HumanaQ Science explains the behavioral due diligence process behind HumanaQ. Each participant writes five open-ended answers in 10 to 15 minutes. The MiliMatch Cognitive NLP engine extracts 24 workplace behaviors across 6 behavior domains, and HumanaQ reads them against the deal thesis, value creation plan and business objectives to show which teams a deal depends on, where acquirer and target leadership teams differ, and which questions to ask.
The science
Deals are underwritten on numbers. We measure the part that delivers them.
HumanaQ reads how leadership teams operate, from their own words, and connects it to your deal and its business objectives: which teams the plan depends on, where two companies may differ, and which questions to ask.
24 workplace behaviors 6 behavior domains 5 million people through the MiliMatch methodology
01 · The behavioral foundation
Built on a decade of behavioral language science.
HumanaQ doesn't build its own psychometric engine. We partner with the people who did.
Our behavioral data comes from MiliMatch, whose Cognitive NLP engine identifies behavioral patterns in natural language: how people interpret direction, set priorities and take ownership. Its engine was built on tens of millions of real-world texts and refined through a decade of enterprise deployments.
The input is simple. Each participant answers five open-ended questions in their own words. That takes 10 to 15 minutes. No personality test, no rating scales, no games.
The engine extracts behaviors. HumanaQ's translation layer connects them to the deal context and your business objectives: we choose the behaviors that fit the deal, read them against what the deal has to deliver, and point to where to look.
- 01
Their words.
Five open-ended answers, 10-15 minutes per person.
- 02
Behavioral data.
The engine extracts 24 workplace behaviors with Cognitive NLP.
- 03
The translation layer.
HumanaQ connects the behaviors to your deal context and business objectives.
- 04
Where to look.
The teams and behaviors behind a gap, and the questions to ask about them.
02 · The research we build on
The research behind the reads.
Our behavioral model rests on a well-supported finding in cognitive linguistics and psycholinguistics: persistent cognitive and behavioral structures produce persistent language structures. People reveal how they prioritize, coordinate, make decisions and respond to pressure through the structure of their language, not only through what they say.
Where the 24 behaviors come from
In 2004, Peterson and Seligman published the Values in Action (VIA) classification, the result of a three-year study involving 55 social scientists. They identified 24 measurable human strengths organized under six higher-order categories. MiliMatch kept that 24/6 structure and reoriented it from virtue and wellbeing toward operational execution: how teams deliver under pressure.
- VIA classification published
- 2004VIA classification published
- Social scientists in the study
- 55Social scientists in the study
- Measurable human strengths
- 24Measurable human strengths
- Higher-order categories
- 6Higher-order categories
How the engine reads language
Cognitive NLP, the engine MiliMatch built, analyzes how people say things rather than what they say: framing, attribution, certainty structures, ownership language, sequencing and abstraction level. It is rules-based, not a black box. Every linguistic marker maps to one of the 24 behaviors, and every score is auditable and explainable.
The dataset the engine was built on
- Emails
- 10M+Emails
- Reviews and surveys
- 10M+Reviews and surveys
- News stories
- 5M+News stories
- Social posts
- 5M+Social posts
- Resumes and interviews
- 2M+Resumes and interviews
Refined through a decade of enterprise deployments at organizations including JPMorgan Chase, LinkedIn and Zappos. 5 million people have gone through the MiliMatch methodology. Figures: MiliMatch.
What we do not claim
The engine was validated across organizational contexts broadly. The M&A-specific dataset, linking pre-close behavioral patterns to post-close integration outcomes at deal level, does not yet exist at publishable scale. We are building it through live engagements.
03 · From behavior to deal decisions
A score is not an answer. The deal context decides what matters.
We start from the business objective, not from the people. The same behavior can be a risk in one deal and a strength in another, so we choose the behaviors that fit the deal: for an acquired product development team, being methodical and detail-oriented may matter more than caring or compassion. The thesis is one input; the full deal context decides what to read, and the scores come last.
What we read against
- Business objective
- KPIs and OKRs that matter
- Deal thesis
- Value creation plan
- How the two organizations combine
- Deal stage
- 01
Thesis fit.
Can this leadership team execute what the value creation plan asks of it? We map each plan step to the behaviors it depends on, before we look at any scores.
- 02
Leadership alignment.
Where the plan joins two teams, we compare them behavior by behavior, and a gap can be traced from the org level down to the departments and teams driving it. Not one "culture fit" score.
- 03
Key-team dependency.
Which teams and roles the business objective depends on, and what in the deal design may put pressure on them. We read the acquiring team as closely as the target's, because both sides shape how the combined teams operate.
- 04
Integration readiness.
Which steps in your existing plan may deserve a closer look, and which questions to ask before they start. We point to where to look. The plan and the decisions stay yours.
Leadership alignment
Behavior by behavior, from org level down to teams.
- Acquirer team
- Target team
Illustrative
- Structured
- Drive
- Assertive
- Flexibility
04 · Across the deal
Start from the business problem. Then look at the people.
People evidence matters most where people move the numbers. Before due diligence, most questions are financial and there is rarely access to the target's people, so we start at due diligence. For each block: the business pain point, the questions we analyze, and where the output points you to look.
01 · Before close
Due diligence
Through to signing and close.
Business pain points
- The value creation plan depends on a few teams hitting their KPIs, and diligence rarely shows how those teams operate.
- Access is often limited to senior executives, and the process is confidential.
