Asset Analyzer — Multi-Agent AI Decision Platform

Many business decisions require multiple experts

Evaluating suppliers, reviewing claims, processing invoices, assessing customers — these activities involve multiple departments, manual reviews, and fragmented sources.

The result is a process that is:

⏳ Slow 💸 Expensive 📉 Difficult to scale 🔀 Inconsistent

↓ Check the Multi-Agent AI Decision solution below ↓

Asset Analyzer — Asset Research Platform

A single ticker — BTC, ETH, SOL — triggers five parallel specialist sub-agents. Each runs its own focused research loop with dedicated tools and a scoring rubric. A synthesis node aggregates findings into a composite-scored markdown report with a Buy / Hold / Avoid signal.

Input

→

5 Specialist Agents

→

Synthesizer

→

Scored Report

The Outcome

Transparent, repeatable, and auditable recommendations — faster decision-making, improved consistency, and significantly reduced operational effort.

Business Applications

The same architecture powers decisions across industries — real examples below.

📄

Document Processing

Real-Life Example

An accounting firm receives supplier invoices by email. The system extracts invoice details, validates vendor information, checks VAT compliance, and either posts the invoice automatically or routes it to a finance employee for approval.

OCR Agent Validation Agent Compliance Agent Decision Agent

Reduced manual data entry Faster invoice processing Improved compliance Lower operational costs

🛡️

Insurance Claims

Real-Life Example

An insurance broker receives a vehicle damage claim. The system reviews submitted photos and documents, validates policy coverage, checks for fraud indicators, and prepares a recommendation for a claims specialist.

Claim Review Agent Fraud Detection Agent Policy Validation Agent Decision Agent

Faster claim handling Improved fraud detection Better customer experience Consistent decisions

💬

Customer Support

Real-Life Example

A software company receives hundreds of support emails per week. The system categorizes incoming requests, searches product documentation, drafts responses, and escalates only complex cases to support engineers.

Intent Agent Knowledge Agent Escalation Agent Response Agent

Reduced response times Increased support capacity Higher satisfaction Lower support costs

🎯

Sales & Lead Qualification

Real-Life Example

A consulting company receives inbound leads through its website. The system enriches company information, evaluates lead quality, estimates deal potential, and drafts personalized outreach messages for the sales team.

Lead Research Agent Qualification Agent Opportunity Agent Outreach Agent

Improved conversion Better prioritization Increased sales efficiency Higher revenue potential

🏢

Enterprise Decision Support

Real-Life Example

A manufacturing company evaluates a new supplier. The system researches supplier performance, assesses supply chain risks, validates compliance requirements, and generates a recommendation package for procurement leadership.

Research Agent Risk Agent Compliance Agent Executive Recommendation Agent

Better strategic decisions Reduced operational risk Increased transparency Faster executive reviews

📦

Procurement & Vendor Evaluation

Real-Life Example

A logistics company evaluates new transportation partners. The system reviews financial data, compliance documentation, service history, and produces a risk-adjusted recommendation before onboarding.

Supplier Research Agent Financial Risk Agent Compliance Agent Recommendation Agent

Reduced vendor risk Faster procurement cycles Better compliance Improved supplier quality

👥

Human Resources & Recruitment

Real-Life Example

An HR department receives hundreds of applications for a technical role. The system evaluates candidate fit, highlights strengths and gaps, and prioritizes applicants for recruiter review.

CV Analysis Agent Skill Matching Agent Risk & Compliance Agent Recommendation Agent

Faster hiring processes Better candidate matching Reduced recruiter workload Improved hiring quality

📚

Internal Knowledge Management

Real-Life Example

Employees ask questions such as "What is our travel reimbursement policy?" and receive verified answers sourced directly from internal company documentation — no ticket, no waiting.

Document Discovery Agent Knowledge Extraction Agent Policy Validation Agent Response Agent

Faster info retrieval Reduced support burden Better productivity Better knowledge use

Asset Analyzer — Technical Details

Specialized AI agents collaborate, share context, and produce transparent, auditable recommendations — demonstrated through financial analysis, applicable to any complex business workflow.

asset-analyzer takes a single cryptocurrency ticker — BTC, ETH, SOL, anything CoinGecko knows — and produces a deeply-researched markdown report scored 0–100 across five independent dimensions. Each dimension is owned by a dedicated sub-agent with its own focused skill prompt, search budget, and scoring rubric.

The five specialists are On-Chain Analytics, Tokenomics, Sentiment & Momentum, Technical Analysis, and Fundamental Analysis. They run concurrently, then a synthesis step weighs their findings, flags cross-dimensional convergence and divergence, and writes the final composite report.

Skills as source of truth

Each sub-agent's behaviour is defined in a SKILL.md file loaded as its system prompt. Tweak the markdown, re-run — no code edit needed.

Provider-agnostic LLMs

Built on LangChain's init_chat_model — the same graph runs on Anthropic, OpenAI, Google, or local Ollama models picked per role via env vars.

Parallel by construction

Five sibling edges from discovery to the sub-agents place all five in the same Pregel superstep — they actually run concurrently, not in a loop.

Composable scoring

Each sub-agent emits a 0–100 score across five sub-dimensions. The synthesizer combines them into a composite score with an A–F grade and a Buy/Hold/Avoid signal.

What does LangGraph do here?

LangGraph is a graph-based orchestration framework on top of LangChain. You declare nodes (LLM calls, tool calls, or Python functions) and edges (how state flows between them). The runtime is a Pregel-style scheduler: in each "superstep" every currently-runnable node executes in parallel.

Parallel fan-out / fan-in. Five edges from discovery to sub-agents and back into synthesize are all the wiring needed to run specialists concurrently and join their results.
Structured shared state. A typed AnalyzerState dictionary flows through the graph. Each node writes to its own key, so concurrent writes never collide.
Subagents as react loops. Each sub-agent is a create_react_agent — an LLM that calls search tools until it has enough evidence, then emits its final report.

No live demo. This page intentionally doesn't expose a hosted endpoint — a single end-to-end run hits external LLM and search APIs that carry real per-request cost. Clone the repo, point it at your own API keys, and run locally — a full analysis takes 2–4 minutes and costs cents on the cheap-OpenAI preset.

Sample output — Solana (SOL)

The report below is the actual unmodified output of one run of the pipeline. Source: reports/CRYPTO-ANALYSIS-SOL.md.

CRYPTO-ANALYSIS-SOL.md generated by asset-analyzer

Loading sample report…