Multi-Model AI + Enterprise and Financial Workflows
Combine documents, OCR, layout understanding, language models, retrieval, classification, rules, verification, and human approval into controlled operational systems.
CROSS-LAYER ENGINEERING EXPERTISE
Expertise from AI models to hardware, networks, and production systems.
OPTIME combines AI engineering with C/C++ and Rust systems development, accelerated computing, embedded Linux, telecom, video and audio, networking, security, applications, and high-performance infrastructure.
We engineer the layers between a model and a dependable production system - from data and applications to inference runtimes, accelerators, devices, protocols, infrastructure, evaluation, and operations.
One engineering team across models, compute, applications, media, devices, networks, and infrastructure.
ONE CONNECTED ENGINEERING SYSTEM
Models: Language, vision, speech, audio, and document intelligence
- Models: Language, vision, speech, audio, and document intelligence
- Systems: Applications, data, evaluation, routing, and secure workflows
- Compute: Runtimes, accelerators, devices, networks, and infrastructure
- Operations: Observability, reliability, privacy, performance, and cost
PRODUCTION HISTORY
Expertise built in production.
Today’s AI work extends an engineering history shaped by real hardware, live media, devices, telecom networks, and systems that had to remain operable after launch.
- 2012
Hardware-accelerated media efficiency
Before OPTIME was founded, its technical leadership developed Intel GPU-accelerated Full HD transcoding focused on stream density, CPU use, power, and hardware cost.
- 2017+
OPTIME systems engineering
Telecom, embedded, media, networking, infrastructure, and performance-sensitive production systems.
- 2018
Machine learning in telecom and IPTV
Customer-behavior analysis across internet and IPTV services.
- 2021-22
Edge-AI acceleration
Specialist engagements connecting models to constrained devices and hardware-aware execution.
- Today
Production AI
Private AI, multimodal systems, optimized inference, accelerated computing, evaluation, and production operations.
THE COMPLETE PRODUCTION STACK
Our work begins where a model API ends.
A basic implementation may connect OCR, a general-purpose model, and a web interface. Production systems often require specialized models, deterministic logic, secure applications, hardware-aware inference, evaluation, observability, and operational control to work as one architecture.
Multimodal systems combine text, documents, images, video, speech, audio, or operational data. Multi-model systems coordinate several specialized models through routing, cascades, verification, and deterministic logic.
Business applications and operational workflows
Interfaces, decisions, review paths, automation, and the people responsible for outcomes.
Data, documents, knowledge, and integrations
Structured and unstructured information, enterprise systems, retrieval sources, and governed data access.
Language, vision, speech, audio, and multimodal models
The specialized models required for text, documents, images, video, voice, audio, and operational data.
Model routing, retrieval, evaluation, and orchestration
Multi-model selection, cascades, confidence checks, deterministic rules, verification, and human review.
Inference runtimes and performance optimization
Serving, batching, caching, quantization, memory management, profiling, latency, and throughput.
CPU, GPU, NPU, FPGA, edge, and private infrastructure
Target-aware deployment across heterogeneous compute, constrained devices, private environments, and data centers.
Security, networking, observability, and operations
Controlled data paths, identity and access, monitoring, availability, auditability, and operational ownership.
Not every system needs every layer. OPTIME engineers and optimizes the layers the production environment actually requires.
AI ACROSS INDUSTRIES
The engineering approach is cross-industry.
Production constraints differ by industry, but the underlying challenge is similar: combine the correct models, data, applications, infrastructure, security boundaries, and operational controls into one dependable system.The same architecture and optimization disciplines apply whether the system analyzes financial documents, supports regulated enterprise workflows, processes live video, operates on edge hardware, or assists telecom and media operations.
01 / 06
Financial services, banking, and insurance
- Private and on-premises AI
- Document and transaction intelligence
- Fraud, risk, compliance, and operational workflows
- Controlled data access
- Auditable model-assisted decisions
SIX PRINCIPAL EXPERTISE DOMAINS
Depth across the layers production systems depend on.
These domains remain valuable independently. Their greatest advantage appears when a difficult system crosses several of them at once.
01 / 06
AI Systems and Optimized Inference
Engineer language, vision, speech, document, and multimodal AI as evaluated, optimized production systems rather than isolated model integrations.
Production evidence includes telecom and IPTV customer-behavior machine learning from 2018, public-safety computer vision, intelligent broadcast workflows, edge-AI acceleration, and current private and multimodal AI systems.
- LLMs and SLMs
- Vision-language and multimodal models
- Computer vision
- Speech recognition, transcription, translation, and audio AI
- OCR and document-layout processing
- Model selection, benchmarking, fine-tuning, and domain adaptation
- Quantization, distillation, and compression
- Multi-model routing, cascades, and confidence scoring
- Retrieval, knowledge integration, bounded agents, and RAG
- Batching, caching, memory optimization, and inference serving
- Private, on-premises, hybrid, and edge deployment
- Production evaluation, monitoring, and human review
RARE ENGINEERING COMBINATIONS
The difficult systems sit between disciplines.
