Built for the Inference Era
We started Latentforce because the hardest problem in production AI is not training — it is serving. We fix that.
Our Story
Latentforce was founded in 2023 by a team of infrastructure engineers who had spent years watching capable AI models fail in production — not because the models were bad, but because the serving layer was broken. Latency spikes, unpredictable costs, and scaling failures were killing AI products before they ever reached users.
We built Latentforce to be the inference infrastructure that teams needed but couldn't find: purpose-built for large models, obsessively optimized for latency, and designed to make enterprise deployment feel like a solved problem rather than a permanent engineering headache.
Today, Latentforce serves inference workloads across fintech, healthcare, and enterprise SaaS — handling billions of tokens per month with the reliability and cost profile that production AI demands.
Make AI Inference a Commodity
The AI stack is being democratized at every layer — from foundation models to fine-tuning to evaluation. Inference is the last bottleneck. Our mission is to remove it entirely: make fast, cheap, reliable model serving available to every engineering team, regardless of scale.
We believe the companies that win the AI era will be the ones that can iterate fastest. Iteration speed depends on inference speed. That is why we build this infrastructure.
Inference-First, Always
Latentforce invests its engineering entirely in the inference layer — model serving, latency optimization, quantization pipelines, autoscaling, and observability. We do not build training infrastructure, data pipelines, or application-layer tooling.
Our customers range from early-stage AI teams deploying their first production model to enterprise ML platform organizations running hundreds of model endpoints at scale.
Principles That Drive Us
Performance First
Every architectural decision at Latentforce starts with a latency budget. We optimize obsessively and measure everything.
Radical Transparency
We publish our benchmark methodology, pricing structure, and SLA definitions openly. No hidden limitations, no surprise invoices.
Customer Obsession
Our roadmap is driven by customer infrastructure problems, not internal assumptions. We listen before we build.
Security Without Compromise
Enterprise-grade security is not an add-on tier for us. Every deployment ships with end-to-end encryption and audit logging by default.
Our Investors
We are proud to be backed by investors who understand the infrastructure layer of AI and the long-term opportunity in inference.
Seed Round · March 2025
This funding accelerates our core inference engine development and enterprise sales motion across North America.
Infrastructure Engineers, Through and Through
Our team has built production ML systems at scale. We know inference from the inside.