Principal-Led Enterprise AI & .NET 10 Systems Architecture
Move beyond fragile prompt wrappers and experimental sandboxes. Led by Principal Architect Zeeshan Dad Khan, we engineer production-hardened Generative AI platforms, zero-key ASP.NET Core 10 AI gateways on Google Vertex AI and Azure OpenAI, and offline on-device Edge AI applications that deliver measurable cost reduction and revenue growth.
End-to-End AI Engineering
Every AI system we build is architected on strict Onion Architecture principles—decoupling your core domain logic from rapidly evolving model providers so your investment never goes obsolete.
Rigorous technical discovery before writing code. We evaluate your proprietary datasets, benchmark candidate models against latency and unit-cost targets, and design a vendor-agnostic architecture blueprint comparing Cloud APIs against on-device Edge AI.
Measurable Business Impact: Eliminates wasted R&D spend and validates positive unit economics before full production rollout.
Production integration of Google Vertex AI (Gemini 3.1 Flash & Pro), Azure OpenAI, and Anthropic Claude with deterministic structured JSON outputs, multi-turn Anchor & Delta context management, and automated provider failover.
Measurable Business Impact: 99.95% AI gateway availability with zero context drift across complex multi-turn user sessions.
Run quantized SLMs and vision/speech models directly on user hardware across iOS, Android, macOS, and Windows using .NET MAUI, ONNX Runtime, Apple CoreML, Whisper Tiny, Qwen, and Microsoft Phi-4 with hardware NPU acceleration.
Measurable Business Impact: 100% offline capability, zero per-inference cloud API bills, and absolute local data privacy.
Protect your AI infrastructure with high-throughput ASP.NET Core 10 BFF gateways that authenticate via Google Cloud Application Default Credentials (ADC) and Azure Managed Identity—eliminating hardcoded API keys from mobile and web clients.
Measurable Business Impact: Zero credential leakage risk, cryptographic billing verification, and per-tenant quota enforcement.
We engineer plug-and-play C# NuGet libraries and modular microservices for RAG ingestion, document OCR, semantic caching, and tool-calling orchestration that snap into your web, mobile, and desktop apps without duplicate code.
Measurable Business Impact: 60–90% code reuse across business units, cutting time-to-market for subsequent AI features in half.
Full-stack observability using OpenTelemetry, Redis semantic prompt caching, dynamic model routing, OWASP LLM Top 10 prompt-injection shields, and PII redaction pipelines for regulated FinTech and Healthcare workloads.
Measurable Business Impact: Up to 55% reduction in monthly token spend and audit-ready GDPR/HIPAA compliance.
Verified Production Systems
We don't theorize about AI architecture—we ship and operate high-concurrency AI systems in production. Here is how our battle-tested patterns solve real enterprise engineering challenges.
Multi-turn generative AI image editing and conversational workflows rapidly degrade when iterative outputs are fed back into the model—causing facial identity drift, hallucinated artifacts, and compounding quality loss.
For our flagship VisionFlow AI platform (.NET MAUI 10 + ASP.NET Core 10 + Google Vertex AI), we architected the Anchor & Delta Context Engine. Every turn references the immutable original Anchor payload paired with a structured JSON State Delta, authenticated via zero-key Google Cloud Application Default Credentials (ADC).
10,000+ active users, 4.8★ store rating, 100% biometric identity preservation across multi-turn 2K studio edits, and zero exposed client credentials.
Heavy neural inference tasks (such as deep archival photo restoration, custom LoRA fine-tuning, and batch document OCR) block HTTP web threads and cause gateway timeouts when run synchronously inside web APIs.
In LuminaEdit and our enterprise AI backends, we decouple ingestion from inference using ASP.NET Core 10 Minimal APIs, asynchronous job queues, and dedicated PyTorch MPS / CUDA worker nodes that stream live percentage telemetry back to .NET MAUI and Blazor clients via SignalR WebSockets.
Sub-25ms HTTP API responsiveness under heavy GPU load with real-time progress streaming and automatic retry resilience.
Sending every frame, voice command, or sensitive identity document to cloud LLMs inflates monthly API bills and triggers strict GDPR/HIPAA data-residency roadblocks.
We deploy quantized ONNX Runtime, Apple CoreML, Whisper Tiny STT, and local browser/mobile canvas pipelines (proven on visionflowai.app/tools) so biometric cropping, DPI validation, EXIF stripping, and local SLM inference run 100% on the user's device.
Up to 70% reduction in cloud inference costs and instant sub-15ms local processing with zero server uploads.
Many teams bolt OpenAI SDK calls directly into UI controllers and database repositories, creating untestable spaghetti code that breaks whenever model versions deprecate.
Applying our proven 2-Week Onion Architecture modernization methodology, we isolate pure C# Domain Entities and AI orchestration interfaces in the core while pushing Vertex AI, Azure OpenAI, and vector databases into swappable outer Infrastructure adapters.
100% unit-testable AI domain logic and zero-downtime model upgrades across providers in a record 2-week sprint.
