# Tenzo — cross-site purchasing analysis prompt

Paste the whole of the block below into Claude with the Tenzo connector switched on.
You need a code environment (Cowork or Claude Code) — plain chat cannot hold this much
data — and the Tenzo connector added under Settings → Connectors.

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You are a restaurant purchasing analyst. Using the Tenzo
connector, pull my purchase data and find where we are paying
more than we need to for items we already buy.

SETUP
Work in a code environment, not in plain chat — the dataset is
too large to hold in context. Pull once, save to disk, then
analyse from file.

1. Pull purchases for the last 28 complete days, by store,
   vendor and item, with: item code, item description, vendor,
   pack description, quantity received, unit of measure, and
   total spend.
2. Save the raw pull before you touch it. Every number you
   report to me must be traceable back to it.

STEP 1 — NORMALISE TO A TRUE UNIT COST
Price per pack is spend divided by quantity received. That is
not comparable across stores. Read the pack description and
convert to a true unit cost:

   "Case - 6/Pack (5 LB)"     = 30 LB per case
   "Case - 24/CT (7.2 FL OZ)" = 172.8 FL OZ per case
   "Case - 48/CT"             = 48 each

Express every line as cost per LB, per FL OZ or per each.
Where the unit of measure is a weight but the item is billed
catch-weight, handle it separately and say so — do not
silently mix it in with the fixed-weight lines.

STEP 2 — DECIDE WHAT IS COMPARABLE
Group items that are genuinely the same product at the same
grade. Matching descriptions are not enough: different item
codes across stores frequently hold the same product, and one
item code sometimes holds several. Use judgement, and record
the groups you formed so I can check them.

Exclude the following, and list them separately rather than
dropping them silently:

 - Items bought in inconsistent units across sites. These go
   on a "needs standardising" list, not into the savings.
 - Items where the cheapest option is a different pack, grade
   or brand. Flag these "check pack" and keep them out of the
   headline number.
 - Any unit cost more than about three times the median for
   its group. Treat it as a likely data-entry error and tell
   me which invoice to check.
 - Named farms, single-source producers and specialty items.
   These are usually a deliberate choice, not an overpayment.

STEP 3 — THREE FINDINGS, IN THIS ORDER

A. Same item, same vendor, same pack, different price by
   store. The cleanest finding. For each: item, vendor, pack,
   number of stores, low and high price per pack, spread as a
   percentage, 28-day spend, and the saving if every store
   paid the lowest price already being charged somewhere in
   the group. Total it.

B. Items the group buys several different ways. Restrict to
   items bought by at least half the sites. For each: how many
   sites, how many vendors, how many distinct vendor-and-pack
   combinations, the recommended standard, and the saving from
   standardising on it. Total it.

C. A switch list per site. For each store, the ten items with
   the largest 28-day saving. Columns: what they buy now
   (vendor, pack, unit cost), what to switch to (vendor, pack,
   unit cost), 28-day spend, saving, and a flag of either
   "clean" for like-for-like or "check pack".

STEP 4 — REPORT TWO TOTALS, NEVER ONE
   Realistic — clean like-for-like switches only.
   Ceiling   — including everything that needs a pack check
               first.

Report them separately, with the item count behind each. A
single blended number will be argued with, and you will lose
that argument.

OUTPUT
A short summary with both totals, then the three findings as
tables, then the excluded items with the reason for each
exclusion. Tell me plainly what you were unsure about and
where you had to make a judgement call.

WHAT THIS WILL NOT DO
This gives you a point-in-time answer. It will not tell you
whether the savings actually landed, alert you when a price
moves again, or remember the standardisation decisions so
that next month's run starts where this one finished. That
needs the data held in one place permanently, rather than in
a prompt.