- People issues surface late, as hard and soft conditions to close.
Questions we analyze
- Which teams does the business objective depend on, and which behaviors matter for that work?
- Where the plan puts the two companies together, how does each one operate?
- What capabilities and potential do leaders show for the roles the plan asks of them?
Where the output points
- The teams and behaviors where acquirer and target differ most against the objective.
- Questions to take into management meetings and confirmatory diligence.
- People topics that may need to be part of the conditions to close.
02 · After close
First 100 days
Where most adjustments are made.
Business pain points
- Integration KPIs and OKRs are set, and the first bottlenecks start to show.
- Many adjustments are made quickly, with little evidence about how the combined teams operate.
Questions we analyze
- Which teams sit behind the bottlenecks in the first OKRs?
- Where may the combined teams differ in how they decide, plan and hand off work?
- Which departments drive a gap that shows up at company level?
Where the output points
- The departments and teams driving a gap, traced from the org level down.
- Questions for the integration lead and the owners of the affected KPIs.
03 · Ongoing
Ongoing integration and value capture
Continuous, after the first 100 days.
Business pain points
- Post-close reviews are board-level and financial: capital, operating cost, customer churn.
- When a number slips, it is hard to see which part of the organization is behind it.
Questions we analyze
- Where do people influence the numbers under review?
- Has the behavior of the teams behind a KPI shifted since the baseline?
Where the output points
- Where to look when a number moves, and which teams to ask about it.
- A consistent read over time, using the same instrument.
We don't see your financials or KPIs, so the output shows where to look and what to ask; the diagnosis stays with you. Post-close integration work may continue with our partner BowMerge.
05 · Validation and rigor
How we keep ourselves honest.
Behavioral data is useful and noisy. Published research on language-based measures shows real signal and real limits. We'd rather tell you our rules than sell you a black box.
Requirements before scores.
For each plan step we lock the behaviors it needs before we see a single result. That stops us finding what we expected to find.
Teams, not individuals.
Findings are read on teams of five or more and reported at team level.
A gap has to be a real gap.
A difference counts only when it crosses a level and a minimum distance on the scale. Near misses become questions for management, not findings.
Every finding is traceable.
Each one points back to the plan step, the behaviors and the read behind it.
Strengths count too.
Every report looks for where the plan can move faster, not only where it may stall.
Same instrument, every time.
Re-measurement at Day 30, 100 or 365 uses the same questions and scoring version, so changes are comparable.
Outcomes
We log every engagement against what actually happened in the integration. We'll publish outcome results once the sample is large enough to mean something, and not before.
06 · What it is, and what it isn't
Clear about the limits.
It is
- a behavioral evidence layer for deal and integration decisions.
- a read of how teams are likely to operate under pressure.
- a complement to financial, legal and commercial diligence.
- a pointer to where to look and what to ask.
- built from five written answers.
It isn't
- a hiring, firing or pay decision tool.
- a personality test or a diagnosis.
- a replacement for them, or for your judgment.
- a diagnosis, a recommendation, or a prediction of who will leave.
- monitoring of email, chat, calls or social media.
One snapshot can show where to look. It can't tell you what will happen. That's why we pair every finding with a Day 30 and Day 90 check.
07 · Data, privacy and regulation
Designed for regulated, confidential deals.
Behavioral data about senior people deserves the same care as anything else in the data room.
Minimal input.
Five written answers per participant. No biometric data, no voice or video, no scraping.
Purpose-limited.
Data is used only for the engagement it was collected for, and deleted within 30 days of the final contracted report.
GDPR.
Engagements are designed to run under a data protection impact assessment and agreed contract terms before anyone is assessed. Where a works council or employee representatives must be involved, that happens first.
EU AI Act.
We treat behavioral assessment of people at work as potentially high-risk under the EU AI Act and design our process to that standard.
Cross-border data.
Behavioral scoring is performed by our behavioral science partner in the United States.
Confidential processes.
For sensitive or competitive deals, participant scope, timing and how the exercise is introduced are agreed with you before anything is sent.
08 · FAQ
Questions about the science.
Can you really measure behavior from five written answers?
Language carries consistent signals about how people decide, prioritize and take ownership, and published research supports language-based measures of leaders. The signal is real but not perfect, which is why we read teams rather than single individuals, use thresholds before calling anything a finding, and re-measure over time.
Who builds the behavioral model?
The behavioral scoring is done by MiliMatch's Cognitive NLP engine. HumanaQ owns the M&A layer: which behaviors matter for which deal, how they're read against the deal context and business objectives, and where the output points you to look.
Isn't this just a culture assessment?
No. Culture assessments usually ask people how they see their organization. We read how leaders actually operate, and we never reduce two teams to a single fit score. We compare them behavior by behavior, only where the plan brings them together.
Can it tell me who will leave after close?
No, and we're wary of anyone who says they can from a single snapshot. What it can show is which roles the thesis depends on and where the deal design may put pressure on them, so you know which questions to ask before close.
How do you validate your results?
Every engagement is logged against what happened in the integration, and our read rules are tested against that log. We'll publish outcome results once the sample is large enough to be meaningful.
How is personal data protected?
Inputs are limited to five written answers, used only for the engagement, and deleted within 30 days of the final contracted report. Each engagement is designed to run under a DPIA and agreed contract terms before anyone is assessed.
See the science on a real deal.
Bring us a thesis and a leadership team. We'll show you what the behavioral evidence says about whether they can deliver it.