Architecture Comparison & Delivery Blueprint
Why leading enterprises choose our Hybrid Cloud + Edge AI architecture on ASP.NET Core 10 and .NET MAUI over naive single-vendor API wrappers.
| Architecture Dimension | Naive Cloud API Wrapper | Pure On-Device Edge AI | KCC Hybrid Cloud + Edge AI (.NET 10) |
|---|---|---|---|
| 1. Credential & API Security | Often exposes API keys or lacks tenant-level token circuit breakers. | Model weights bundled locally; zero cloud key exposure. | Zero-Key ASP.NET Core 10 BFF using Google Cloud ADC / Azure Managed Identity + local Edge execution. |
| 2. Inference Latency & Offline UX | 300ms–4,000ms network round-trip; fails completely without internet. | Sub-20ms NPU inference; works 100% offline in field environments. | Best of Both Worlds — Instant local ONNX/CoreML pre-processing + streamed SignalR cloud reasoning. |
| 3. Multi-Turn Context Fidelity | Suffers from context bloat, hallucination loops, and identity drift. | Limited by mobile RAM context windows (4k–16k tokens). | Anchor & Delta Context Engine — Immutable Anchor + lightweight Delta state prevents drift. |
| 4. Unit Economics & Token Spend | Unbounded per-request token costs that spike with user growth. | Zero cloud inference cost; constrained by device compute ceiling. | 50–70% Lower TCO — Offloads routine tasks to local NPU/Flash models + semantic Redis caching. |
| 5. Vendor Lock-In & Maintainability | Hardcoded to a single LLM SDK; painful rewrites on model deprecation. | Requires platform-specific C++/Swift/Kotlin wrappers if not unified. | Onion Architecture + Semantic Kernel — Swap Gemini, GPT, Claude, or ONNX models via DI. |
We audit your business workflows, data privacy constraints, and latency targets to establish a quantified ROI baseline and select the right mix of Cloud Frontier LLMs and local Edge models.
We scaffold your clean C# domain core, ASP.NET Core 10 Backend-for-Frontend proxy, Application Default Credentials (ADC), and Anchor & Delta context schemas with zero vendor lock-in.
We implement RAG vector search, deterministic tool-calling, background PyTorch/ONNX workers, and responsive .NET Blazor or .NET MAUI user interfaces with real-time SignalR streaming.
We run OWASP LLM prompt-injection tests, configure OpenTelemetry token/latency dashboards, containerize via Docker, and deploy with zero-downtime CI/CD pipelines.
Enterprise AI Technology Stack
We combine frontier multimodal cloud models with compiled C# 13/.NET 10 performance and hardware-accelerated Edge AI runtimes.
Production integration with Gemini 3.1 Flash & Pro, Imagen, GPT-4o / o3-mini, Anthropic Claude Sonnet & Opus, and open-weights Llama / Mistral endpoints via unified enterprise gateways.
Hardware-accelerated local inference on iOS Neural Engine, Android NNAPI, macOS Apple Silicon MPS, and Windows DirectML using quantized Phi-4, Qwen, Whisper Tiny, and YOLO vision models.
Microsoft Semantic Kernel, Microsoft.Extensions.AI, LangGraph, AutoGen, and our proprietary Anchor & Delta Context Engine.
High-concurrency ASP.NET Core 10 BFF APIs, SignalR real-time streaming, .NET Blazor web portals, and .NET MAUI 10 mobile/desktop apps.
PostgreSQL pgvector, Qdrant, Pinecone, Azure AI Search, SQL Server vector indexing, and Redis semantic prompt caching.
Google Cloud ADC, Azure Managed Identity, OAuth 2.0/OIDC, OpenTelemetry token tracing, and OWASP LLM security guardrails.
Frequently Asked Questions
Clear answers on security, Cloud vs. Edge trade-offs, token economics, and production integration.
We never embed OpenAI, Anthropic, or Google Vertex AI keys inside client binaries or frontend bundles. Instead, we engineer a zero-key Backend-for-Frontend (BFF) proxy in ASP.NET Core 10 that authenticates to cloud AI providers via Google Cloud Application Default Credentials (ADC) or Azure Managed Identities, paired with per-user JWT validation, token budgets, and rate limiting.
The optimal architecture is often hybrid. We run lightweight, latency-critical, or privacy-sensitive tasks—such as Whisper Tiny speech-to-text, biometric face detection, OCR, and quantized Qwen or Phi-4 models—directly on-device via ONNX Runtime and Apple CoreML at zero cloud cost, while routing heavy multimodal generation to Google Vertex AI or Azure OpenAI.
Yes. Unlike agencies that only build slide-deck prototypes, we engineered and operate VisionFlow AI (visionflowai.app), a flagship cross-platform AI studio serving 10,000+ users with a 4.8-star rating, powered by .NET MAUI 10, ASP.NET Core 10, and Google Vertex AI with our proprietary Anchor & Delta biometric identity preservation engine.
We implement semantic prompt caching, tiered model routing (sending classification and extraction to fast Flash/Mini models and reserving frontier reasoning models for complex synthesis), immutable context anchoring, and real-time OpenTelemetry token accounting so your unit economics remain profitable.
Yes. Led by Principal Architect Zeeshan Dad Khan (20+ years of C#, .NET, and systems architecture), we specialize in embedding modular AI services, RAG pipelines, and background PyTorch/ONNX workers into existing ASP.NET Core, legacy .NET Framework, Blazor, and .NET MAUI ecosystems without disrupting live operations.
Direct Principal Consultation
Share your AI product vision, target workflows, or existing codebase constraints. Every inquiry is reviewed directly by Principal Architect Zeeshan Dad Khan within 24 hours on business days.
Zeeshan Dad Khan
