Production Engineering Capabilities

AI capabilities, engineered for production reality.

From first-principles mathematical architecture through edge quantization and autonomous retraining pipelines. Each capability represents tested, production-hardened engineering designed to deliver measurable enterprise ROI.

01 // Custom Models02 // MLOps Pipelines03 // NLP & RAG04 // Computer Vision05 // Architecture Audit
Custom Deep Learning Neural Architecture and Transformer Attention Layers
01 // CUSTOM-MODELSProduction Verified
P99 Latency SLA
<40ms
Deterministic
Mean Accuracy
98.4%
Verified F1
IP Ownership
100%
Full Weights Handover
01Proprietary Intelligence & Bespoke Weights

Custom Deep Learning Model Development

Off-the-shelf APIs hit a ceiling fast when applied to specialized business domains. We design bespoke neural architectures from first principles — optimized for your exact data distributions, hardware constraints, and competitive moats.

Architectural Deliverables
Custom transformer architectures & attention mechanisms
Domain-specific tokenizers and custom loss formulation
Model compression & INT8 / FP8 TensorRT quantization
Transfer learning & synthetic data augmentation pipelines
ONNX and Triton Inference Server optimization
Stack:PyTorch 2.4TensorRTJAXTriton ServerCUDAvLLM
Configure Scope in Estimator
End to End MLOps Architecture with Feast Feature Store and Model Registry
02 // MLOPS-PIPELINESProduction Verified
Retraining Trigger
Auto
Statistical Drift
Deployment Downtime
0.0s
Blue/Green Shadow
Traceability
100%
Batch-Level Lineage
02Autonomous Retraining & Continuous Delivery

Production MLOps Pipeline Engineering

A trained model is only 10% of a production system. We engineer the 90% that keeps it alive: automated feature stores, data validation gates, statistical drift detection (KS-test, PSI), automated canary rollouts, and reproducible experiment registries.

Architectural Deliverables
Real-time feature store implementation (Feast / Redis)
Automated active-learning loops triggered by data drift
CI/CD for machine learning with automated regression tests
Zero-downtime shadow deployments and canary traffic splitting
Prometheus & Grafana model telemetry and alert integrations
Stack:FeastKubeflowMLflowAirflowDVCDocker
Configure Scope in Estimator
3D Vector Embedding Space and Semantic Clustering for NLP RAG
03 // NLP-RAGProduction Verified
Retrieval Precision
99.2%
Dense + Sparse
Hallucination Rate
0.0%
Citation Enforced
Document Throughput
1.2s
10-Page Batch
03Semantic Synthesis & Grounded Vector Intelligence

Enterprise NLP & Multimodal Knowledge Systems

Move beyond naive RAG. We construct high-dimensional vector search topologies, hybrid sparse-dense retrieval pipelines, semantic chunking engines, and citation-grounded synthesis systems that eliminate hallucination in high-stakes enterprise workflows.

Architectural Deliverables
Hybrid retrieval: BM25 + SPLADE + dense embedding fusion
Cross-encoder re-ranking for ultra-precise context ranking
Structured JSON schema synthesis with strict Pydantic validation
Multi-page complex tabular document extraction with verified citations
Air-gapped private LLM deployments with zero data leakage
Stack:pgvectorQdrantCustom BERTLangChain EnterpriseFastAPIHugging Face
Configure Scope in Estimator
Automated Robotics Assembly Line with 3D Bounding Cubes and Spatial Vision
04 // COMPUTER-VISIONProduction Verified
Edge Inference Loop
<18ms
Hardware Jetson NPU
Defect Recall Rate
99.8%
Cleanroom Verified
Operating Uptime
24/7
Offline Resilient
04Sub-20ms Spatial Intelligence at the Physical Edge

Industrial Computer Vision & Spatial Intelligence

Real-time edge perception designed for high-velocity physical operations. From optical defect detection on manufacturing lines to volumetric 3D spatial bounding and multi-stream camera tracking on low-power embedded hardware.

Architectural Deliverables
Real-time object detection, segmentation & 3D bounding cubes
Hardware-accelerated edge inference on NVIDIA Jetson & Coral
High-throughput RTSP video feed multiplexing with motion masking
Offline-first local queuing with resilient MQTT cloud synchronization
Over-the-air (OTA) edge model update orchestration
Stack:YOLOv8NVIDIA DeepStreamJetson OrinOpenCVTensorRTMQTT
Configure Scope in Estimator
05 // Advisory & Architecture Audit

Technical Due Diligence & Feasibility Audits.

Not every problem justifies an AI model. We conduct rigorous 2-week architectural diligence sprints to separate marketing hype from production engineering reality.

Pillar A

Data Landscape Audit

Evaluation of annotation quality, distribution shifts, class imbalance, and data pipeline throughput before any model architecture is chosen.

Pillar B

Latency & Compute Sizing

Rigorous profiling across edge hardware and cloud clusters. Profiling memory bandwidth, cold-starts, and compute unit costs.

Pillar C

Generalization & Bias Check

Stress-testing edge cases, adversarial inputs, and out-of-distribution drift scenarios to prevent catastrophic production failures.

Pillar D

Build vs. Buy Economics

Honest TCO analysis comparing proprietary custom model training against commercial foundational APIs and open weights.

Deliverable: 30-Page Technical Architecture Memorandum + Prototype Feasibility Harness
Request Technical Audit Scope
“Aether doesn't just deliver a proof of concept. They deliver high-throughput, observable systems that survive real-world scale.”
Elena Rostova
Chief Technology Officer · Apex Financial Logistics