SignalSolved

Our Methodology

How we turn raw data into an honest, sourced answer — and what we refuse to do.

Last updated .

1. Availability results

For a given location and service, we resolve availability into one of four plain outcomes:

  • Available — supported by reliable, sufficiently specific data.
  • Likely available — area-level or strong indirect evidence, not address-confirmed.
  • Check with provider — signals exist, but precision or freshness is insufficient to assert it.
  • Not enough data — no reliable source; we show guidance, not a claim.

We never upgrade a result because of a commercial relationship. ZIP/area results are exploratory; address-level claims require address-level data.

2. Confidence levels

Every availability assertion carries a confidence badge, derived by a single pure function from source tier × geographic specificity × freshness:

Confidence: High Official, recent data that is address-specific or provider-confirmed. How we score Confidence: Medium Official area-level data, or a provider map with known limitations. How we score Confidence: Low ZIP/city-level, older, partial, or crowdsourced data. How we score Confidence: Unknown No reliable source — we show guidance, not a claim. How we score

The exact “freshness” windows are per-source and are being calibrated as real datasets are integrated; until then we apply the qualitative model above and lean conservative.

3. Rankings & “best” lists

Any “best providers in [place]” list is computed from objective, weighted, published criteria: realistic availability at the location, technology tier (fiber generally beats cable/5G-home, which beat DSL/satellite, context-depending), typical performance from cited sources, reliability signals where reputable data exists, and value. Rules we hold to:

  • Weights are explicit and published; changes are logged with dates.
  • Commercial status is excluded from scoring inputs.
  • If a place lacks sufficient sourced local data, we do not publish a ranking.
  • Every figure shown is sourced and dated.

4. Composite scores

Scores like a “good for remote work” area score combine multiple records (is fiber present? cable? 5G home? cell coverage by carrier? typical speeds? redundancy?) through a transparent rubric. We show the inputs, cap precision at the weakest input’s resolution, display the score’s confidence and as-of date, and never imply a guarantee.

5. Data handling

  • Source tier and freshness drive conflict resolution when sources disagree.
  • Missing data becomes unknown — never fabricated.
  • Modeled data (for example, mobile coverage) is labeled and caveated, never treated as ground truth.

6. What our methodology must never do

  • Let affiliate or sponsor status change availability, confidence, or rank.
  • Present precision beyond what the data supports.
  • Publish rankings without sufficient, sourced local data.
  • Use unlicensed data for user-facing claims.

Disagree with a result? That’s welcome — tell us and we’ll review it against authoritative sources.