AHMAD BUKHARI / SYSTEMS00
Opening the decision system
Mapping the signal
Ten flagship systems / Verified public demos

Agentic AI & LLM Systems Specialist

A production-minded revenue and creator operations portfolio proving controlled multi-agent orchestration, evidence, typed APIs, human approval, evaluation, observability, replay, and cloud deployment.

Positioning backed by working systems

Python foundations with AI-assisted development across FastAPI APIs, LangGraph workflows, deterministic decision systems, data processing, and automated testing.

The controls are real: campaign objective, attribution window, source freshness, cash reconciliation, buyer language, stakeholder coverage, creative test validity, conversion lag, audience authenticity, rights, conflicts, budget concentration, locale, register, fault scenario, forecast inputs, consent, and speed-to-lead timing each change the computed output. Each project links to its live system, states whether the implementation repository is public or private, and publishes the architecture, API contract, evaluation evidence, and operating limits.

Creator Campaign Command project visual01 / Multi-agent campaign planning · Public demo

Creator Campaign Command

Five bounded roles turn a campaign objective and budget into an evidence-linked creator plan, then stop before outreach or spend.

Proof: LangGraph, FastAPI, 5 roles, 3 scenario shapes, objective-sensitive ranking, approval gate

Open live system ↗ Inspect GitHub ↗
Marketing Revenue Assurance project visual02 / Cross-system revenue assurance · Public demo

Marketing Revenue Assurance

Ten bounded specialists reconcile advertising delivery, CRM state, funnel movement, and collected cash, then propose a recovery plan without changing any external system.

Proof: LangGraph, FastAPI, 12 of 12 golden scenarios, 31 tests, 81.93 percent coverage, 25 Postman assertions, signed serverless approval receipts, Docker and PostgreSQL acceptance

Open live system ↗ Read technical brief ↗

Source: Private implementation repository

Deal Rescue and Forecast Truth project visual03 / B2B forecast control · Public demo

Deal Rescue and Forecast Truth

Twelve bounded stages compare seller confidence with exact buyer language, stakeholder coverage, and dated commitments, then stop at a sales manager decision.

Proof: LangGraph, FastAPI, 12 of 12 golden deal states, 53 tests, 83.97 percent coverage, 28 live Postman assertions, PostgreSQL checkpoint restart proof, 0 external mutations

Open live system ↗ Read technical brief ↗

Source: Private implementation repository

Creative Learning OS project visual04 / Creative measurement and learning · Public demo

Creative Learning OS

Eleven bounded stages separate attributable creative signal from sample, audience, placement, funnel, lag, fatigue, and data-quality confounds, then stop before publishing or changing spend.

Proof: LangGraph, FastAPI, 13 of 13 golden scenarios, 68 tests, 82.76 percent coverage, 18 evaluation measures, 34 production Postman assertions, PostgreSQL checkpoint restart proof, 0 platform mutations

Open live system ↗ Read technical brief ↗

Source: Private implementation repository

LanguageMix Studio project visual05 / Multilingual content operations · Public demo

LanguageMix Studio

Timed English scripts become distinct Urdu, Roman Urdu, or Arabic review packages with voice register, safety checks, and native-language approval.

Proof: FastAPI, 3 source scenarios, 3 locale routes, 3 registers, 27 meaningful combinations

Open live system ↗ Inspect GitHub ↗
Agentic Systems Evaluation Lab project visual06 / Independent evaluation · Public demo

Agentic Systems Evaluation Lab

A live black-box evaluator tests deployed systems for contracts, evidence, approval gates, boundaries, idempotency, and latency.

Proof: 3 target systems, 7 weighted checks, 4 baseline and fault scenarios, arbitrary URLs blocked

Open live system ↗ Inspect GitHub ↗
Content Performance Forecaster project visual07 / Pre-publish decision support · Public demo

Content Performance Forecaster

A reproducible historical baseline returns forecast ranges, confidence, cohort fallback, and input sensitivity before publication.

Proof: 500 licensed records, 400 training rows, 100 holdout rows, versioned ridge models, no LLM claim

Open live system ↗ Inspect GitHub ↗
Revenue Signal Graph project visual08 / Governed revenue operations · Public demo

Revenue Signal Graph

Seven bounded agents convert fragmented account evidence into an explainable qualification, speed-to-lead decision, and human-reviewed action proposal.

Proof: LangGraph, FastAPI, PostgreSQL path, 12 of 12 golden scenarios, 31 tests, 85.30 percent coverage, 18 Postman assertions, $0 replay inference

Open live system ↗

Source: Private implementation repository

Creator & Talent Campaign OS project visual09 / Creator and talent campaign control · Public demo

Creator & Talent Campaign OS

Twelve bounded stages test audience authenticity, campaign fit, conflicts, rights, safety, historical evidence, budget, and concentration, then stop at a campaign manager decision.

Proof: LangGraph, FastAPI, 13 of 13 golden scenarios, 70 tests, 85.38 percent coverage, 18 evaluation measures, 34 production Postman assertions, PostgreSQL checkpoint restart proof, 0 external writes

Open live system ↗ Read technical brief ↗

Source: Private implementation repository

Agentic Systems Gateway project visual10 / Governed model access and release evidence · Public demo

Agentic Systems Gateway

A deterministic policy gateway gives nine deployed systems free replay, visitor-owned NVIDIA NIM or OpenRouter access, signed receipts, and an evidence-qualified evaluation registry without exposing an owner-funded model key.

Proof: FastAPI, fixed provider allowlist, HMAC receipts, 85 tests, 18 of 18 evaluation measures, 40 production Postman assertions, PostgreSQL restart proof, persistent themes, 0 production 5xx

Open live system ↗ Read technical brief ↗

Source: Private implementation repository

Stack demonstrated in the deployed systems

  • Python 3.12, FastAPI, Pydantic v2, REST and generated OpenAPI
  • LangGraph for explicit state, interruption, approval, and resumption, with deterministic rules for scoring, evidence, limits, and safety
  • Postman collections, Pytest, GitHub Actions, Playwright and Vercel deployment
  • OpenTelemetry traces, Prometheus metrics, structured JSON logs, evidence records, latency, usage and approval state
  • Black-box evaluation with fault injection, idempotency checks, replay mode, uncertainty and input sensitivity

What the public proof does not claim

These verified portfolio deployments use synthetic or licensed public records. They do not use client credentials, private records, patient health information, live publishing, ad spend, messaging, or CRM write tools. LangChain, CrewAI, MCP, RAG, Kubernetes, Langfuse, LangSmith, and Sentry are not claimed as connected runtime components where they are not deployed.