From datatodecisions

Qlue is designed for:

Brands:

• Consumer brands who have a lot of on-going decisions to make on product innovation, marketing plans & growth strategy. 
• Teams that have a lot of existing valuable data assets, without a way to leverage it to answer new & on-going questions. 
• The teams within client organisations we work with are: Insights, Marketing, Innovation, strategy & growth teams

Partners:

• Qlue also works in partnerships with consultants & agencies to serve clients in a partnership model.
• Kinds of partners we work with are: Boutique agencies, market research agencies, branding studios & strategy partners and consultants

Many AI research tools are built to make research faster: generate synthetic respondents, run quick concept checks, summarise interviews, analyse open-ends or simulate survey-style responses. Qlue is built for a different job: strategic decision simulation. Consider it an ongoing, evolving part of your organisation that compounds institutional knowledge.

Qlue does not start with a generic synthetic sample. It starts with your own source material. From there, Qlue builds a connected intelligence layer that can be used to explore new questions, test scenarios and shape strategic options. The difference is the full system: Client-specific intelligence, digital twins built from real source material, and traceability / evidence back to source material. 

So if you need a quick synthetic read on a stimulus, an AI research tool may be useful. If you need to decide where a brand can stretch, which innovation territory deserves investment, how a category is shifting, or what strategic route has the strongest evidence behind it, Qlue is built for that larger decision.

A typical project can take 1-2 weeks, with larger, more complex projects taking up to 3 weeks (against a traditional 8–12 weeks)

From a combination of evidence traceability, confidence notes and expert judgement throughout the process.

Qlue is not a black-box tool that simply takes your data and generates an answer. Every Qlue simulation is guided by experienced strategists who help frame the brief, choose the right inputs, apply the right strategic lenses, challenge the output and shape what becomes a real-life recommendation.

The platform itself is grounded in trusted source material: research transcripts, survey findings, reports, dashboards, strategic documents, market signals and other inputs. Qlue shows the evidence behind an answer, the assumptions being made, and the level of confidence attached to the recommendation. 

This means the process makes it clear whether an answer is directly supported by evidence, reasonably inferred, contradicted, or in need of further validation.

Qlue is strongest when the problem is complex, strategic and multi-dimensional. It is especially useful for questions like:

• Where can our brand stretch next?
• Which product idea deserves further investment?
• What does a new audience really want from this category?
• How should we premiumise without losing credibility?
• Which claims, messages or propositions are most likely to land?
• What hidden opportunities exist across our existing research?
• How do market, culture, category and consumer signals connect?
• Which strategic direction has the strongest evidence behind it?

Qlue works best when the question needs more than one input source and more than one lens.

Qlue is not the right tool for every question. We would not recommend it for:

• Experiential testing: such as sensory, taste, texture, fragrance or physical product experience.
• Observational behaviour research: such as how people brush their teeth, move through a store, pay at a counter or use a product in the real world.
• Highly polarising or emotionally charged content: where real human response, cultural sensitivity and moderation are especially important.
• Very current cultural moments: where the context is changing quickly or public sentiment is still forming.
• Simple factual lookups: where a direct answer can be found from an existing source.
• Basic desk research: where the task is to gather and summarise publicly available information.
• Generic document summaries: where the need is only to condense a report, deck or transcript.
• One-off copywriting tasks: where there is no larger strategic question, evidence base or decision to simulate.

Absolutely not. Qlue activates research. It does not make it unnecessary or obviate the need for further research. Qlue amplifies & gives second life to all past & fresh primary work. 

• For new research briefs, Qlue can be used to sharpen the questions, add hypotheses from the past work. 
• Once the project is done, Qlue extends the life of the primary work by giving it a second life - & connect to the data library, help refresh consumer twins & compounds its value over time.

Not every Qlue project needs fresh primary work, but Qlue is designed to complement both qualitative and quantitative primary work when there is need for it.

A Qlue Digital Twin is a one-to-one or segment-level AI persona built from relevant, trusted source material. 

That source material may include interviews, focus groups, survey responses, behavioural data, consumer diaries or other research inputs. Qlue structures that material into a richer profile: motivations, behaviours, barriers, attitudes, category relationships, decision logic and contradictions.

The twin can then be used to explore how a real respondent or segment might react to a new question, idea, product, claim, pack, proposition or scenario.

Before twins are used on new questions, they're tested against outcomes we already know from the original research. Once calibration is complete, we maintain trust & transparency through data support & confidence scoring.

No - We have built Qlue to show its work and working.

Outputs are supported by evidence traceability, confidence notes and strategist review. Users can understand where an answer came from, what evidence supports it, what assumptions are being made, and where the system is less certain. Qlue is designed to make the reasoning visible to support strategic direction.

Qlue usually begins with a specific business question. The typical engagement flow is:

1. Share the question
You bring us a product, brand, market, audience or innovation question worth exploring.

2. Demo the approach
We show how Qlue would structure the problem, what inputs may be useful, and what kind of simulation is possible.

3. Run a pilot
A focused pilot is built around one meaningful question, using relevant source material and strategic review.

4. Move into an ongoing engagement
For teams that see value in the pilot, Qlue can become an ongoing decision simulation layer — helping the organisation build, query and compound its intelligence over time.

Qlue can also work with strategy partners across projects, repeatable products and custom panels.

The data does not have to be perfect. But the better and more relevant the source material, the richer the simulation. Qlue can ingest a variety of data formats & types. Depending on the problem we could use:

• Interview transcripts, focus groups, surveys
• Past research reports - brand tracking, Concept tests, category reports, etc.
• Market and retail data
• Cultural and social signals
• Internal strategy decks
• Product or innovation pipelines
• Stakeholder documents

Yes, but the role of Qlue changes depending on how much data exists.

With a rich archive, Qlue can build a deeper intelligence layer and more robust simulations. With limited data, Qlue can still help structure the problem, surface hypotheses, identify gaps and guide what fresh research may be needed.

When the evidence is thin, Qlue says so.

We currently work across global markets including the US, India and Australia.