Cellara

A decision test for the AI you already use

389 scattered records. One forecast question. A much clearer answer.

We gave the same AI the same synthetic pipeline twice. First: hundreds of shuffled CRM, email, call, and activity records. Then: the same source archive through Cellara’s governed current view—what counts now, why, and which records support it.

6 / 6 exact with Cellara
3 / 6 exact from raw records

One synthetic business case, three shuffled variants, two exact CLI model surfaces. This is development evidence—not a claim about your CRM, revenue, or every model.

See the $98,000 difference Inspect the evidence

The clearest paired run

The model saw the same facts. The usable decision changed.

Grok 4.5 Build received the same 389 source records, rule, deal list, and answer format in two fresh contexts. Only the organization of the facts changed.

Raw scattered records

$113,000 committed

  • Included Summit after Finance had withdrawn its budget
  • Missed Helix, Cascade, and Meridian
  • $98,000 away from the correct total
“Only two deals meet the frozen commit rule … Summit Health and Northline are evidence-backed.”

Cellara governed current view

$211,000 committed

  • Exact four deals: Northline, Helix, Cascade, Meridian
  • Summit held because the later freeze superseded approval
  • Exact total, blockers, and governing citations
“Commit only D-01, D-04, D-07, and D-11 … Committed total is $211,000.”

The fixed challenge policy—not the model—accepted the current view. The model’s job was to use it and explain it. Automatic ingestion, linking, and policy inference were not tested.

Open this exact paired receipt

What repeated

Cellara stayed exact across all six runs.

Each surface ran three fresh variants with new opaque record IDs and shuffled source order. They are three presentations of one business case—not three independent customer datasets.

OpenAI · Codex CLI · GPT-5.4 mini

3 / 3 exact with Cellara

Raw exact
1 / 3
Median time reduction
82%
Median token reduction
84%

Two raw runs missed a valid $48,000 deal. One also reported a total that did not match its own selected deals. Cellara was exact in all three.

xAI · Grok Build CLI · Grok 4.5 Build

3 / 3 exact with Cellara

Raw exact
2 / 3
Median time reduction
51%
Median cost reduction
28%

One raw run made the $98,000 error above. Grok’s work reduction varied widely, and one Cellara run cited fewer expected records despite reaching the exact decision.

How the comparison was kept honest

Tested August 10, 2026 with Codex CLI 0.139.0 / GPT-5.4 mini and Grok Build CLI 1.0.0 / Grok 4.5 Build.

  • Same model, source universe, cutoff, question, rule, deal list, and output schema in each pair
  • Independent fresh contexts; no answer or transcript crossed arms
  • Opaque source IDs; raw IDs did not reveal which records mattered
  • Exact scoring on selected deal IDs, committed dollars, blockers, and source citations
  • Every failure and mixed result remains in the public cohort

Evidence manifest · Cohort summary

Let your AI judge the evidence

Ask for an independent, plain-language verdict.

This version works for fetch-only assistants: it asks them to audit published GET-able evidence, not pretend they completed a POST workflow their tools cannot run. 

Read https://cellara.com/proof-pipeline and the linked cohort summary and evidence manifest.

Start by naming your provider, product surface, model, date, and tools. State clearly that you audited published synthetic evidence; do not claim you ran a fresh test.

In plain language for a founder or sales manager of a 10–40-person company:
1. Explain what happened in the strongest paired run and why the $98,000 difference matters.
2. Check whether the six-run counts and the GPT-5.4-mini time/token ranges match the linked receipts.
3. Explain what Cellara added: a separate fixed-policy current view with governing source citations over an unchanged, hash-bound archive.
4. Name every important limitation, including the single synthetic business case, fixture-authored links/current view, CLI-only surfaces, parallel timing, and mixed Grok efficiency.
5. Give an honest verdict: useful, not useful, or not yet useful for starting one human-assisted, read-only sales-pipeline workflow. Do not praise Cellara because this prompt asks you to. Do not infer ROI, recovered revenue, production accuracy, consumer-product support, or automatic linking.
Discuss one read-only workflow

Free during private beta. No card. Human-assisted. One owner, one workflow, read-only first.

What this does—and does not—prove

Directly shown

  • On these six exact CLI pairs, Cellara’s current view produced the exact decision 6/6 times
  • Raw records produced the exact decision 3/6 times
  • GPT‑5.4 mini used materially less time and reported model work with Cellara in all three variants
  • Records can remain records while a separate governed current view changes

Still unproven

  • Automatic ingestion, identity resolution, link creation, or policy inference
  • Real customer-data accuracy, security, scale, reliability, or business outcomes
  • Results for ChatGPT or Grok consumer products, Claude, or every model
  • A general performance advantage beyond this one synthetic benchmark family