Real-Time Multimodal AI
Engineer live and recorded video, audio, speech, computer vision, real-time communications, and operational data as one dependable pipeline.
- Real-time media processing
- Event understanding
- AI-assisted operational workflows
Production AI engineered for the real world.
Since 2017, OPTIME has engineered systems where latency, privacy, reliability, hardware, and infrastructure efficiency matter. We combine AI models with the applications, media pipelines, devices, networks, evaluation, security, and performance engineering required to make them dependable in production.
- Real-time media
- Edge and device AI
- Private and on-premises AI
- GPU and infrastructure cost
TRUSTED WITH SENSITIVE SYSTEMS
Confidentiality is part of the engineering.
Sensitive work stays sensitive. We protect customer identities, architectures, data, source code, and operational details throughout delivery. We publish only what the customer explicitly approves.
ENGINEERING CREDIBILITY
Engineering trust, measured over years.
Our engineering history spans telecommunications, broadcasting, public infrastructure, healthcare, storage, embedded systems, high-performance computing, and AI.
TelecommunicationsBroadcastingPublic infrastructureHealthcareStorageEmbedded systemsHigh-performance computingAITelecommunicationsBroadcastingPublic infrastructureHealthcareStorageEmbedded systemsHigh-performance computingAI
WHERE WE APPLY PRODUCTION AI
Production AI for environments where a model alone is not enough.
OPTIME engineers the complete path from live data and devices to models, infrastructure, and operational workflows.
Edge and Device AI
Deploy resource-aware AI across cameras, sensors, devices, embedded Linux, and intermittently connected environments - with local and offline inference when the cloud is not enough.
- Local and offline inference
- Secure device operation
- Edge-to-cloud orchestration
Private AI Infrastructure and Inference Optimization
Design controlled on-premises and hybrid environments, then optimize model serving, routing, memory, latency, throughput, and hardware utilization.
- Data and operational control
- Predictable infrastructure cost
- Better accelerator utilization
BEYOND THE MODEL
A model is only one layer of a production system.
A model, API, or RAG layer does not create a dependable production architecture by itself. Data, applications, media processing, networking, security, evaluation, infrastructure, and operational ownership must work together.
OPTIME engineers those layers as one system - so performance, cost, privacy, and reliability are design inputs rather than late-stage fixes.
- SourcesCameras, microphones, sensors, applications, and enterprise data
- TransportReal-time media, codecs, WebRTC, networking, and secure data movement
- InferenceEdge or on-premises runtimes, models, memory, and accelerators
- OperationsEvaluation, orchestration, observability, and secure workflows
ENGINEERING OUTCOMES
- Move an AI prototype into dependable production
- Use existing GPU capacity more effectively
- Keep sensitive workloads inside controlled environments
- Lower latency across media, models, and applications
- Operate inference on resource-constrained edge hardware
- Maintain evaluation, security, and operational control
CROSS-LAYER ENGINEERING DEPTH
From every byte to every token.
Since 2017, OPTIME has engineered streaming, communications, embedded, networking, and high-performance systems where every byte, millisecond, CPU cycle, and hardware constraint mattered. We apply that same discipline to model selection, tokens, memory, accelerators, latency, throughput, evaluation, energy use, and AI infrastructure cost.
One engineering team across models, compute, media, devices, networks, and infrastructure.
AI inference and model engineering
Architecture, evaluation, model serving, and runtime decisions
C++ and Rust systems engineering
Performance-sensitive applications and systems software
- View Accelerated Computing
GPU, HPC, and hardware optimization
Compute, memory, throughput, and infrastructure efficiency
- View Telecom Engineering
Video, audio, codecs, and WebRTC
Live media pipelines and real-time communications
Embedded Linux and edge devices
Constrained hardware, local operation, cameras, and sensors
Networking, security, and private infrastructure
Controlled data paths from device to data center
ENGINEERING PROOF
Selected systems that prove the discipline.
Evidence-aware examples spanning private AI, long-running broadcast platforms, and high-performance telecom systems. Customer identities remain confidential where disclosure is not appropriate.
Private AI · edge inferencePUBLIC TECHNICAL CASE
VaultAI - Private Enterprise AI and Knowledge Platform
A decentralized private-AI platform combining a customized llama.cpp/CUDA runtime, RAG, dynamic agents, secure WebRTC connectivity, and centrally managed edge inference.
Read Case Study
9-year platformPUBLIC TECHNICAL CASE
SignalBridge - Long-Lifecycle FPGA Broadcast Media Platform
A nine-year engineering partnership spanning web administration, Java and C/C++ platform software, embedded Linux, custom drivers, FPGA integration, firmware, and DSP-assisted media processing.
Read Case Study
~500 GbpsPUBLIC TECHNICAL CASE
MobileScope - Mobile-Core Traffic and GTP Analytics
A C/DPDK mobile-core processing layer validated at approximately 500 Gbps, decapsulating GTP-U into raw IP for downstream PacketLens analysis while separately processing GTP-C control traffic.
Read Case StudyWHY OPTIME
Systems engineering for AI that must remain dependable across system boundaries.
Operating since 2017
Established engineering discipline applied to long-running, mission-critical systems.
Complete-system perspective
Models, applications, media, devices, networks, infrastructure, and operations considered together.
Performance tied to cost
Latency, throughput, memory, compute, reliability, and infrastructure economics treated as connected requirements.
Long-term, confidential ownership
Specialist teams integrate with customer engineers and protect sensitive work throughout delivery.
CONTACT US
Building an AI system that has to work in the real world?
Talk to us about moving a prototype into production, reducing GPU or inference cost, deploying on-premises, integrating live video, audio, or devices, or meeting demanding latency, security, and reliability requirements.
Austin, Texas
Distributed engineering teams across North America, Europe, the Caucasus, and Latin America.
[email protected]