How 18 answers become a view of organisational friction. The questions, the maths and the evidence behind your score.
18 scored answers + 5 open-text fields
Six dimensions. A population reference.
Defined inputs → bounded calculations → a clear read
This public summary explains the structure, headline maths and validation sample. Item-level weights, segment baselines, prompts and intervention mappings are available in the full methodology pack under NDA.
SignalPulse produces three headline numbers, a band from Lower to Severe and two cultural flags that help you read the result.
The friction dragging the organisation. An equal-weighted mean of three drag dimensions.
The capacity already in the system, shaped by clarity, speed and leadership enablement.
The gap between capacity and friction, adjusted for how normal that friction has become.
A short diagnostic using a consistent 1 to 5 scale. The questions cover three drag dimensions, three capacity dimensions and the cultural normalisation item.
Workflow drag covers decisions reversed, duplicate work, coordination breakdown. Data drag covers reporting friction, version disputes, time spent assembling information instead of using it. Leadership drag captures whether decisions get unmade above the level of the person doing the work.
Higher scores mean more friction. The three dimensions average into the headline Complexity Tax.
Clarity covers shared understanding of priorities. Speed and scale covers the organisation's ability to move once it's decided. Leadership enablement captures whether the senior layer removes blockers or creates them.
These items are reverse-scored so that higher always means better. They combine into the Efficiency Dividend.
A single question that captures whether the organisation still feels the friction or has accepted it as the way things are. Sensitive cultures (1, 2) make recoverable friction visible. Normalised cultures (4, 5) bake it in. This item doesn't change the Tax score itself but multiplies the Net Signal up or down by 20%, since recoverable friction is worth more than friction that's already invisible.
Statutory registries, market data and research add context to the diagnostic. Each source below is labelled to distinguish what is live from what is still on the roadmap.
Real-time API lookup against the UK statutory registry. Returns company number, status, registered address, incorporation date and officer-level data where relevant. Used to pin every UK submission to a real legal entity.
For any submission resolved to a public ticker, we pull a 33-metric financial snapshot at the moment of submission. Revenue, operating profit, EBITDA, margins, headcount, sector classification, year-on-year deltas and a benchmark index for the relevant exchange.
A curated lookup that maps company names to exchange tickers across LSE, NYSE, NASDAQ, Euronext, SIX, Deutsche Börse, Tokyo, Hong Kong and the major Australian and Indian exchanges. Index pairing happens automatically so YoY performance gets compared to the right benchmark (FTSE, S&P, DAX, CAC, SMI, etc).
The US equivalent of Companies House for any organisation that files with the SEC. On roadmap for 2026 to give US private-company submissions the same statutory anchoring UK submissions already have via Companies House.
The instrument was fitted on 443 clean responses collected via Prolific across three research stages (January to April 2026). UK and US knowledge workers across function, seniority and sector. This is what gives every score a real population reference rather than a synthetic baseline.
Every completed snapshot adds to the aggregated, anonymised reference base, segmented by industry, company size band, role family and function. As volume grows the segment-level baselines tighten. The score isn't just compared to the original n=443, it's compared to the population that resembles your respondent.
The 18 scored items and 5 open-text fields the respondent submits in the 10-minute snapshot. Each open-text field is interpreted by an LLM with constrained driver vocabulary so qualitative signal feeds the score rather than sitting separately.
Timestamps, time-per-section, pauses and item revisits. We don't score this directly but we use it to flag patterns: respondents who race through the instrument, or who pause heavily on specific items, get the result framed with the right caveat. Quality control, not surveillance.
For procurement and audit. Exact field mappings, refresh cadences, retention windows and segment definitions are available under NDA. Request the data pack ↗
The headline score uses defined averages and bounded multipliers. Beneath it, regression weights, segment baselines and qualitative classification shape the drivers and narrative.
Tax = mean(workflow drag,
data drag, leadership drag)The three drag dimensions carry equal weight in the headline Complexity Tax. Higher means more friction.
Dividend = mean(clarity, speed & scale)
× leadership coefficientThe leadership coefficient ranges from 0.8 to 1.2. It adjusts the capacity score by up to 20% in either direction.
Net Signal = (Dividend − Tax)
× normalisation coefficientThe normalisation coefficient also ranges from 0.8 to 1.2. It reflects whether the organisation still feels its friction or has accepted it.
Six techniques used in the engine. Open each one to see its role and the calculation behind it.
