Arash Nicoomanesh

Arash Nicoomanesh

Agentic AI Architect

Deterministic Core · Stochastic Range · Multi-Agent Scale

AI Engineering Services & Consulting

Don't Build Just "Expensive Chatbots" Real Agentic Systems Reason and Learn within Adaptive Architecture

Execution over Conversation

Build AI systems that read reports, flag risks, generate artifacts, and update systems autonomously while learning from outcomes. Intelligence that cannot act or improve from action is not a system; it's a demo.

Planning over Reaction

Most so-called "AI agents" respond to prompts but fail to plan, execute multi-step workflows, persist state, or refine behavior over time. Stateless LLM wrappers are costly — and fragile.

Control over Cleverness

Apply robust agentic architectures that separate stochastic reasoning from deterministic execution, enabling learning within explicit constraints. Governance, validation, and recovery come first.

Agentic AI Architectures Mindmap

Agentic Solutions

Closed-loop autonomous systems with traceable reasoning and human-in-the-loop safety.

CBT Autonomous Agent

Scalable, evidence-guided workflows that detect cognitive distortions and adapt to responses.

Multi-agentRAG
View Case Study → Research tool — not a clinician replacement.

Emotion-Focused Agent

Supports deep emotional processing through sentiment trends and schema mapping.

Empathy-EngineEthics-Guard
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Biomedical Hypotheses

Hypothesis-driven retrieval over PubMed/DrugBank for repurposing leads.

PubMed-KBGNN
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Marketing ROI Optimizer

Continuously reallocates budget using Multi-armed bandits and real-time conversion signals.

BanditsStreaming
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Supply Chain Orchestrator

Autonomously reroutes logistics based on inventory, weather, and telemetry.

SimulationEvent-driven
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Consulting Services
Build and Operate Intelligent Systems that Last

The Blueprint

AI Strategy & Advisory

De-risk your AI investment with a comprehensive technical architecture, cost model, and execution strategy

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The Forge

Solution Development

Validate your AI idea in 4 weeks. We Build a production-grade prototype, not a throwaway demo

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The Nexus

Deployment & Scale

Governance first, Intelligence second. Production architectures with Policy-as-Code, blast-radius containment, and durable execution.

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Generative AI & LLM Engineering

Determinism, Neuro-Symbolic Logic and Optimization

Beyond APIs. Custom architecture, model optimization, and reliable engineering for high-stakes AI systems.

Moving beyond API wrappers, we re-engineer model architectures for production scale. We optimize neural weights for latency, implement custom precision tuning, and design dedicated inference layers that drastically reduce compute costs without sacrificing reasoning quality.

Unsloth vLLM TensorRT FlashAttn PyTorch Triton Quantization LoRA/QLoRA CUDA
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Specialized Agentic AI Solutions

Agentic AI Systems

Neuro-symbolic agents that do work. Separating the 'Brain' and reasoning from the 'Body' and execution. We implement hierarchical planning, self-correction loops, and "Blast Radius" protocols. Your agents won't just chat; they will safely execute complex workflows, verify their own outputs, and persist state across system failures.

LangGraph Temporal DSPy OPA Pydantic BurpSuite LangChain CrewAI
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crispr-designer — deep-crispr-v2
$ design_agent --target="Gene_HBB" --goal="correct_mutation" # --- STEP 1: OBSERVE & SIMULATE --- [OBSERVE] Analyzed patient genome (3.2B base pairs). [SIMULATE] Running Cas9 binding prediction... └─ Candidate A: Efficiency 99.1% (Top Pick) └─ Candidate B: Efficiency 84.5% # --- STEP 2: REASONING --- [THINK] "Candidate A is superior. Maximizes therapeutic edit rate. Preparing synthesis payload." # --- STEP 3: GOVERNANCE GATE (Safety) --- [ACTION_ATTEMPT] finalize_design(seq="GTC...AGG", candidate="A") [POLICY_INTERCEPT] 🛡️ DENY (rule: off_target_toxicity) └─ Critical Risk: "Candidate A has a 0.4% off-target match with 'TP53' (Tumor Suppressor). Editing this locus carries high cancer risk." # --- STEP 4: RECOVERY --- [PLAN_UPDATE] Discarding Candidate A. [EXEC] Finalizing Candidate B (Lower efficiency, zero off-target risk). [STATUS] SAFE DESIGN LOCKED...
Anthropic Claude API Azure AI Foundry Azure ML BentoML BitsAndBytes Celery Chainlit Cloudflare Workers AI CrewAI Databricks Mosaic ML Docker ElasticSearch FAISS FastAPI GCP Vertex AI GitHub Actions GoLang GraalVM Gradio Haystack (Deepset) Hugging Face Transformers IBM Granite 3.0 Jupyter / JupyterHub Kafka Kedro Kubernetes LangChain LangGraph Milvus MLflow Modal MongoDB Atlas Vector Search Nebula Graph Neo4j Nginx Nvidia Merlin Nvidia Triton Inference Server Okta Ollama OpenAI Swarm OpenTelemetry PGVector PostHog Prefect Prometheus Pulumi Pydantic Python PyTorch Quarkus Ray Serve Redis Replicate Rust S3 (MinIO, AWS) Semantic Kerne Snowflake Arctic SQLAlchemy Streamlit Supabase Temporal TensorRT Terraform TGI (Text Generation Inference) Torch Serve TypeScript Vercel AI SDK Vespa

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