Arash
Nicoomanesh

Bridging Strategic Business Intent
with Adaptive Agentic Systems

Core Capabilities
Multi-Agent Microservices & Orchestration Neuro-Symbolic Hybrid Governance Advanced Memory & State Management Runtime Optimization & Agent FinOps Simulation & Property-Based Evaluation
Agentic AI Systems

Autonomous Multi-Agent Orchestration & Cognitive Planning

LLM Engineering

Advanced Reasoning Patterns & Fine-Tuning Optimization

Predictive Machine Learning

Industrial Time-Series Forecasting & Recommendation Engines

Cross-Domain Experience
Enterprise Healthcare Marketing Supply Chain Finance & Banking Manufacturing
[SYS_CORE // 01]

Deterministic Core

Enterprise autonomy requires absolute reliability. The LLM is isolated as an untrusted guest within a rigid state machine, ensuring critical actions are strictly governed.

Explore Core Architecture
[REASONING // 02]

Stochastic Range

Intelligence requires constraints. We architect reasoning engines that creatively explore solutions, distilling output into strict deterministic execution graphs.

Explore Reasoning Engines
[ORCHESTRATION // 03]

Multi-Agent Scale

A single agent is a prototype; centralized orchestration is a platform. We scale isolated bots into parallel, DAG-driven sub-agents.

Explore Orchestration

Agentic Solutions

Real problems, strict constraints. Engineering autonomous systems built to survive and scale in production.

> Contribute to Github
Edge AI / Model Compression

Edge SLM Optimizer

Edge-First Small Language Model Compression & Deployment Pipeline.

Multi-stage quantization pipeline (FP32 → INT8 → INT4) with dual export targets for ONNX Runtime Mobile and ExecuTorch. Includes speculative decoding with a 100M-parameter draft model for 2x speedup, and full telemetry suite measuring watts-per-token on Raspberry Pi 5 under 5W sustained.

PyTorchONNXExecuTorchllama.cpp
Inspect Repository →
edge-slm — quantize-deploy
$ edge_slm optimize --model=gemma-1b --target=rpi5
# --- STAGE 1: QUANTIZATION ---
[QUANT] FP32 → INT8 (static) → INT4 (dynamic)
      └─ Perplexity: 12.4 → 13.8 (<15% degradation)
# --- STAGE 2: EXPORT ---
[ONNX] Cross-platform CPU build complete
[EXECUTORCH] ARM NEON delegate compiled
# --- STAGE 3: BENCHMARK ---
[RPI5] Latency: 42ms/token | Power: 3.8W
[SPEC] Draft model (100M) → 2x decode speedup
[PASS] All accuracy gates cleared.
[STATUS] EDGE_READY_FOR_DEPLOY
Local-First / 3-Agent Pipeline

Enterprise Intelligence Crew

Autonomous enterprise trend intelligence pipeline.

Sequential 3-agent CrewAI pipeline (Trend Investigator → Risk Analyst → Copywriter) with LangGraph risk gate state machine, local-only Ollama inference, ChromaDB semantic memory, and Pydantic V2 contracts at every stage. Zero API keys, zero cloud dependency.

CrewAIOllamaChromaDBFastAPIPydantic V2
Inspect Repository →
enterprise-crew — trend-intel
$ crew run --topic="edge-ai-adoption" --local
# --- AGENT 1: TREND INVESTIGATOR ---
[OLLAMA] gemma2:2b analyzing trend signals...
[CHROMADB] Retrieved 12 related research chunks
# --- AGENT 2: RISK ANALYST ---
[LANGGRAPH] Risk gate: evaluate → approve
      └─ Confidence: 0.87 | Risk: LOW
# --- AGENT 3: COPYWRITER ---
[PYDANTIC] ContentPayload validated ✓
[PASS] 3/3 agents completed. Output persisted.
[STATUS] INTELLIGENCE_REPORT_READY
Reasoning Optimization

DeepSeek Reasoning Fine-Tuning

Medical chain-of-thought LoRA alignment pipeline.

Efficient 4-bit parameter fine-tuning that maps diagnostic reasoning patterns into model weights. Uses Unsloth for memory-efficient training, improving structured clinical response generation while preserving general reasoning capabilities.

