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
$ python -m quantization.ptq_int8 --model gemma-3-1b
$ python -m export.onnx_exporter --model gemma-3-1b
$ python -m benchmarks.baseline --model gemma-3-1b
[baseline] Loading gemma-3-1b (1B params, FP32)...
[baseline] Loaded in 2.8s | Size: 2.00 GB
[baseline] Generating 20 tokens...
[baseline] Results: 12.3 tok/s | 81.3 ms/tok | 318 MB
$ pytest tests/ -v --cov
test_quantization.py::TestPTQInt8::test_quantize_returns_model PASSED
test_quantization.py::TestPTQInt8::test_quantized_has_int8_weights PASSED
test_export.py::TestONNXExport::test_export_valid_graph PASSED
test_export.py::TestONNXExport::test_dynamic_shapes_supported PASSED
test_runtime.py::TestONNXRuntime::test_generate_with_tokenizer PASSED
test_telemetry.py::TestInferenceProfiler::test_profile_generation_returns_metrics PASSED
test_end_to_end.py::TestEndToEnd::test_fp32_export_then_quantize_pipeline PASSED
[PASS] 37 passed in 18.42s
[STATUS] EDGE_READY_FOR_DEPLOY
Distributed Inference / KV-Cache

vLLM Context Cache Router

State-Aware Smart Router for Distributed KV-Cache Optimization.

Concurrency-hardened gateway using Radix Tree prefix-matching to pin incoming requests to the worker with the warmest cache overlap. Thread-safe Block Manager with RLock isolation, deterministic LRU eviction, and native backpressure via HTTP 429 load shedding. Reduces TTFT by 40%+ across multi-agent orchestration loops.

PythonFastAPIPydanticPrometheus
Inspect Repository →
vllm-ccr — cache-route
$ uv run python -m vllm_ccr.main
# --- COLD START ---
[WORKER-0] cache_hit_ratio: 0.0 | ttft_ms: 550.0
# --- WARM PREFIX MATCH (same system prefix) ---
[HIT] prefix_tokens_matched: 14 | assigned_worker_id: 0
[PASS] ttft_ms: 550.0 → 12.0 | cache_hit_ratio: 0.583
# --- /metrics (prometheus) ---
vllm_ccr_requests_total{model="vllm-ccr-sim"} 2.0
vllm_ccr_cache_hits_total{model="vllm-ccr-sim"} 1.0
vllm_ccr_worker_utilization{worker_id="0"} 0.031
[PASS] 48 tests passed in 2.34s
[STATUS] CACHE_ROUTER_READY
RAG Evaluation / Drift Detection

Post-RAG Drift Evaluator

Latent Space Drift Telemetry & Comparative RAG Architecture Benchmark.

Statistical framework detecting population-level distribution shifts in vector spaces via Jensen-Shannon Divergence over PCA-reduced embeddings. Benchmarks Naive (Single-Pass) RAG vs Agentic (Multi-Hop) RAG across precision, faithfulness, latency, and token footprint. Includes Streamlit observability dashboard with 2D PCA spatial telemetry.

PythonPolarspgvectorlitellm
Inspect Repository →
rag-drift — evaluate
$ python -m evaluator.benchmark
INFO - --- Starting Evaluation for NaiveRAG ---
INFO - --- Starting Evaluation for AgenticRAG ---
INFO - FINAL BENCHMARK SUMMARY:
  Pipeline    | Avg Precision | Avg Faithfulness | Avg Latency
  AgenticRAG | 0.92         | 0.89           | 1.45s
  NaiveRAG   | 0.74         | 0.71           | 0.38s
$ pytest tests/ -v
test_drift_math.py::test_drift_monitor_zero_divergence PASSED
test_drift_math.py::test_drift_monitor_detects_significant_shift PASSED
test_e2e.py::test_end_to_end_benchmark_execution PASSED
[PASS] 3 passed in 1.12s
[STATUS] EVALUATION_COMPLETE
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
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