NextFood.ai: turning the creative brief into a useful first concept.
We built the guided AI ideation journey behind NextFood. Food and beverage marketers bring a brief and campaign strategy; the platform uses current trend context to generate concepts, then lets the team explore the tactics and rationale behind each idea.
- Client
- NextFood.ai
- Sector
- Food & beverage innovation
- What we built
- A custom AI ideation platform for food and beverage teams
- Headline outcome
- 60% shorter time to first draft ideas
More generated ideas were not necessarily better ideas.
Marketing teams needed current culinary and consumer trends, but dense trend modules created information overload. Generated concepts could miss the selected strategy, and too many presets made the product harder for non specialists to use.
NextFood needed a clearer route through its six step journey: enough context to generate useful, brand aligned ideas, with enough detail to act on them. The engineering and product design had to keep that flow useful as data and usage grew.
A guided route from strategy to idea detail.
Fresh trend context
A trend pipeline combines current sources with caching, bringing relevant marketing context into the ideation flow.
Strategy aware generation
The generation engine adapts to selected creative strategies such as awareness, positioning and conversion, keeping the output tied to the brief.
Progressive insight and idea views
High level trends appear first, with deeper context available when useful. Idea screens lead with campaign ready highlights and let users expand into tactics, formats and rationale.
Guided onboarding and targeted presets
Walkthroughs and role relevant presets help strategists and marketers complete the journey without needing to understand the underlying AI tooling.
Keep the depth without overwhelming the person using it.
Context at the right step
Trend information and strategy choices feed generation rather than sitting in disconnected research views.
A responsive product foundation
Caching and scalable services support generation and the handling of growing trend data.
Feedback driven refinement
Prototypes, stakeholder reviews, code review and automated checks supported refinement of the product journey.
Product design and AI engineering in the same loop.
We worked through the journey from brief input to detailed concept review, using stakeholder sessions, prototypes and sprint feedback to balance depth with usability.
- 01
Define the creative journey
Map the brief, strategy choices, trend exploration and idea outputs.
- 02
Connect intelligence and interface
Build the trend pipeline, generation engine and progressive output screens together.
- 03
Refine the path to first value
Introduce guided onboarding and clearer presets, then iterate on the experience.
A faster route to a workable first draft.
reduction in average time to first draft ideas
from brief to concept in the project account
of users rated ideas ready for first drafts
These are project reported results from the supplied case study. A measurement period and sample size were not provided.
A few useful
questions.
Is this an open ended AI chat experience?
The product guides users through strategy, trend context and concept exploration, with output shaped around the selected creative brief.
What did the engagement cover?
The end to end product journey, trend intelligence, strategy aligned generation, idea detail views and onboarding, alongside the engineering foundation.
Building an owned, bilingual platform for scheduling, service reports, invoicing and customer visibility.
Read the case study
Building Frontdoor for transaction records, commission workflows and financial reporting.
Read the case studyWhere does a promising AI output still need too much work to become useful?
From the first proof to a working implementation. We’ll explore the workflow, the systems involved and a practical first build.