ForcePlatforms, Inc.

The voice interface is the final frontier of human-computer interaction. Not because it's new—but because we've never gotten it right.

For decades, voice systems have been an afterthought. IVR menus that trap you in loops. Assistants that forget your name mid-sentence. AI that sounds human but thinks like a form.

The gap isn't intelligence. Modern language models can reason, synthesize, and respond with remarkable depth. The gap is latency. The gap is memory. The gap is the engineering between the model and the moment.

300 milliseconds. That's the threshold where conversation breaks. Where the brain registers silence as disconnect. Where trust evaporates and users hang up. Most voice AI systems operate in seconds. Humans think in fractions of one.

Voice AI doesn't have a model problem. It has an infrastructure problem.

When a caller says "Actually, make that Tuesday instead," the system needs to retrieve context from three turns ago, understand temporal reference, update state, and respond—all before the pause becomes awkward. Today's systems can't do this. They restart. They hallucinate. They ask you to repeat yourself.

Memory architectures weren't designed for real-time. Retrieval systems optimize for accuracy, not speed. The entire stack assumes you have time to think. Voice doesn't give you that luxury.

ForcePlatforms is a research lab solving this.

We build low-latency voice calling agents. Not demos. Not prototypes. Production systems that handle real calls, at real scale, with real consequences for failure.

Our approach is different: we treat memory as the core infrastructure layer, not a feature. Voice agents that remember aren't just more helpful—they're faster. Context retrieval that takes 200ms is useless in a 300ms window. We've rebuilt the stack to make memory instant.

The architecture:

• Chunk-based ingestion with atomic memory extraction — every voice session is decomposed into semantic blocks. Ambiguous references are resolved at write-time, not query-time. When a caller says "my usual order," the system already knows what that means.
• Relational versioning — facts evolve. Preferences change. Our memory graph tracks updates, extensions, and corrections as first-class operations. The agent doesn't just recall—it understands what's current.
• Dual-layer temporal grounding — distinguishing when the conversation happened from when described events occurred. "Last Tuesday" means something different depending on when you said it. Voice agents that schedule, remind, and follow up need this precision.
• Sub-100ms hybrid retrieval — semantic search on atomic memories with source chunk injection. Not eventually consistent. Not good enough for batch. Fast enough for the conversational window where humans decide to trust or abandon.

The vision is simple.

Voice should be the most natural way to interact with any system. Not because it's convenient—because it's human. The phone call will outlive the chatbot. The voice memo will outlive the text thread. Speaking is how we've communicated for 200,000 years. Typing is a workaround.

We're building the infrastructure for AI that listens the way humans do: with memory, with context, with the patience to wait and the speed to respond when it matters.

The companies that get voice right will own the next interface layer. We intend to be the ones who make that possible.