph links each engagement to the account with its date and direction — the silence was directly visible.
The contact roles omitted the buyer entirely. On a May 28 call she said: “I own the platform engineering budget.”
Endgame had extracted that statement as a cited fact — every tier named her and flagged the missing role.
The full runs — every Data 360 screenshot, every Endgame citation, and the two extra steps Data 360 needed — are in the appendix.
© 2026 Endgame Labs, Inc. · Confidential & Proprietary
08 The representative set
The easy set shows a data layer alone missing questions the records can answer. It leaves a fair objection standing: easy questions can’t show what the context layer adds. The representative set exists to answer it — seventeen questions at the working difficulty of real deal work, run and scored the same way.
The questions and their per-tier results are being verified and sealed in a parallel workstream. When they land, this section carries the seventeen scorecard rows and worked runs — with screenshots and citations — for the questions that best show the seam.
© 2026 Endgame Labs, Inc. · Confidential & Proprietary
09 The bottom line
Both systems saw exactly the same data. The two rows are different jobs — and the stack that answers is the one that staffs both: the data layer centralizing the records, the context layer turning them into cited, linked, ready-before-the-question context, and every agent on top — Agentforce Coworker, Claude, anything — inheriting the answers.
†For this test we loaded the records into Data 360 directly. In a live Salesforce deployment only CRM is centralized by default — email, calls, and Slack each require separate connectors, and email in particular stays largely outside queryable storage (Einstein Activity Capture writes it to AWS, with native EmailMessage sync only since Summer ’25 and historical backfill capped at 180 days).
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A1 Appendix · the easy set in full · Q1 renewal
The right answer is Aug 31, 2026 — the customer corrects the date in a June 10 email. The obvious field — the CRM close date — still says Sep 30. The truth lives in the email, not the field.
Reached “August 31, 2026 as the operative renewal deadline” only by auto-escalating into a five-minute, 31-source research run. No answer arrived in a single turn.
Reports “2026-09-30” — the Salesforce close date — as “the closest thing to a formal ‘renewal date.’” It never found Amara Mensah’s email correcting the date to Aug 31.
“I don’t have direct access to Databricks account information.” No query is run — it hands back three suggestions and asks which to try. No date.
All three tiers answer “August 31, 2026” in one turn, cite Amara Mensah’s June 10, 2026 email, and flag that the Salesforce close date of 2026-09-30 is the record she was correcting.
© 2026 Endgame Labs, Inc. · Confidential & Proprietary
A2 Appendix · the easy set in full · Q2 last contact
The right answer is April 3, 2025 — silent since. The obvious field — the LastActivityDate rollup — reads June 2026, a year off; read alone, it calls a dead account active.
“Last touch: June 19, 2026 — that’s the Last Activity Date on both the account record and the one open opportunity.” In-flow, and a year off: the rollup field, read as the answer.
States “the most recent touch on record is June 19, 2026” off the EngagementAggregation rollup — while its own closed-won table, in the same reply, shows the real Apr 3, 2025 row.
“No matching tools found,” three times over — and this run used the spelled-out phrasing. It offers manual Gmail / Slack / Calendar checks and asks which to try. No date.
Fable dates the last real inbound to April 3, 2025 — Ibrahim Sato’s email — and confirms the silence since; Haiku puts the last substantive exchange at April 2025 and recommends re-engaging. Sonnet returned the same April 3, 2025 answer (Fable and Haiku frames shown).
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A3 Appendix · the easy set in full · Q3 economic buyer
The right answer is Priyanka Sharma — she says on a May 28 call that she owns the platform-engineering budget. The obvious field — the opportunity contact roles — doesn’t list her at all. Fatima Khan is a FinOps gatekeeper, not the buyer.
Names Priyanka as the “presumed economic buyer to validate, not a confirmed fact” — right name, correctly flagged as missing from the roles, but recommended only “once validated.” A hedge, not an answer.
The honest result: Data 360 is not zero-for-everything. It prints the committee table, then catches the gap — “your buying committee is missing its actual economic buyer.” Shown with full weight.
“Update the Opportunity Contact Role for Fatima Khan to include ‘Economic Buyer.’” The FinOps gatekeeper, confidently recommended as a CRM edit; Priyanka never surfaces.
Every tier names Priyanka Sharma, quotes her own May 28 call — “I own the platform engineering budget” — flags the missing contact role, and rules out Fatima Khan as a FinOps gatekeeper. No hedge, no wrong person.
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A4 Appendix · what it took to get Data 360 to answer
Two of Data 360’s better results took extra steps beyond asking the question. Those steps, documented.
On the plain question, Data 360 on Fable 5 returned no answer in a single turn. It auto-escalated into a research run — 31 sources, 5 minutes 24 seconds — and only that run’s report names August 31, 2026 as the operative renewal deadline.
On the plain question, every Data 360 run read the LastActivityDate rollup as the answer — June 2026, a year off — with no signal the answer was wrong. Getting the right date took re-wording the question to spell out the channels — “who at Eli Lilly last contacted us directly: an email, a call, or a meeting?” Sonnet 5 then lands it: “the April 3 email … from Ibrahim Sato is the last time someone at Eli Lilly directly reached out.” Fable 5’s re-worded run reaches the right region but calls the gap “over three months” — for a silence of more than a year.
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