UnslothPyTorchTRLLoRA
Inspect Repository →
deepseek-lora — reasoning-train
$ train_lora --base=deepseek-r1 --bits=4 --data=clinical_cot
# --- CONFIG ---
[UNSLOTH] 4-bit QLoRA: r=16, alpha=32
# --- TRAINING ---
[EPOCH 1/3] Loss: 2.34 → 1.12 | Grad norm: 0.8
[EPOCH 2/3] Loss: 1.12 → 0.67 | Grad norm: 0.4
[EPOCH 3/3] Loss: 0.67 → 0.41 | Grad norm: 0.2
# --- EVALUATION ---
[PASS] Clinical accuracy: +18% vs base model
[PASS] Reasoning coherence: 0.91
[STATUS] LORA_ADAPTER_SAVED
Oncology-Focus

Clinical Oncology Agent

Navigates NCCN guidelines with toxicity-aware reasoning.

Deterministic orchestration of multi-drug regimens via Human-in-the-Loop (HITL) safeguards, enforcing strict physiological constraints and site-specific protocols.

FHIR/HL7Policy-as-CodeDAG-Orchestrated
View Case Study →
onco-pathway — precision-med-v3
$ agent_core run --workflow="onco_triage" --pt_id="PT-88392"
# --- STEP 1: PERCEPTION & NORMALIZATION ---
[PERCEIVE] Ingesting EMR: ER/PR+, HER2-, Echocardiogram.
[NORMALIZE] Mapping data to NCCN clinical vectors...
      └─ Option A: AC-T (Doxorubicin) - 92% Efficacy
      └─ Option B: TC (Docetaxel) - 88% Efficacy
# --- STEP 2: GOVERNANCE PRE-FLIGHT ---
[STRATEGIST] "Option A maximizes survival. Building node..."
[GOVERN] Intercepting node: prescribe(regimen="AC-T")
# --- STEP 3: POLICY-AS-CODE SHIELD ---
[DENY] POLICY_VIOLATION (rule: cardiotoxicity)
      └─ Reason: "Patient LVEF is 48% (Threshold >50%)."
# --- STEP 4: DETERMINISTIC RECOVERY ---
[RE-PLAN] Reverting to Option B. Re-running policy check...
[PASS] Safety parameters cleared. Node approved.
# --- STEP 5: COMMIT & HANDOFF ---
[COMMIT] Immutable record written to the ledger.
[STATUS] AWAITING ONCOLOGIST SIGN-OFF...
Clinical AI / Hybrid RAG

Speculative Clinical GraphRAG

9-node LangGraph with hybrid retrieval & self-correction.

Qdrant vector store + Neo4j graph traversal with fusion scoring (α=0.7). Quad-track LLM backend with SemanticRouter auto-selection. Self-correcting feedback feeds violations + prior reasoning back via regenerate_with_feedback() with confidence decay.

LangGraphNeo4jQdrantDeepSeek-R1SNOMED-CT
Inspect Repository →
clinical-graphrag — speculate
$ /v1/speculate --note="persistent cough, weight loss"
# --- STEP 1: HYBRID RETRIEVAL ---
[QDRANT] Vector search: 12 ontology concepts
[NEO4J] Graph traversal: 34 taxonomic edges
      └─ Fusion: α=0.7 vector + 0.3 graph
# --- STEP 2: DIFFERENTIAL ASSESSMENT ---
[LLM] Differentials: lung_ca(0.82) tb(0.11) sarcoid(0.07)
# --- STEP 3: SAFETY VERIFY ---
[FAIL] Neo4j edge lung_ca→metastasis missing
# --- STEP 4: SELF-CORRECTION (1/3) ---
[REGEN] Feeding violations + reasoning trace...
      └─ Confidence: 0.82 → 0.72 (decay -0.1)
[PASS] Corrected path validated by all verifiers.
[AUDIT] Trace committed. Zero PHI persisted.
[STATUS] AWAITING_CLINICIAN_REVIEW
Drug Discovery

Molecular Discovery Agent

High-throughput docking agent that halts synthesis of hepatotoxic structures.

Automated virtual screening pipeline that combines molecular docking simulations with toxicity prediction. The agent iteratively proposes candidate molecules, evaluates binding affinity, and rejects structures flagged for hepatotoxicity — all within a closed-loop autonomous workflow.

