As AI moves into real products, one limitation has become unavoidable: context doesn’t persist. Most systems forget everything once a session ends. Developers rebuild state manually. Workflows fracture. Every model swap risks losing history.
That’s not an accidental inconvenience. It’s a fundamental gap in how AI systems are built today.
Backboard is tackling that gap with a memory-centric infrastructure layer that makes stateful AI reliable from prototype to production.
Insight Came from Doing the Hard Work
Rob Imbeault has spent decades building enterprise software where continuity, compliance, and operational reliability matter. He saw the same patterns emerging as teams began adopting large language models: models can generate, but they can’t remember reliably across sessions or workflows.
If AI applications are going to be long-lived, memory can’t be an afterthought.
The Solution: Configurable Platform for Stateful AI
What makes Backboard non-obvious is how they’ve integrated memory into a configurable platform for stateful AI.
Backboard isn’t just a vector store or a retrieval engine. It’s a unified API that lets developers:
- Store and retrieve context reliably across conversations and workflows
- Run memory alongside thousands of large language models, swapping providers without rewriting code
- Build and tune a complete stack with embeddings, RAG, stateful threads and model routing in one place
- Persist long–term knowledge and recall the right pieces at the right time
Memory becomes architecture, not a hack. Backboard’s benchmark results demonstrate high recall and retrieval accuracy on long-context benchmarks — a practical signal of robustness for real workflows.
Why Now
Several macro shifts make this the right time:
- Technology: The maturation of LLMs.
- Behavior: Growing comfort with tech in classrooms.
- Regulation: Ever-changing curriculums that demand agility.
TeachAid isn’t just riding the wave—it’s architecting the future of instructional design.
Why N49P Invested
First, the problem is structural. Teams building beyond simple bots don’t want to glue together half a dozen tools to get stateful behavior, and they shouldn’t have to. Durable memory needs to be accessible and reliable across models and use cases.
Second, the team is uniquely positioned. Rob and Jonathan have built and sold production infrastructure before. They know what developers need, and they’ve aligned the product around real engineering burdens, not academic benchmarks.
Third, the timing is right. Developers are being asked to assemble increasingly complex stacks — models, embeddings, vector databases, RAG, agents — without a coherent integration layer. Backboard gives teams a single, consistent API with memory and routing built in. That’s the foundation you build scalable systems on.
N49P x Backboard
Backboard is early — there’s real execution ahead in trust, performance, and ecosystem adoption. Built-in routing across more than 2,200 large language models and a configurable stack are strong starting points, but developer success will come from predictable, reliable state across real workflows.
We believe Backboard is building toward a future where AI systems don’t just compute — they remember intelligently, across sessions, models, and products. That’s not a nice-to-have. It’s how serious AI will be built.
N49P is co-leading a pre-seed round in Backboard with Mistral Ventures and participation from Garage Capital and look forward to supporting Rob, Jonathan, and the team as they make memory a first-class primitive in the AI stack. If you want to shape the future of AI checkout job opportunities here. Alternatively you can start using Backboard here.
