Product Fit Signals

Challenges Product Fit Signals addresses

Done by hand, account growth means gathering customer behaviour export and what you can offer, working through 3 separate passes over the same material, then producing signal → product, signal strength and what to do. 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.

Product Fit Signals 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 real signals?” 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: Reading the behaviour export

First of 3. It works from customer behaviour export and what you can offer and feeds the step after it.

Key Tasks:

• Locating the material: It works from customer behaviour export and what you can offer, so nothing has to be forwarded, re-keyed or renamed first.
• Handling the format it arrives in: Scanned pages, native documents, spreadsheets and message bodies are all read the same way, including layouts where the relevant figure sits inside a table rather than a labelled field.
• Pulling the fields that matter: Only the fields the rest of the run needs are extracted. What cannot be read confidently is recorded as unread rather than filled in with a best guess.

Outcome:

• Fields extracted: The fields are available to the steps that follow, with anything unreadable listed rather than silently defaulted — which is what stops a bad extraction becoming a confident wrong answer three steps later.

Step 2: Reading the signals

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

Key Tasks:

• Reading the signals specifically: This pass is scoped to the signals rather than to the document as a whole, so a field that appears in more than one place is taken from the one that governs.
• Keeping the original alongside: Each extracted value stays linked to where it was found, so a figure that looks wrong can be checked against the source rather than re-entered.
• Locating the material: It works from what step 1 produced, so nothing has to be forwarded, re-keyed or renamed first.

Outcome:

• The signals captured: The fields are available to the steps that follow, with anything unreadable listed rather than silently defaulted — which is what stops a bad extraction becoming a confident wrong answer three steps later.

Step 3: Writing the next actions

Last of 3. It takes what step 2 produced and produces signal → product and signal strength.

Key Tasks:

• Writing from the run, not from a template: The text is built from what this run actually found, so two account growth 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 real signals?” 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 Product Fit Signals 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 Product Fit Signals?

• 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.
• Takes documents as they arrive: Scanned pages, native files and awkward layouts are read as they are. Nothing has to be renamed, re-keyed or converted into a template before a run.
• Corrected by the people using it: After each run it asks “Are these real signals?”. 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.
• Structured results, not prose: All 4 artefacts are structured — signal → product, signal strength and what to do — so a result can be scanned, sorted and acted on instead of read end to end.