SimulationRDKitAutoDock
View Case Study →
mol-screen — docking-pipeline
$ mol_screen run --target="EGFR" --rounds=200
# --- BATCH 1: VIRTUAL SCREENING ---
[DOCK] Screening 200 candidates against EGFR...
[FILTER] Top 12 by binding affinity (ΔG < -8.5 kcal/mol)
# --- BATCH 2: TOXICITY PREDICTION ---
[ADMET] Running hepatotoxicity classifier...
[REJECT] 4 candidates flagged: DILI risk > 0.7
[PASS] 8 candidates cleared all safety gates.
[RANK] Best: compound_047 (ΔG=-9.2, DILI=0.12)
[AUDIT] Full pipeline trace persisted.
[STATUS] SCREENING_COMPLETE
Repurposing

Biomedical Hypotheses Agent

Hypothesis-driven retrieval over PubMed for repurposing leads.

Graph Neural Network over a knowledge graph of drug-gene-disease associations. The agent generates repurposing hypotheses, validates them against PubMed evidence, and scores confidence based on citation density and pathway overlap.

PubMed-KBGNNNeo4j
View Case Study →
bio-hypo — repurpose-engine
$ hypo_agent propose --disease="IPF" --top_k=5
# --- STEP 1: KNOWLEDGE GRAPH TRAVERSAL ---
[GNN] Querying drug-gene-disease subgraph...
      └─ Nodes: 1,247 | Edges: 3,891
# --- STEP 2: HYPOTHESIS GENERATION ---
[LLM] Top candidates: nintedanib(0.91) pirfenidone(0.87)...
# --- STEP 3: EVIDENCE VALIDATION ---
[PUBMED] Searching for supporting citations...
      └─ Found: 23 papers for nintedanib-IPF link
[SCORE] Confidence: 0.89 (high evidence density)
[AUDIT] Hypothesis + evidence chain persisted.
[STATUS] HYPOTHESES_RANKED
Game Theory / Ad Tech

Nash Marketing Agents

Neuro-symbolic ad auction simulator with Nash Equilibrium.

LLM proposes stochastic bidding strategies; symbolic Nash solver validates via iterative best-response with softmax annealing. VCG second-price engine enforces paid≤bid invariant. Monte Carlo 5000-sample win-probability estimation with multi-layer budget guardrails.

FastAPISciPyPydantic v2Game TheoryDocker
Inspect Repository →
nash-sim — auction-engine
$ nash_sim run --rounds=50 --agents=4
# --- ROUND 1: STRATEGY PROPOSAL ---
[LLM] Bids: Agent-A=$2.40 B=$1.85 C=$3.10 D=$2.70
[SOLVER] Best-response iteration (softmax τ=0.3)...
# --- VCG AUCTION ENGINE ---
[AUCTION] Winner: Agent-C (bid $3.10)
      └─ VCG payment: $2.70 (next-highest bid)
[ASSERT] paid≤bid invariant: 2.70 ≤ 3.10 ✓
# --- BUDGET GUARDRAILS ---
[WARN] Agent-A budget at 22% → soft warning
# --- CONVERGENCE @ ROUND 41 ---
[NASH] Equilibrium reached. Δu < 0.01
[TEST] 49/49 property-based tests passed.
[STATUS] SIMULATION_COMPLETE
Multi-Agent / Bandits

Marketing ROI Optimizer

Continuously reallocates budget using Multi-Agent Swarms and Multi-Armed Bandits.

A swarm of specialized agents — data collector, analyzer, optimizer, and reporter — collaborate to continuously monitor campaign performance and reallocate budget across channels in real-time using Thompson Sampling bandits.

BanditsStreamingMulti-Agent
View Case Study →
roi-optimizer — budget-agent
$ roi_agent optimize --budget=$50k --channels=6
# --- COLLECT: Real-time performance data ---
[SWARM] DataCollector: ingesting 6 channel feeds...
# --- ANALYZE: Attribution modeling ---
[SWARM] Analyzer: last-touch + Shapley values
      └─ Insight: Google Ads CPA rose 34% this week
# --- OPTIMIZE: Thompson Sampling ---
[BANDIT] Reallocating: Google→LinkedIn (+$4.2k)
[ASSERT] Total budget invariant: $50,000 ✓
# --- REPORT: Executive summary ---
[SWARM] Reporter: projected CPA improvement -18%
[STATUS] REALLOCATION_APPLIED
FinTech / RegTech

Automated KYC & AML Screening Agent

SLM-first ReAct agent with deterministic risk scoring.

