Arash Nicoomanesh
Bridging Strategic Business Intent with Adaptive Agentic Systems
Autonomous Multi-Agent Orchestration & Cognitive Planning
Advanced Reasoning Patterns & Fine-Tuning Optimization
Industrial Time-Series Forecasting & Recommendation Engines
Beyond the Hype of Expensive Chatbots
The Black Box
Probabilistic WrappersStandard chatbots and hardcoded workflows are fragile cost centers. They create technical debt and require constant human supervision to handle edge cases.
> User: "Process invoice #4492"
> Agent: Guessing API payload...
> FATAL: Max recursion depth reached.
> Hallucinated parameter: 'amount_null'
> - Unbounded Action Space
- Probabilistic Guessing
- Zero Blast-Radius Containment
The Glass Box
Deterministic State MachinesTrue value requires moving from automated processes to autonomous reasoning, backed by strict physical boundaries and mathematically auditable execution.
> Orchestrator: DAG Received.
> Shield: OPA Policy Check [PASS]
> Worker: Executing Step 1 (Idempotent)
> HTTP 200: Execution Committed.
> Audit Hash: 0x8f92a4...
> - Constrained Execution Graphs
- Policy-as-Code Guards (OPA)
- Immutable Audit Trails
Architecture Philosophy
Generative AI builds prototypes; governed state machines build platforms. We engineer neuro-symbolic architectures that cross the Planning Rubicon—isolating probabilistic reasoning from deterministic execution to deploy fault-tolerant, mathematically auditable digital workforces.
Deterministic Core
Enterprise autonomy requires absolute reliability. We treat the LLM as an untrusted guest within a rigid state machine, ensuring critical actions are governed by deterministic logic.
Read: Why "Chatbots" Fail →Stochastic Range
Intelligence is useless without constraints. We architect reasoning engines that explore solutions creatively, distilling output into strict deterministic execution graphs.
Read: The Engine of Reasoning →Multi-Agent Scale
A single agent is a prototype; centralized orchestration is a platform. We scale isolated bots into parallel, DAG-driven sub-agents managed by a central orchestrator.
Read: The 2026 Roadmap →Agentic Solutions
Real problems, strict constraints. Engineering autonomous systems built to survive and scale in production.
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.
View on GitHub →# --- 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
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.
View on GitHub →# --- 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
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.
View on GitHub →# --- 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
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.
View Case Study →# --- 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...
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.
View on GitHub →# --- 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
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.
View Case Study →# --- 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
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.
View Case Study →# --- 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
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.
View on GitHub →# --- 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
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.
View Case Study →# --- 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
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.
View on GitHub →# --- 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 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.
View on GitHub →# --- 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
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.
View on GitHub →# --- 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
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.
View on GitHub →# --- 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
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.
View on GitHub →# --- 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
The Blueprint
AI Strategy & Advisory
De-risk your AI investment with a comprehensive technical architecture, cost model, and execution strategy
The Forge
Custom Agentic Prototype
Transform your validated blueprint into a production-grade agentic prototype. We engineer the deterministic skeleton, stateful control loops, and pre-commit verification gates required to cross the Planning-Rubicon.
The Nexus
Agentic Deployment & Scale
Transition your prototype into a fault-tolerant agentic system. We implement Policy-as-Code guardrails, temporal state management, and token-level AgentOps for zero-trust production autonomy.
Generative AI & LLM Engineering
Determinism, Neuro-Symbolic Logic and Optimization
Moving beyond fragile API wrappers, we engineer robust, high-stakes LLM infrastructure built for survival in enterprise production environments. We bridge the critical gap between probabilistic text generation and strict neuro-symbolic logic, ensuring your AI systems execute with absolute predictability.
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.
# --- STEP 1: PERCEPTION & SIMULATION ---
[PERCEIVE] Ingesting patient genome (3.2B base pairs).
[TOOL_INVOKE] Cas9_Microservice -> Binding prediction...
└─ Candidate A: Efficiency 99.1% (Primary)
└─ Candidate B: Efficiency 84.5% (Fallback)
# --- STEP 2: DETERMINISTIC PLANNING ---
[PLANNER] Intent matched. Compiling execution DAG...
[GOVERN] Intercepting node: commit_design(candidate="A")
# --- STEP 3: POLICY-AS-CODE (OPA) PRE-FLIGHT ---
[DENY] 🛡️ POLICY_VIOLATION (rule: strict_off_target_toxicity)
└─ Critical Risk: "0.4% off-target match with 'TP53' (Tumor Suppressor)."
# --- STEP 4: DAG RE-COMPILATION ---
[RE-PLAN] Reverting to Candidate B. Re-running policy check...
[PASS] Zero critical off-target matches detected. Node approved.
# --- STEP 5: STATELESS EXECUTION & AUDIT ---
[EXECUTE] Irreversible write: finalize_design(candidate="B").
[AUDIT_LEDGER] Immutable trace cryptographically signed.
[STATUS] SAFE DESIGN LOCKED...
