The evidence loop

The full moat, end to end — how evidence becomes a decision that makes the next decision smarter.

Synloom’s durable advantage isn’t any single agent — it’s a closed loop that compounds. Here is the whole cycle in one place.

Ingest

Evidence enters, embedded for search.

Cluster

Step by step

1
Ingest & embed
Each evidence item is stored with a credibility level and a vector embedding, so similar evidence can be found by meaning, not just keywords.
2
Cluster
Synthesis groups related evidence into candidate problems — using embeddings when available, falling back to the model otherwise.
3
Score & rank
Each problem is scored on frequency, severity, revenue impact, and strategic fit, with a floor that stops popular-but-trivial issues from outranking strategic ones.
4
Decide
You review the ranked problems and record decisions as outcomes with a target metric.
5
Measure
On the measurement date, you log the actual result — hit or miss.
6
Reinforce
A hit reinforces the evidence behind it (it decays slower); a miss penalizes it (decays faster). The next synthesis weighs evidence by this track record.

The compounding part

Over many cycles, the evidence that reliably predicts good outcomes gains influence and the noise fades — automatically, and specific to your product.

Example · One full turn of the loop

From 300 support tickets to a shipped fix that stuck

A three-person product team at a payments startup

The situation. The team is drowning in feedback — 300 tickets, dozens of sales notes, a churn survey. It’s not clear what to build next, and everyone has a favorite theory.

What they do

1They import everything as evidence, tagging credibility as they go.
2Synthesis clusters it and ranks “failed payments on retry” #1 — high frequency, high severity, and a clear revenue link.
3They accept it, record an outcome: expected retry-success up 15% in 30 days.
4They ship the fix. On the measurement date, retry success is up 22% — a hit.
5The interview and analytics evidence behind that problem get reinforced.

The result. The fix moved the metric, the win is recorded, and next quarter’s synthesis already leans toward the evidence sources that just paid off. One loop done — and the tool is now a little sharper than it was.

Lesson: the loop only compounds if you close it

What it is: The cycle has six steps, but the last two — measure and reinforce — are the ones teams skip under pressure. Ingest-cluster-score-decide feels like progress; measuring the result feels like overhead.
Why it matters: Without the final steps, you get a good ranking once, but it never improves. With them, every quarter’s ranking is better-calibrated than the last — that’s the entire moat.

A half-loop is just a smart search. The full loop is what competitors can’t copy from your data.

Step by step

Ingest & embed

Each evidence item is stored with a credibility level and a vector embedding, so similar evidence can be found by meaning, not just keywords.

Cluster

Synthesis groups related evidence into candidate problems — using embeddings when available, falling back to the model otherwise.

Score & rank

Each problem is scored on frequency, severity, revenue impact, and strategic fit, with a floor that stops popular-but-trivial issues from outranking strategic ones.

Decide

You review the ranked problems and record decisions as outcomes with a target metric.

Measure

On the measurement date, you log the actual result — hit or miss.

Reinforce

A hit reinforces the evidence behind it (it decays slower); a miss penalizes it (decays faster). The next synthesis weighs evidence by this track record.