NavigatorLab

The problem

Demand space work is some of the most useful strategy work a brand team can buy, and it usually arrives in the least useful format possible: a 2x2 on slide 38 of a consulting deck. Six months later the market has moved, the deck is stale, and nobody can interrogate the model behind it. The thinking was good. The artifact was dead on arrival.

We kept running into this on our own engagements. So we built the tool we wanted to use.

The build

NavigatorLab maps a market in three connected lenses. Consumer typologies describe who is buying. Consumption occasions describe when and why. Demand spaces sit at the intersection: a typology meeting an occasion, with a market size, a growth score, and a brand fit assessment attached.

The platform handles the full workflow: building and editing typologies and occasions, generating the demand space grid, sizing each space, scoring growth potential, and assessing which spaces fit the brand well enough to act on. AI assists with research synthesis and generates imagery that makes each space vivid enough to brief a creative team from. Finished maps share as live decks that stay current.

The workflow

The front end is Angular with D3 driving the interactive space visualisations. Gemini powers the AI research and image generation. Firebase handles auth and hosting, with PostgreSQL behind the data layer. The whole thing is built so a strategist can operate it end to end: every model assumption is visible and editable in the interface.

The outcome

Demand space mapping on our engagements went from a six-to-eight week deliverable to a working session. Clients keep access to the live map after the engagement ends, so the model gets updated as the market moves. It is also the clearest proof we can offer that we build the instruments we consult with.