How We Think & Work

Consumr.AI’s research mindset emerged from watching how clients and practitioners actually work, and learning what helps them get better answers.

It is OK to be wrong

The goal of any research exercise at Consumr.AI is not to be correct on the first attempt. The goal is to understand the process, iterate, and improve.

“It’s OK to be wrong. I want you to be wrong. But the point is you need to understand the process.”

Gaps in knowledge are not failures; they are inflection points. When a data scientist cannot explain accuracy and precision from first principles, that is not embarrassing; it is a prompt to go back and build the foundation properly. The same applies to research design: you will make choices that turn out to be wrong. The question is whether you understand why they were wrong, so the next iteration is better.

Do not be shaken by uncertainty. Understand it.

Design your own research flow

There is no single correct sequence for running research. Traditional practice suggests running qualitative (focus groups, interviews) before quantitative (surveys), so you understand the problem space before measuring it at scale. But there are situations where the reverse is better: run a survey first to understand the magnitude of a problem before committing to deep qualitative investigation.

Both approaches can be right or wrong depending on the objective. The skill is in designing a flow that reaches the answer efficiently given what you know at the start.

Do not be boxed by convention. Ask: given what I already know and what I need to find out, what is the fastest path to a reliable answer? Then design accordingly. Use AI tools freely along the way, but build studies with intent, not impulse.

Drill down and iterate

The most productive research loops follow this general shape:

Survey: establish what is happening at scale. Look for skewness: where do responses cluster in unexpected ways?
Ask why: take the question behind the unexpected skewness to the AI twin. “Why did you respond this way? What is driving this?”
Brainstorm: take the twin’s “why” and use it to generate possible responses or solutions. What could be done to address this need?
Validate: run another survey or concept test to check whether the proposed solution resonates.
Ask why not: if validation is negative, run a qualitative call or follow-up to understand the objection.

This loop (survey → why → brainstorm → validate → why-not) is not a rigid process. Each iteration narrows the uncertainty.

Listen to the consumer, then innovate

The Muthoot Finance example illustrates this directly.

Muthoot Finance (a gold loan company) uploaded an ad creative for AI twin feedback. The twin flagged that the interest rate was missing from the ad: “I don’t see the interest rate; your ad isn’t making sense to me.” The client objected: “We can’t show the interest rate. It’s dynamically assessed per customer based on their credit history. It would never be fixed.”

The client’s frustration was understandable. But the correct response was not to dismiss the twin’s feedback as wrong. The twin was reacting to the ad exactly as a real consumer would, a consumer who does not know that interest rates are dynamically assessed. From the consumer’s perspective, a missing rate signals an incomplete ad.

The reframe: use the consumer’s need as a mechanism. Add a QR code to the ad. The consumer scans it, fills in a short form, and receives a personalised rate. Add urgency: “This rate is only valid for the next three hours. Here is the nearest branch.” Now you have converted a creative limitation into a lead-generation mechanism and created urgency in the same move.

When the consumer’s signal feels unhelpful, the instinct to argue with it is wrong. The question to ask is: what need does this reveal, and how can I meet that need given my constraints?

Governance: the business team is not the product team

Consumr.AI is a specialist tool that requires correct use to produce value. Clients, especially those who come from a general LLM background, will sometimes use it incorrectly. The business team’s instinct may be to accommodate incorrect usage rather than correct it, to protect the client relationship.

That is the wrong trade-off.

The analogy: a customer who wants to buy a car but insists on driving it drunk is still a customer. But there are laws and safety systems that prevent that, regardless of what the customer wants. Customer success and business teams are the governance layer for Consumr.AI. Their job is to tell clients when they are using the platform incorrectly, and redirect them, not to defend the misuse.

Real example from the training: a client pasted a 500-word strategy brief into Converse and expected the AI twin to analyse and rank 20 strategies in order of merit. That is not what Converse is for. Converse is a conversation interface with a purpose-built cap (approximately 250 words), because the platform is designed for dialogue, not for receiving essays. The business team initially defended the client. After internal debate, the cap was raised marginally as a compromise; but the principle holds.

If a client pastes sensitive customer data into the chat interface: who is responsible? Governance is the answer. The team that allows incorrect usage shares accountability for the consequences.

Protecting the product’s integrity over the long run is in everyone’s interest, including the client’s.