Each of the 17 scored items has a population β coefficient fitted via standardised regression against an external cultural anchor (the normalisation item). We use β against an external anchor specifically to avoid circularity, regressing on the Tax score itself would just describe how we computed it.
contribution = (item_value - 3) × |β_pop|Every respondent is compared not just to the full sample but to the segment that resembles them. We compute z-scores against pre-fitted segment baselines (company size, role family, function, seniority) so a CFO's score is read against CFO norms, not against everybody.
z = (your_score - segment_μ) / segment_σThe four bands (Lower, Moderate, High, Severe) use fixed thresholds for the headline placement, plus hysteresis on borderline cases so a score of 3.39 doesn't flip Moderate or High based on a rounding accident. The exact threshold values are published in the formula section above.
band = max(t ∈ T : tax ≥ t)Two multiplicative coefficients shape the headline numbers. Leadership enablement multiplies the Efficiency Dividend up or down by ±20%. The cultural normalisation flag multiplies the Net Signal up or down by ±20%. Bounded, defined, documented in the code.
net_signal = (dividend − tax) × q_norm_multOpen-text responses are interpreted by gpt-4o under a locked taxonomy: each response is scored on a 1-5 friction scale, tagged with a primary and secondary driver drawn from a fixed 10-driver vocabulary, and weighted by the relevant population β before flowing into the driver-attribution layer. Multi-label output, weighted contribution. On model failure or malformed JSON, a deterministic neutral fallback fires so a submission never returns a blank score.
driver ∈ {rework, handoff, tool sprawl, ...} × |β_pop|The exposure pound figure on the results screen is an explicit Fermi estimate, headcount × hourly-rate proxy × friction coefficient derived from published research. We show a low, mid and high range, not a single number, and we label it "modelled" so a sophisticated reader knows what they're looking at. Order-of-magnitude reliable, not pinpoint accurate.
exposure ≈ headcount × rate × hrs(tax) × 52The deeper modelling pack includes the full β coefficient table, segment baselines, driver mapping, LLM prompts and financial coefficient sourcing. Request the technical detail ↗
The instrument was developed and validated through three rounds on Prolific between January and April 2026, pooling 443 clean responses across UK and US knowledge workers. The driver-level weights were fitted via standardised regression against a held-out cultural anchor (the normalisation item), avoiding circularity with the Tax score itself. Segment baselines for company size, role type and function are computed from the same dataset.
Clean respondents in the validation pool.
R² on the cultural-anchor regression.
Mean Complexity Tax across the sample.
Mean Net Signal across the sample.
Prolific · UK and US knowledge workers · Three rounds · January to April 2026
Across 511 respondents from the research panel and live diagnostic, decision delay and workflow friction account for 63.8% of primary drivers. We group these patterns as decision friction: the gap between a decision and the action that follows.
Named decision delay or workflow friction as their primary driver.
Pooled research and live diagnostic responses, n=511. Bars use the same 0 to 40% scale. Combined decision friction is approximately 7 times as frequent as the next primary driver, data fragmentation.
A meeting that produces no answer. An approval loop that stays open. A handoff that leaves the work unresolved. SignalPulse identifies the shape of that friction and the dimensions where it concentrates, so the result points to what needs attention.
Use the score, band and driver list as your primary read. Company-specific comparisons and financial exposure add context, with their assumptions made explicit.
The peer comparison shown on the results screen draws from a reference set of large consumer-goods organisations. If you operate in CPG or an adjacent sector, the benchmark is directly relevant. If you operate outside those sectors (technology, financial services, pharma, retail), the peer comparison should be read as directional rather than precise, since the underlying reference set isn't a perfect sector match. We're actively building out sector-specific reference sets and will note in the results when one is available for your category.
The headline pound figure ("modelled exposure: £X, £Y million") is a Fermi-style estimate, not a measurement. It combines your Tax band, an assumed average compensation level for the relevant company size and a friction-time coefficient derived from published research on organisational drag in knowledge work. It's calibrated to be directionally sensible rather than pinpoint accurate. Two organisations with identical scores can have meaningfully different real exposures depending on factors the instrument can't capture in 10 minutes.
Use the Tax band and driver list as the primary read. They're validated against a real population. Use the financial figure as a sense-check on order of magnitude: is this a £100k problem or a £10 million problem? It'll tell you which side of that line you're on, with confidence. The exact number is a model not a measurement, and the basis line under the figure states this explicitly.
The item-level regression coefficients, segment baseline tables, the driver-to-intervention mapping, the BX narrative prompts and the deeper engineering of the model aren't in this public summary. They're available under NDA for client procurement, internal audit or any other due diligence context where the detail matters.
For procurement, internal audit or technical due diligence, request the full methodology pack under NDA. We can also walk through the model with your team in 30 minutes.