Services

Behavioral intelligence and decision testing

Compare the options before you build, launch, or change something people need to use.

I help businesses, governments, and institutions examine how a proposition, service, policy, or environment may be understood and adopted. The work combines behavioral analysis, scenario testing, and AI-enabled simulation around a specific decision.

The questions we can work on

• Products and commercial decisions: which proposition to test, how pricing changes the choice, and where a customer journey creates friction.
• Services and public policy: what may prevent participation, where a process becomes difficult, and which intervention deserves a pilot.
• Organizational change: whose approval matters, what teams need to act, and where adoption could stall.

What you receive

A decision brief comparing the alternatives, plausible explanations for resistance or uptake, and a recommendation for the next test. It makes the assumptions, evidence gaps, and conditions that could change the recommendation explicit.

From analysis to a measured pilot

1. Define the decision. Agree on the audience, alternatives, constraints, and outcome that matters.
2. Challenge the alternatives. Use available research and appropriate models to investigate possible responses and practical barriers.
3. Test against observation. Design a proportionate user study or operational pilot to check the recommendation.
4. Update the decision. Compare the observed result with the original assumptions and revise the approach.

Synthetic audience pretests support early exploration. Predictive use requires validation for the population and decision concerned.

Questions

Common questions

Can AI tell us which of our contacts are actually serious instead of guessing from job titles?

Yes, within limits worth stating plainly. Behavioral intelligence ranks likelihood from observable signals: what people do, what they engage with, and what changes in their situation, rather than from a title on a profile. That is a real improvement on intuition, and it is not certainty. I build these as decision support, so you get a ranked list with the reasoning attached and your people can see why someone surfaced and override it. A score nobody can interrogate gets ignored, and it should be.

Can a system qualify inbound interest in Arabic and English before it reaches my team?

Yes, and multilingual handling is a requirement in this region rather than a feature. A qualification agent can answer routine questions, capture what you need to know, score intent against your criteria, and pass genuine prospects to a person with the whole conversation attached. Everything else waits in a queue instead of consuming your team's day. I build these for Arabic, French, and English, and the language quality of the handoff is where most off the shelf tools fall down.

Can this predict which customers are about to leave, or which people are about to disengage?

Often yes, because disengagement leaves a trail before it becomes a decision. Falling usage, slower responses, unresolved complaints, and changes in who deals with you are all observable. A model ranks that risk so your team spends attention where it matters. Two rules make it work. Act on the ranking with a human contact rather than an automated message, and check afterward whether the intervention changed anything. Otherwise you have a dashboard, not a retention program.

Related: The Epirroi Factors · Strategic foresight · All services