Single-threaded LangChain ReAct loop on CPU using 4-bit Qwen2.5-7B-Instruct. Pluggable BaseLLMBackend swaps to vLLM GPU inference via one-line config. Every tool call validated by Pydantic v2 with immutable PostgreSQL audit trails.

Qwen2.5-7BLangChainNeo4j UBOOpenSanctionsPostgreSQL
Inspect Repository →
kyc-agent — react-loop
$ kyc_agent screen --entity="Acme_Corp_Ltd"
# --- REACT TURN 1: TOOL_CALL ---
[REASON] Entity requires PEP & sanctions check.
[TOOL] sanctions_check(name="Acme Corp")
      └─ Result: OFAC: CLEAR | EU: CLEAR | cache_miss
# --- REACT TURN 2: TOOL_CALL ---
[REASON] Fetching corporate registry for UBO extraction.
[TOOL] ubo_extract(doc="incorporation.pdf")
      └─ Extracted: 3 beneficial owners → Neo4j graph
# --- REACT TURN 3: DETERMINISTIC ---
[TOOL] risk_score_combine() # pure algorithm, no LLM
[PASS] Composite Risk: 12/100 (Low). Cleared.
[AUDIT] Pydantic-validated KYCPacket → PostgreSQL.
[STATUS] ONBOARDING_APPROVED
Zero-Shot Forecasting / MCP

Zero-Shot Demand Foundation

MCP Agentic Forecaster Skill with foundation models.

Zero-shot time-series demand forecasting using Google TimesFM and Amazon Chronos-2. Dual-track evaluation (point forecast + quantile bands) aligned with M5 Competition framework. Pydantic-validated 3D tensor input with exogenous signal support for price elasticity and promotional flags.

PyTorchTimesFMChronos-2Pydantic v2M5 Benchmark
Inspect Repository →
demand-foundation — zero-shot
$ forecast run --series=walmart_daily --horizon=128
# --- MODEL SELECTION ---
[TIMESFM] google/timesfm-2.5-200m loaded
[CHRONOS] amazon/chronos-2 loaded
# --- INFERENCE ---
[SHAPE] (3049, 1, 16000) → (3049, 128)
[QUANTILE] p10/p50/p90 bands computed
# --- M5 BENCHMARK ---
[WAPE] 15.2% | [RMSSE] 0.78
[VALID] Pydantic ForecastOutputPayload ✓
[STATUS] FORECAST_COMPLETE
Turn-Based Negotiation / Market Sim

Autonomous Procurement Swarm

LLM-Powered Multi-Agent Contract Negotiation.

4-agent negotiation system (Buyer, Seller, Market Intelligence, Arbiter) with stochastic market simulation using Geometric Brownian Motion and 4-state Markov chain geopolitical risk model. Reward engineering balances cost, margin, capacity utilization, and risk premiums.

FastAPISciPyTransformersPydantic v2Market Sim
Inspect Repository →
procurement-swarm — negotiate
$ swarm negotiate --item="steel_coils" --rounds=20
# --- MARKET STATE ---
[SIM] GBM price: $842/ton | Regime: MEDIUM
[RISK] Markov geopolitical: LOW (p=0.72)
# --- TURN 5: BUYER ---
[BUYER] Offer: $810/ton (cost-target: $815)
# --- TURN 6: SELLER ---
[SELLER] Counter: $835/ton (margin: 12%)
# --- ARBITER ---
[PASS] Fair range validated. Gap: $25
[LEDGER] Hash-chain integrity ✓
[STATUS] NEGOTIATION_IN_PROGRESS
Generative Chemistry

Quantum-Bound Molecular Generator

Zero-Waste Neuro-Symbolic Molecular Engine.

Type 6 Neuro[Symbolic] architecture with symbolic physics (valency, symmetry) embedded directly into the PyTorch computation graph as differentiable convex projection. Every forward pass outputs a chemically valid bond adjacency matrix — zero compute waste. IFT backprop through KKT equilibrium enables end-to-end gradient flow.

