Case study: Hypermode + Food Forest Ai

The highly interconnected world of food and beverage supply chains demands the ability to rapidly identify and analyze relationships between producers, certifications, regions, and logistics. Yet traditional search systems often fall short, relying on rigid filters and siloed data rather than surfacing the full complexity of these networks.

Food Forest Ai was created to address this gap. Their mission is to enable a more decentralized, localized and regional food systems to prosper globally by creating the foundational search layer of the food and beverage ecosystem. This highly personalized search engine tailors to the needs of supply chain professionals. By combining graph-native data infrastructure with natural language interfaces and built-in collaboration tools, Food Forest streamlines how supply chain teams discover and manage critical information.

But building such a system from scratch, especially with a small engineering team, posed significant challenges. Food Forest needed more than just a database — they needed infrastructure that could scale with their ambition. They found the missing layer in Hypermode: a graph-native, AI-ready platform that enabled them to launch 12 to 18 months ahead of schedule.

The challenge

The food and beverage supply chain is inherently relational. Finding a suitable supply chain partner, for example, might require evaluating location, certifications, processing capacity, logistics compatibility, and customer reviews — all of which are connected but often stored in different systems.

In addition to the technical challenge of modeling and querying these relationships, the industry also suffers from a high degree of semantic ambiguity. Varied terminology across regions, specialties, and business types makes searching for specific services and products particularly difficult. The same process or service might be described differently depending on whether the user is in procurement, logistics, or manufacturing. As a result, even well-structured data can be difficult to search or analyze without a system that understands the nuances of industry language.

Avery-Dante Hinds, co-founder and Chief Product Officer at Food Forest, recognized early that a traditional relational or document-based database would not offer the flexibility needed to represent these complex relationships. They needed a graph structure as the foundation of their system to model entities like ingredients, manufacturers, logistics partners, and certifications as nodes and define their interactions via edges. Furthermore, Avery wanted a dynamic, queryable representation of their domain that was flexible enough to evolve with their strategy.

We knew graph databases were built for mapping and traversing relationships which is perfect for this deeply connected industry and for building AI-driven solutions. We had always envisioned incorporating natural language and universal search into our search engine.

While their choice of database architecture was settled, the team struggled to operationalize it into a user-facing search engine. They had a schema, but lacked the orchestration layer to connect it to a natural language interface. The uncertainty around how to scale the system, iterate quickly, and support diverse user queries not only slowed product development but also limited their ability to validate ideas with real users.

Searching for an AI-driven solution

Food Forest prioritized innovative technologies with robust community support to ensure they could drive growth and integrate seamlessly with their needs.

The team explored multiple options. Neo4j was considered but didn’t align well with their chosen stack. OpenAI’s APIs showed potential for smart search and summarization, but introduced additional engineering tasks such as context handling, prompt chaining, and model integration that would divert resources from core product development.

What differentiated Hypermode was its holistic approach to building AI-powered apps. Rather than focusing exclusively on LLM hosting or vector search, Hypermode provided an integrated infrastructure stack, purpose-built for teams working with complex relational data.

We chose Hypermode for its developer-centered approach and seamless integration. Our existing Dgraph setup, with its vibrant community, flexible query options, and rapid cluster deployment made the process effortless. Combined with Next.js and Vercel in our tech stack, Hypermode’s leadership and vision clearly set it apart.

Two core components stood out for Food Forest:

1. Hosted Dgraph clusters: Hypermode provided scalable, production-ready graph environments that integrated directly with Food Forest’s schema and queries. This eliminated the burden of managing backend infrastructure while preserving full flexibility.
2. Modus framework: This open-source framework allowed Food Forest to enable natural language search features. Users can search however they like, with built-in messaging and project organization tools. Modus enabled Food Forest to streamline the search process by integrating a language model with vector search capabilities to handle semantic similarities in the data being searched, since traditional search mechanisms struggled with the industry's varied terminology.

Food Forest’s onboarding process with Hypermode was hands-on and iterative. From the outset, Hypermode’s engineers worked closely with Food Forest’s team to understand their schema, product roadmap, and key user stories.

Together, the teams conducted schema reviews, explored search flows, and constructed data diagrams that clarified how queries would be interpreted by Modus. Hypermode’s deployment mechanisms based on GitHub actions allowed the team to rapidly iterate.