OPTIME’s differentiation is the ability to connect disciplines that are often split across model, application, infrastructure, device, media, and operations teams.
AI + Accelerated Computing
Optimize models and pipelines across CPUs, GPUs, NPUs, and FPGAs for latency, throughput, memory, capacity, power, and infrastructure cost.
AI + Video, Audio, and Broadcasting
Combine vision, speech, multimodal models, codecs, streaming, WebRTC, OTT, set-top-box, and broadcast workflows.
AI + Telecom and Private Networks
Apply AI architecture to communications intelligence, traffic analytics, anomaly detection, capacity planning, voice systems, and private mobile infrastructure.
AI + Embedded and Edge Devices
Deploy secure, resource-aware inference on cameras, sensors, gateways, appliances, vehicles, and intermittently connected devices.
AI + Security and Private Infrastructure
Build on-premises and hybrid AI with controlled data paths, access policies, auditability, private networking, evaluation, and operational ownership.
TECHNOLOGY STACK
Core technologies first. Supporting tools in context.
Technologies are grouped by the role they play in production systems, with core engineering capabilities shown first.
JUMP TO AN EXPERTISE AREA
Core Technologies
Languages and systems programming
Systems, application, and model-platform engineering across native performance paths, services, tooling, and product interfaces.
- C
- C++
- Rust
- Go
- Python
- Assembly
- JavaScript
- TypeScript
AI Models and Architectures
Model architectures engineered for production use
Select, adapt, combine, evaluate, and optimize model architectures for production workloads.
- LLMs
- SLMs
- Vision-language models
- Multimodal models
- Dense transformers
- Mixture-of-Experts (MoE) models
- Embedding and reranking models
- Classification models
- Speech and audio models
- OCR and document-layout models
- Computer-vision models
- LoRA and adapters
- Quantization
- Distillation
- Pruning
- Speculative decoding
- KV-cache optimization
- Continuous batching
- Paged attention
- Tensor parallelism
- Pipeline parallelism
- Data parallelism
- Context parallelism
- Expert parallelism
AI Inference and Serving
Serving paths matched to scale, latency, and deployment boundaries
Engineer distributed serving, native runtimes, and edge inference around throughput, memory, hardware, privacy, and operational requirements.
High-throughput and distributed serving
- vLLM
- SGLang
- TensorRT-LLM
- NVIDIA Triton Inference Server
Local, edge, and native inference
- llama.cpp
- Ollama
- CTranslate2
- ONNX Runtime
- TensorRT
- OpenVINO
- OpenVINO GenAI
GPU and Heterogeneous Computing
Accelerated computing across vendor and hardware boundaries
Optimize inference and data pipelines for the processor, memory hierarchy, capacity, latency, and deployment environment they actually run on.
NVIDIA
- CUDA
- cuDNN
- TensorRT
- TensorRT-LLM
- NVIDIA Triton Inference Server
- NVIDIA DeepStream SDK
- Jetson
- NVENC
- NVDEC
- NVIDIA GPU Operator
AMD
- ROCm
- HIP
- MIGraphX
- AMD GPU Operator
- AMD Instinct
Intel
- oneAPI
- SYCL / DPC++
- oneDNN
- OpenVINO
- OpenVINO GenAI
- Intel CPU, GPU, and NPU inference
Cross-platform and specialized
- ONNX Runtime
- OpenCL
- Vulkan Compute
- FPGA
- SIMD
- AVX/AVX2/AVX-512
- ARM NEON
- Zero-copy processing
- Hardware-specific optimization
Production Platforms and Systems
Retrieval, vector search, and knowledge systems
Custom vector-search, hybrid-retrieval, indexing, reranking, caching, and knowledge-retrieval pipelines designed around the application’s accuracy, latency, scale, and data-control requirements.
- FAISS
- Qdrant
- Milvus
- pgvector
- PostgreSQL
- Redis Vector Search
- Dense retrieval
- Sparse retrieval
- Hybrid search
- Reranking
- Metadata filtering
- Multi-vector retrieval
- Multimodal retrieval
- Semantic caching
- Customized indexing and retrieval pipelines
On-premises AI and platform engineering
Operate controlled AI platforms with private serving, accelerator scheduling, resilient deployment, monitoring, and recovery practices.
- Docker
- NVIDIA GPU Operator
- AMD GPU Operator
- GPU scheduling
- Private model serving
- Model registries
- Private container registries
- Air-gapped deployment
- High availability
- Cluster monitoring
- Capacity monitoring
- Infrastructure as code
- Backup and disaster recovery
Private cloud, virtualization, and storage
- OpenStack
- KVM
- QEMU
- libvirt
- KubeVirt
- Proxmox VE
- Ceph
- MinIO
- Open vSwitch
- OVN
- Kubernetes
- Helm
- containerd
Security and identity
Protect model, data, application, and infrastructure boundaries with explicit identity, access, encryption, audit, and network controls.
