FULL-STACK SOFTWARE & APPLIED AI STUDIO

Engineering Autonomous AI, Native Apps & High-Velocity Web Platforms.

From custom full-stack web applications and native desktop/mobile software to multi-agent AI orchestration, RAG pipelines, and domain-specific LLM fine-tuning—built for deterministic reliability, security, and scale.

Request Architecture Consultation Explore 6 Core Pillars

End-to-End

Web, Mobile, Desktop & Cloud

Air-Gapped / Cloud

Local vLLM or Hybrid APIs

LoRA / Full FT

Custom Model Weights

orchestrator_pipeline.py — Acid Secs AI Core

# Multi-Agent Orchestration & Fine-Tuned Inference
from asp_ai.orchestrator import AgentSwarm, VectorRouter
from asp_ai.inference import vLLMEngine

engine = vLLMEngine(
    model="asp-domain-llama3-70b-qlora",
    quantization="awq_int4",
    tensor_parallel_size=2
)

swarm = AgentSwarm(
    router=VectorRouter(collection="enterprise_kb"),
    validators=["schema_guard", "hallucination_triad"],
    mcp_tools=["sql_executor", "stripe_billing", "smtp_dispatch"]
)

response = await swarm.execute_workflow(
    task="Automate enterprise intake, verify compliance & dispatch invoice",
    deterministic=True
)

// Core Engineering Capabilities

Six Disciplines. One Unified Engineering Partner.

We bridge traditional enterprise software engineering with frontier machine learning—eliminating the gap between prototype notebooks and production-hardened systems.

Web Application Development

High-throughput SaaS platforms, customer portals, real-time analytics dashboards, and automated billing & e-commerce architectures built for speed and SEO authority.

Native & Cross-Platform Apps

Custom Windows desktop software, iOS, Android, and macOS applications engineered with hardware-level performance, offline-first databases, and direct peripheral/protocol integrations.

Enterprise AI Integration

Embed LLMs, vision models, and speech pipelines directly into your existing ERPs, CRMs, databases, and internal workflows using Model Context Protocol (MCP) and strict JSON schemas.

Multi-Agent AI Orchestration

Autonomous multi-agent pipelines featuring semantic task routing, multi-step planning, cross-model verification triads, and deterministic guardrails that eliminate hallucinations.

Custom AI Model Training

End-to-end dataset curation, synthetic data pipelines, tokenization, and foundational pre-training or continual pre-training for specialized scientific, clinical, and technical domains.

LLM Fine-Tuning & Alignment

Transform open-weight models (Llama, Mistral, Qwen, DeepSeek) into domain specialists using LoRA, QLoRA, DPO, and RLHF—optimized and quantized for cloud or on-premise inference.

// Production Blueprints

How We Architect Intelligent Systems

Select a deployment layer below to inspect how Acid Secs Productions Inc. structures full-stack software and AI workloads.

Deterministic Multi-Agent Workflows

Single-prompt wrappers fail in production. We build orchestrated agent graphs that decompose complex user intents, retrieve grounded context from vector + graph stores, and validate outputs before execution.

> Hybrid RAG + Knowledge Graphs: Combines dense vector embeddings (ChromaDB/Qdrant) with NetworkX/Neo4j entity relationships.
> Consensus Validation: Independent verifier agents audit generated SQL, code, or reports against strict schemas before delivery.
> Cloud or Local Air-Gapped Execution: Run via frontier cloud APIs or 100% locally on dedicated NVIDIA Blackwell/Ada hardware.

routing_topology.json

{
  "ingress": "SemanticTaskClassifier",
  "routing_policy": "Asymmetrical_Dual_Tier",
  "tiers": {
    "fast_triage": "8B-Instruct-FP8 (Sub-80ms)",
    "deep_synthesis": "70B-Domain-FineTuned"
  },
  "grounding": ["VectorStore", "GraphOntology", "SQLReadReplica"],
  "output_enforcement": "Strict_Pydantic_JSON_Schema"
}

Custom Weights & Domain Adaptation

When prompt engineering hits a ceiling, we train and fine-tune custom models on your proprietary codebase, technical documentation, or industry data so the model natively speaks your domain language.

> Automated Data Sanitization: Deduplication, PII scrubbing, and synthetic instruction-pair generation.
> Parameter-Efficient Fine-Tuning: QLoRA/LoRA adapters and Direct Preference Optimization (DPO) for tone and accuracy.
> Inference Optimization: Post-training quantization (AWQ, GPTQ, EXL2, GGUF) for maximum tokens/sec per dollar.

finetune_config.yaml

base_model: meta-llama/Llama-3.1-70B
adapter: qlora_4bit_nf4
lora_r: 64
lora_alpha: 128
target_modules: [q_proj, k_proj, v_proj, o_proj, gate_proj]
dataset_mix:
  - domain_corpus_verified.jsonl (70%)
  - synthetic_function_calls.jsonl (30%)
eval_benchmark: strict_domain_f1 >= 0.96

Production Web Portals & Native Desktop/Mobile

Great AI requires rock-solid software around it. We engineer complete commercial applications with integrated invoicing, payment gateways, real-time sockets, and native OS integration.

> Cross-Platform Native UI: .NET MAUI, WPF, and modern web SPAs sharing unified C#/TypeScript/Python business logic.
> Automated Billing & FinTech: Embedded Stripe Checkout, PDF generation, and automated SMTP/IMAP communications.
> Hardened Infrastructure: Zero-trust Cloudflare tunnels, LiteSpeed edge caching, and automated CI/CD deployment.

system_stack.manifest

FRONTEND  :: React / Next.js / HTML5 / Tailwind
DESKTOP   :: .NET 9 MAUI / WPF / C# / WinUI
BACKEND   :: Python FastAPI / ASP.NET Core / Node
DATA      :: PostgreSQL / MySQL / SQLite / ChromaDB
PAYMENTS  :: Stripe Checkout API / Automated PDF SMTP