Sales Collateral Recommender

Challenges Sales Collateral Recommender addresses

Done by hand, sales enablement means gathering the prospect and the conversation, working through 3 separate passes over the same material, then producing how to use these, recommended assets and what the library does not have. None of it is difficult and all of it is exacting, which is the combination people are worst at holding. The errors that matter are the ones a tired reader does not notice, and they surface later — in a reconciliation, or in somebody’s reply. It waits until someone remembers it, which is usually the point at which it has become urgent. As volume grows the work does not get harder, only longer, and the first thing to go is the checking.

Sales Collateral Recommender runs that same sequence end to end and returns the result as structured artefacts. What it cannot settle it hands over rather than guesses at, and your correction is kept: it asks “Are these the assets you would take in?” after every run, and those answers become the set it is measured against. Nothing that moves money, alters a contract or reaches a customer executes without human approval, and every action is written to an audit log. The gain is in the volume that no longer has to be read, not in removing the judgement.

How it works

Step 1: Searching the collateral library

First of 3. It works from the prospect and the conversation and feeds the step after it.

Key Tasks:

• Covering the whole set: Every record in scope is examined, not a sample. That is the difference between a run and a spot check.
• Judging relevance by content: Whether something belongs in this run is decided from what it says rather than from where it was filed or how it was named.
• Discarding visibly: What is excluded is recorded as excluded, so "nothing found" can be distinguished from "nothing looked at".

Outcome:

• Relevant items found: They pass to the next step with the reason they were selected attached.
• Nothing relevant: The run reports that it found nothing and stops, rather than producing an empty artefact that reads like a failure.

Step 2: Matching assets to the conversation

Step 2 of 3. It takes what step 1 produced and hands its result to step 3.

Key Tasks:

• Finding the counterpart: It searches the connected system for the record this one should correspond to, using the identifiers taken from what step 1 produced.
• Comparing field by field: Each field is checked against its counterpart rather than the documents being compared as wholes, so a single line that disagrees is reported as that line rather than as a failed match.
• Applying your tolerances: The variance you accept is configuration. A difference inside it clears; a difference outside it is held, and the amount is stated rather than described as a discrepancy.

Outcome:

• Everything agrees: The record clears and moves on without anyone reading it.
• Something does not agree: Each disagreeing field is reported with both values and the size of the gap, so the review starts from the discrepancy rather than from the whole document.
• No counterpart exists: The record is held and flagged as unmatched rather than passed through as clean, which is the failure mode that costs the most to find later.

Step 3: Writing the usage note

Last of 3. It takes what step 2 produced and produces how to use these and recommended assets.

Key Tasks:

• Writing from the run, not from a template: The text is built from what this run actually found, so two sales enablement outputs differ where the underlying records differ.
• Leading with what needs a decision: The exceptions come first and the routine detail follows, because the reader is deciding rather than reading.
• Staying inside the evidence: Nothing appears in the text that is not supported by a record the run examined. Gaps are stated as gaps.

Outcome:

• Artefact ready: A finished artefact, traceable line by line to the records behind it, ready for a person to accept or correct.

Step 4: Your review, and what it changes

The run ends with a person, not with a result being filed.

Key Tasks:

• Asking a specific question: It asks “Are these the assets you would take in?” rather than for a rating. A question about this run is answerable; a score out of five is not.
• Keeping the correction: What you change is recorded against the case that produced it, so the disagreement is retrievable rather than absorbed.
• Building the evaluation set: Those cases become what the agent is measured on. It is scored against your judgement rather than against a general benchmark.

Outcome:

• A measured agent, not an assumed one: The cases Sales Collateral Recommender handles well and the cases it does not are both visible, and the second list is the one that decides what changes. Nothing is retrained silently on the back of a single correction.

Why use Sales Collateral Recommender?

• Answers from your own material: It retrieves from a knowledge base you populate — your policies, contracts and reference documents — so its decisions follow how your business actually operates rather than a general model’s assumptions. Sources are cited alongside the result.
• Every statement cites its source: Findings come back with the records behind them, so a reviewer can check a claim instead of deciding whether to trust it. An assertion with no source is the expensive kind to discover late.
• A batch is one run, not a hundred: It works the whole set in a single pass and returns a row per item with its verdict, so the volume that needs no attention never has to be opened.
• Corrected by the people using it: After each run it asks “Are these the assets you would take in?”. Those answers become the evaluation set, which means it is measured against your judgement rather than ours.
• Reads and reports, does not act: It returns a result for review rather than writing changes back on its own. Anything that moves money, alters a contract or reaches a customer needs human approval first.