The confidence layer
for real-time AI
interaction.
LogitScore builds confidence-aware AI systems for low-latency, real-world interaction — starting with an on-prem multimodal copilot that knows when to answer, when to ask, and when to verify.
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Most AI systems don't know
what they don't know.
Real-time AI interaction is fundamentally different from offline chat. Latency, uncertainty, and noisy outputs compound into systems that are unreliable when it counts most.
Uncalibrated confidence
Language models produce fluent-sounding outputs regardless of whether they are certain or guessing. Nothing in the standard pipeline prevents high-confidence delivery of incorrect information.
Latency in real-world settings
Cloud-dependent models introduce unpredictable round-trip delays. Sensitive or edge deployments require local, low-latency inference that can respond within human perceptual windows.
Noisy, unintelligent verbosity
Assistants trained to maximize helpfulness tend to over-answer — generating verbose responses when a clarifying question or silence would serve the user better. Noise erodes trust faster than silence.
No adaptive behavior policy
A useful real-time system needs to adapt: answer when certain, ask when unclear, verify when the stakes are high. Without an explicit confidence-driven policy layer, all interactions look the same.
"The failure mode isn't that AI answers wrongly. It's that it answers confidently regardless — and you can't tell the difference until it's already too late."
This is the core problem LogitScore is engineered to solve. By reading internal model signals — token-level logits and probability distributions — we give the system a real-time picture of its own certainty, and use that to decide what to do next.
A quantized, confidence-aware
AI runtime — on your hardware.
LogitScore is an on-prem AI copilot built for real-time interaction. It runs fully locally using an optimized LLM + VLM pipeline with streaming TTS, and adapts its behavior based on how certain it is about each response.
On-prem deployment
Runs entirely on your own hardware. No cloud dependency, no data egress, no third-party inference. Compatible with modern edge servers, workstations, and enterprise on-prem infrastructure.
Quantized LLM + VLM pipeline
Uses carefully quantized language and vision-language models optimized for real-time throughput. Inference is engineered for low first-token latency and sustained streaming performance.
Streaming TTS output
Speech output begins streaming before the full response is generated, keeping interaction feel natural and real-time. Designed for voice-forward use cases where latency perception matters.
Logit-based confidence estimation
Reads token-level probability distributions and model logits in real time to estimate how confident the system is. This is not post-hoc scoring — it happens inline during inference, at the signal level.
Adaptive interaction policy
A decision layer translates confidence signals into behavior: answer directly when certain, ask clarifying questions when ambiguous, invoke verification tools when confidence is too low to commit.
Multimodal perception
Processes text and visual inputs through a unified local pipeline. Designed to handle real-world interaction contexts that go beyond pure text: documents, screens, live video frames, and more.
Behavior that tracks certainty,
not just tokens.
LogitScore continuously monitors its own confidence during inference. Depending on what the signal says, it takes one of three actions — and nothing in between.
Answer confidently.
When logit distributions are tight and the model's internal probability mass is concentrated, the system commits to a direct, useful response — delivered with minimal hesitation.
Output: direct speech or text response
Ask a clarifying question.
When probability distributions are broad or the input is ambiguous, the system recognizes that proceeding would likely produce noise. Instead, it surfaces a targeted, minimal clarifying question.
Output: clarifying question, not a guess
Verify with context or tools.
When confidence is too low to commit, the system invokes additional reasoning steps, retrieval, or tool calls before producing output. It refuses to guess when the cost of being wrong is too high.
Output: retrieval, tool call, or deferral
Conventional assistant vs. LogitScore
Conventional assistant
Always answers.
Outputs a fluent response regardless of internal uncertainty. Confidence is implicit and unobservable. Hallucination risk is constant and unpredictable.
LogitScore
Adapts based on certainty.
Answers, asks, or verifies — based on real-time signal from the model itself. Confidence is explicit, observable, and directly drives interaction behavior.
How it works
A tight local pipeline from raw input to calibrated, confident output. Every component is optimized for latency and designed to surface uncertainty before it becomes noise.
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Why LogitScore
Built differently from the ground up — for the constraints and demands of real production environments.
Scientist-built
Designed by AI researchers who understand inference internals, not just API wrappers. The confidence layer is founded on principled probabilistic reasoning, not heuristics or post-hoc classification.
On-prem and privacy-preserving
No data leaves your environment. No cloud APIs, no telemetry by default. Designed for organizations where data sovereignty and operational independence are non-negotiable.
Low-latency real-time interaction
Optimized inference stack targeting sub-500ms first-token latency on modern hardware. Built for interaction loops where humans are waiting, not for batch processing workloads.
Confidence-native behavior
Confidence is not a feature layered on top — it is the mechanism that governs behavior at every interaction. The system cannot decouple its decisions from its uncertainty estimates.
Built for live use
Not designed for asynchronous document tasks or single-shot queries. Purpose-built for continuous, live interaction contexts — voice-first, real-time, ambient, and high-frequency use.
Extensible by design
LogitScore is the first product of a broader deep-tech platform. The confidence runtime, model stack, and decision layer are built to evolve toward more general multimodal systems and runtimes.
Designed for high-trust
interaction environments.
LogitScore is best suited for contexts where confidence, accuracy, and real-time performance are not optional — and where a hallucinating assistant is a liability, not an inconvenience.
On-prem copilot for sensitive environments
Deploy a capable, privacy-preserving AI assistant in environments where cloud connectivity is restricted, data classification is strict, or regulatory constraints prohibit external model calls.
Real-time multimodal assistant
A voice-first, multimodal assistant that perceives text and visual input simultaneously, adapts its interaction to context, and responds within human perceptual latency windows.
Confidence-aware operator support
Assist technical operators, analysts, or domain experts in real time — where wrong answers carry operational cost and the system must recognize the limits of its own certainty before acting.
Edge and local interaction systems
Deploy on disconnected or bandwidth-constrained systems — factory floors, field devices, embedded infrastructure — where inference must be fully local and reliably responsive.
When AI must be right,
not just fast.
LogitScore is designed for environments where a confident wrong answer is worse than no answer at all. Where trust is earned through consistent, calibrated behavior — not fluency or volume.
See LogitScore in action.
We're working with a limited number of early partners and design customers. Tell us about your environment and use case, and we'll reach out to arrange a live demonstration.
Get in touch.
Questions about the product, a potential collaboration, or just want to connect? We're a small team — your message reaches us directly.
contact@logitscore.com