- HashiCorp Vault
- OAuth 2.0
- OpenID Connect
- mTLS
- PKI and certificate management
- Secrets management
- Encryption
- RBAC
- ABAC
- Audit logging
- Network segmentation
- Air-gapped infrastructure
Messaging and distributed systems
Connect real-time services, devices, applications, and model pipelines with appropriate messaging and typed service interfaces.
- Redis
- Redis Streams
- NATS
- RabbitMQ
- AMQP
- Apache Kafka
- ZeroMQ
- MQTT
- gRPC
- WebSockets
- REST
- Protobuf
Media, communications, and computer vision
Build low-latency video, audio, communications, broadcast, and vision pipelines from capture and transport through processing and inference.
Frameworks and processing
- GStreamer
- NVIDIA DeepStream SDK
- OpenCV
- FFmpeg
- libWebRTC
- mediasoup
- Kurento
Video codecs - established
- MPEG-2 Video
- H.263
- H.264 / AVC
- H.265 / HEVC
- VP8
- VP9
Video codecs - modern and next-generation
- AV1
- AV2
- H.266 / VVC
- MPEG-5 EVC
- MPEG-5 LCEVC
- AVS2
- AVS3
Professional and intraframe codecs
- JPEG
- MJPEG
- JPEG 2000
- ProRes
- DNxHD / DNxHR
Audio codecs
- Opus
- AAC
- HE-AAC
- AC-3
- E-AC-3
- MP3
- Vorbis
- FLAC
- G.711
- G.722
- G.729
Media containers, transport, and delivery
- MPEG-TS
- MP4 / ISOBMFF
- CMAF
- Matroska
- WebM
- HLS
- MPEG-DASH
- RTP
- RTSP
- RTMP
- SRT
- RIST
- WebRTC
- SDI
- ASI
- SMPTE ST 2110
- SMPTE 2022-6
Hardware-accelerated media
Includes hardware paths used by NVIDIA DeepStream SDK and the media frameworks listed above.
NVIDIA
- NVIDIA Video Codec SDK
- NVENC
- NVDEC
- CUDA media processing
Intel
- Intel Quick Sync Video
- Intel VPL
- VA-API
- Intel GPU media acceleration
AMD
- AMD AMF
- AMD VCN
- ROCm / HIP media integration
Cross-platform
- Vulkan Video
- OpenCL
- FFmpeg hardware acceleration
- GStreamer hardware acceleration
- Zero-copy CPU/GPU pipelines
Embedded systems and hardware
Engineer firmware, operating systems, device software, and target-specific integrations for connected and resource-constrained platforms.
- Linux
- Embedded Linux
- Yocto
- Buildroot
- Bare metal
- BSD
- x86-64
- AArch64
- AArch32
- Microcontroller platforms
- Firmware
- Bootloaders
- Board-support packages
- Device drivers
- Hardware interfaces
Networking and high-performance infrastructure
Design observable, secure, high-throughput data paths and infrastructure across packets, protocols, servers, and private compute.
- L2-L7 networking
- Routing and switching
- SDN and programmable networking
- Packet processing
- GTP
- GRE
- MPLS
- MPTCP
- Virtualization
- Network visibility and telemetry
- High-throughput data paths
- Low-latency infrastructure
- Linux servers and compute clusters
- NGINX
Open networking, network operating systems, and SDN
Includes SONiC - Software for Open Networking in the Cloud - and open control, configuration, and high-performance data-plane technologies.
- SONiC
- Open vSwitch
- OVN
- FRRouting
- OpenDaylight
- ONOS
- µONOS
- P4
- P4Runtime
- Stratum
- OpenFlow
- NETCONF
- RESTCONF
- YANG
- gNMI
- gNOI
- OpenConfig
- eBPF
- XDP
- VPP / FD.io
- DPDK
- ODP
Open and programmable mobile-core platforms
Mobile-core platforms and accelerated user-plane options for telecom and private-network deployment.
- Magma
- Open5GS
- free5GC
- OpenAirInterface 5G Core
- Aether / SD-Core
- Kubernetes-based mobile-core deployment
- DPDK-accelerated user planes
- eBPF/XDP user planes
- VPP-based user planes
Supporting and Specialized Technologies
Applications and supporting technologies
Product applications, control planes, operator interfaces, and device experiences that connect systems to users and workflows.
- Node.js
- Qt
- QML
- React
- Angular
- Vue
- NestJS
- iOS
- Android
Legacy and enterprise platforms
Specialized technologies retained for integration, modernization, and long-lived enterprise systems without defining the primary stack.
- AIX
- DB2
- WPF
- PHP
- Corosync
- SQL
- MySQL
DISCUSS A CROSS-LAYER ENGINEERING CHALLENGE
Need a team that can work beyond the model?
Talk to OPTIME about a production-AI, embedded, telecom, media, networking, security, or high-performance system that crosses application, infrastructure, hardware, and operational boundaries.