You will get a custom multi-agent AI document analysis pipeline.
Project details
FinClose AI is a production-grade, multi-agent AI system that automates the most labour-intensive tasks in your corporate financial close cycle — 100% offline, zero data egress.
Built on a LangGraph 4-agent pipeline (Planner → Retriever → Executor → Critic), it mirrors the accounting AI initiatives at leading enterprise companies with full support for Oracle Fusion GL, Blackline reconciliations, HFM variance reporting, and SOX internal controls.
What makes it different from generic AI tools is its responsible AI architecture: a deterministic numeric claim verifier that catches LLM hallucinations before they reach the compliance gate, a 5-dimension confidence breakdown (not a black-box scalar), and an append-only SOX audit trail with SHA-256 input hashing — ready for external auditor PBC review.
Supported workflows: account reconciliation, journal entry generation, variance analysis, anomaly detection, accrual review, and close checklist management. All outputs are APPROVED, FLAGGED, or REJECTED by an independent Critic agent with a rule-based SOX gate — no LLM judgment involved in compliance decisions.
Your financial data never leaves your machine.
Built on a LangGraph 4-agent pipeline (Planner → Retriever → Executor → Critic), it mirrors the accounting AI initiatives at leading enterprise companies with full support for Oracle Fusion GL, Blackline reconciliations, HFM variance reporting, and SOX internal controls.
What makes it different from generic AI tools is its responsible AI architecture: a deterministic numeric claim verifier that catches LLM hallucinations before they reach the compliance gate, a 5-dimension confidence breakdown (not a black-box scalar), and an append-only SOX audit trail with SHA-256 input hashing — ready for external auditor PBC review.
Supported workflows: account reconciliation, journal entry generation, variance analysis, anomaly detection, accrual review, and close checklist management. All outputs are APPROVED, FLAGGED, or REJECTED by an independent Critic agent with a rule-based SOX gate — no LLM judgment involved in compliance decisions.
Your financial data never leaves your machine.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AIOps, Anomaly Detection, Natural Language Generation, Natural Language Understanding, Time Series AnalysisAI Development Language
PythonAI Tools
Hugging Face, StreamlitAI Models
LLaMAWhat's included
| Service Tiers |
Starter
$500
|
Standard
$1,200
|
Advanced
$2,500
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | - | ||
Detailed Code Comments | |||
Image Upscaling | - | - | - |
MLOps | - | - | |
Model Deployment | - | - | |
Model Documentation | - | ||
Model Monitoring | - | - | |
Model Testing & Optimization | - | - | |
Model Tuning | - | - | |
Natural Language Processing | - | ||
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | - | ||
Setup File | |||
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$100 - $350
Additional Revision
+$150Frequently asked questions
About Zachary
AI Engineer | Machine Learning & Generative AI
Henderson, United States - 3:58 pm local time
My background is unusual for an AI engineer: I ran a cryptocurrency hedge fund for 7 years, have 30 years of Bloomberg Terminal experience, and actively flip houses. That means when I build financial analysis agents, deal scoring engines, or document intelligence pipelines, I understand the business problem — not just the tech stack.
Recent work includes FinClose AI, a 4-agent LangGraph pipeline automating enterprise financial close with Pydantic validation, JWT/RBAC auth, LangSmith telemetry, and a FastAPI REST layer. I also built a Bloomberg Terminal emulator with real-time WebSocket streaming and a real estate deal analyzer that outputs GO/NO-GO decisions from live MLS data.
I work with Python, LangGraph, LangChain, FastAPI, Claude API, n8n, RAG pipelines, Pydantic, Ollama, and Docker. If you need something built right — not just built — let's talk.
Steps for completing your project
After purchasing the project, send requirements so Zachary can start the project.
Delivery time starts when Zachary receives requirements from you.
Zachary works on your project following the steps below.
Revisions may occur after the delivery date.
Environment setup & data review
Review client requirements, configure the Python environment, install Ollama and the chosen LLM model, and connect to the client's data layer (or configure the mock enterprise data for the Starter tier).
Pipeline deployment & testing
Deploy the LangGraph agent pipeline, configure the Streamlit UI, run all 6 supported task types against sample data, and validate SOX audit trail output. Share results for client review.