While there were initial challenges—such as coordinating across time zones and addressing early access issues—Hypermode provided continuous support. Bug reports were prioritized, feedback was rapidly integrated into the development process, and documentation was improved in real time.

This collaborative model enabled Food Forest’s team to stay focused on product development, rather than platform maintenance or backend orchestration. Just as importantly, the teams shared a common product philosophy: that user-facing intelligence must be grounded in structured, explainable data — not just generic AI responses.

The results

The impact of the partnership was immediate and significant. Prior to adopting Hypermode, Food Forest estimated it would take at least another 12-18 months to ship a functional product. With Hypermode’s infrastructure in place, that timeline was reduced dramatically.

In a matter of weeks, Food Forest had a working prototype. Within a few months, they were engaging early users, collecting valuable feedback, and refining their UX based on real-world interactions.

User response exceeded expectations. More than 100 users joined the waitlist, and early testers consistently described the product as intuitive and remarkably capable for such a complex domain.

Internally, the benefits were equally notable. The engineering team experienced greater velocity, the design team gained the freedom to experiment, and the overall team morale improved as features moved from concept to reality far faster than anticipated. Within the context of this accelerated build process, Food Forest quantifies the gains specifically in the domain of search functionality.

Two key metrics illustrated the magnitude of improvement. First, the core search feature cycle time was dramatically reduced. Without Hypermode, Food Forest estimated that developing their universal search API—spanning specification, development, testing, and production—would have required approximately 16 weeks, based on past projects and internal benchmarks. By leveraging Hypermode’s Modus API, along with hosted graph infrastructure and models, the same feature set was shipped end-to-end in just 4 weeks. This represented a 75% reduction in time-to-market for search capabilities.

Second, their search API release cadence significantly increased. Prior to integrating Modus, Food Forest was averaging one search-related production release per month. After integration, that cadence accelerated to approximately one release per week, including quality assurance, testing, and iteration. In effect, they achieved a 4× increase in deployment frequency for search-related enhancements.

These metrics, drawn directly from Food Forest’s sprint planning, deployment records, and commit logs, demonstrate how Hypermode enabled the team to focus their efforts on UX/UI iteration, rather than dedicating extensive cycles to reinventing backend logic or building AI infrastructure from the ground up.

What used to take months can now be done in weeks or even days. It's simple to use, easy to learn, and supports multiple languages. [Our customers] are consistently amazed by the experience, each interaction leaves them pleasantly surprised.

Co-founder and Chief Product Officer Avery-Dante Hinds described the experience as “having a second dev team dedicated to all things Hypermode.”

A foundation for future innovation

While search remains the core feature, Food Forest leverages Hypermode to build new capabilities. With access to the Modus API and hosted models, they’re developing a graph-powered recommendation engine designed to suggest suppliers, workflows, and optimizations based on real-time user context and historical patterns.

We discovered unexpected benefits when using Hypermode. We've been able to explore cutting-edge product features earlier than anticipated and have uncovered innovative ways to leverage LLMs alongside our graph database.

They are also enhancing their natural language capabilities. As queries become more complex such as involving temporal reasoning, multi-entity comparisons, and operational constraints, Modus enables them to respond with structured intelligence, not just free-text summaries.

Furthermore Food Forest is discovering entirely new interaction paradigms. They are experimenting with contextual autocomplete, smart prompts, and in-app guidance that combines LLM reasoning with schema-aware retrieval. These features would have been prohibitively difficult to prototype without a platform like Hypermode.

In short, what began as a search engine is evolving into a comprehensive decision-support system — one that reflects the depth and dynamism of the supply chain itself.

A new model for AI-native product development

Food Forest’s journey highlights what’s possible when a product team has a strong vision, a flexible graph backend, and an infrastructure partner that enables rapid innovation. Together, Food Forest and Hypermode have shown that small teams can build highly intelligent, domain-specific systems — without compromising on performance, usability, or speed to market.

I wish I had known sooner just how creative you can get with this, diving into the details to uncover what our audience really needs and then leveraging Hypermode to deliver.

Their advice to other teams?

• Don’t wait to bring AI into your core product — especially if your data has rich relationships
• Don’t try to build everything from scratch — tools like Hypermode exist to accelerate you
• Don’t settle for chatbot interfaces when what your users want is structured, explainable intelligence

Hypermode gave them not just infrastructure, but a way of thinking about product development that is faster, more modular, and infinitely more flexible.