PyTorchSE(3)-GNNConvex OptimizationIFTType 6
Inspect Repository →
qbmg — molecular-gen
$ qbmg generate --atoms=24 --target=C10H12N2O
# --- NEURAL PASS ---
[GNN] SE(3)-equivariant forward: 24 atoms
      └─ Raw adjacency: 24x24 matrix (continuous)
# --- SYMBOLIC PROJECTION ---
[KKT] Convex projection: valency constraints
      └─ C≤4 O≤2 N≤3 enforced via KKT
[IFT] Jacobian through KKT equilibrium ✓
[VALID] 100% chemically valid (zero waste)
[PASS] Frobenius distortion: 0.003
[STATUS] MOLECULE_GENERATED
Computational Biology

Protein Binder Flow

Flow-matching protein binder generator.

Flow matching for structural molecular generation, moving beyond diffusion-based protein design approaches. Targets novel protein-ligand binding discovery using PyTorch and Biopython, with FoldSeek for structural alignment validation.

PyTorchBiopythonFlow MatchingFoldSeek
Inspect Repository →
protein-flow — binder-gen
$ flow_match generate --target=EGFR --n=100
# --- STEP 1: STRUCTURE PREP ---
[FOLDSEEK] Binding site aligned: 147 residues
# --- STEP 2: FLOW MATCHING ---
[FLOW] ODE integration: t=0 → t=1 (100 steps)
      └─ Backbone RMSD: 1.2Å (converged)
[FLOW] Side-chain packing: 89% rotamer correct
# --- STEP 3: VALIDATION ---
[PASS] Docking score: -8.7 kcal/mol
[PASS] AlphaFold pLDDT: 0.82
[STATUS] 100_BINDERS_GENERATED

Consulting Services

Build and operate intelligent systems that last. We transition probabilistic AI models into governed, zero-trust production infrastructure.

01

The Blueprint

AI Strategy & Advisory

De-risk your AI investment with a comprehensive technical architecture, strict compute/token cost models, and an execution roadmap.
02

The Forge

Custom Agentic Prototype

Transform your validated blueprint into a production-grade prototype. We engine the deterministic skeletons and stateful loops needed to scale.
03

The Nexus

Agentic Deployment & Scale

Transition code into autonomous systems. We implement Policy-as-Code guardrails, temporal state management, and strict token telemetry.
[ FOUNDATIONAL LAYER // CORE ENGINE ]

Generative AI & LLM Engineering

This core capability drives every stage of our pipeline. Moving beyond fragile API wrappers, we bridge the gap between probabilistic text generation and strict neuro-symbolic logic, ensuring model outputs map cleanly into deterministic execution graphs.

Explore Core Infrastructure →

Enterprise Agentic Orchestration

Hub-and-Spoke Pipelines for High-Stakes Industries

Deploy governed, multi-agent digital workforces engineered for mission-critical environments. We architect centrally orchestrated pipelines that execute complex, state-altering workflows under strict deterministic constraints and immutable audit ledgers.

crispr-orchestrator — deterministic-v2
$ orchestrator run --workflow="crispr_designer" --target="Gene_HBB"
# --- STEP 1: PERCEPTION & SIMULATION ---
[PERCEIVE] Ingesting patient genome (3.2B base pairs).
[TOOL_INVOKE] Cas9_Microservice -> Binding prediction...
  └─ Candidate A: Efficiency 99.1% (Primary)
# --- STEP 2: DETERMINISTIC PLANNING ---
[PLANNER] Intent matched. Compiling execution DAG...
[GOVERN] Intercepting node: commit_design(candidate="A")
# --- STEP 3: POLICY (OPA) PRE-FLIGHT ---
[DENY] POLICY_VIOLATION (rule: strict_off_target)
  └─ Critical Risk: "0.4% off-target match with 'TP53'."
# --- STEP 4: DAG RE-COMPILATION ---
[RE-PLAN] Reverting to Candidate B. Re-running policy check...
[PASS] Zero critical matches detected. Node approved.
# --- STEP 5: STATELESS EXECUTION & AUDIT ---
[EXECUTE] Irreversible write: finalize_design(candidate="B").
[STATUS] SAFE DESIGN LOCKED...
> Explore Orchestration Services