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Industries · Fintech & Financial Services

AI for finance teams that need an audit trail.

Finance runs on long documents and careful decisions. I build agents and pipelines that do the reading, extracting and structuring, and that pause for a human before touching anything sensitive, with every step traceable.

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Where AI pays off

What I build for teams like yours.

  • 01

    Earnings and filings analysis

    Transcripts and PDFs turned into structured, analyst-ready reports.

  • 02

    Research agents

    Agents that search, read and summarize, with approval before sensitive lookups.

  • 03

    Financial document extraction

    Figures and fields extracted from statements and reports into your systems.

  • 04

    Internal knowledge assistants

    Policy and procedure Q&A with citations.

What makes it hard

The constraints are the project.

The model is rarely the problem. These are the requirements that decide whether a system ships, and they are designed in from the first week.

  • Human approval

    Graph-level interrupts before agents access sensitive data or act.

  • Traceability

    Every tool call, input and output logged for review.

  • Accuracy

    Structured outputs and validation instead of free-text guesses.

  • Data control

    Deployment in your environment with least-privilege credentials.

Proof

Relevant case studies.

Fintech & Capital Markets5 stages
  1. 01Ingest
  2. 02Extract
  3. 03Summarize
  4. 04Structure
  5. 05Deliver

Stage 1, Ingest: PDF and transcript extraction. Stage 2, Extract: Figures, guidance, signals. Stage 3, Summarize: Specialized summarization agent. Stage 4, Structure: Report-structuring agent. Stage 5, Deliver: Client-ready report.

01Earnings Intelligence Crew

Financial Intelligence Multi-Agent Pipeline

A CrewAI multi-agent pipeline that reads earnings-call transcripts and financial PDFs, splits the analysis across specialized agents and produces structured, client-ready reports.

Specialized CrewAI roles
Multi-agent
Unstructured financial inputs
PDF + calls
Structured client output
Report-ready
Read case study
agent-run · langgraph + mcp
HITL

  1. Assess Q3 exposure for portfolio Alanggraph · state

  2. web_search("Q3 earnings guidance")mcp · ok

  3. read_docs("risk-policy.pdf")mcp · ok

  4. get_financial_data(account="A-17")awaiting approval

    Sensitive tool. Execution paused by a graph interrupt.

    ApproveReject

state checkpointed · resumes exactly here after a decision

Illustrative interface · client data never shown
02MCP Agent Platform

Agentic Orchestration Platform with LangGraph and MCP

A stateful agent platform on LangGraph that reaches live tools through the Model Context Protocol, remembers context across sessions and pauses for human approval before any sensitive action runs.

Approval gates on sensitive actions
HITL
Checkpointed multi-session memory
Durable
Plug-in tool federation
MCP
Read case study
Enterprise Knowledge Management5 stages
  1. 01PDF
  2. 02Image
  3. 03Clean
  4. 04OCR
  5. 05Structure

Stage 1, PDF: Scanned document intake. Stage 2, Image: Page-to-image conversion. Stage 3, Clean: Preprocessing for OCR accuracy. Stage 4, OCR: Text extraction. Stage 5, Structure: Vector-ready JSON.

03Document Intelligence Pipeline

OCR-to-RAG Document Ingestion Pipeline

A five-stage pipeline that converts scanned PDFs into clean, structured, vector-ready JSON, opening up archives that search and RAG could not read before.

Processing stages
5
Unstructured to structured
Scan → JSON
Vector-store output
RAG-ready
Read case study

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FAQ

Fintech & Financial Services: common questions.

Can agents access our financial systems safely?

Yes. Tools get least-privilege permissions, sensitive calls sit behind human approval gates, and every action is logged, so the agent can propose but a person decides.

How accurate is extraction from financial PDFs?

Extraction uses structured schemas with validation, and accuracy is measured on a labeled sample of your documents before rollout.

Which frameworks do you use for finance agents?

LangGraph for stateful agents with approval gates, CrewAI for role-based analysis crews, and the Model Context Protocol to connect data sources cleanly.

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