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A 200-Agent
Finance OS.
Built with Claude.

The most advanced AI finance operating system built for founders. Powered by 200 specialized agents — each focused on a single financial function.

Get free access → See it in action
200 Specialized agents
10 Financial functions
24/7 Real-time analysis
1 Operating system
What it actually does

Ten functions. Two hundred specialists.
One operating system for your money.

Each capability is powered by a dedicated team of agents that hand off context, validate each other's work, and deliver investor-ready output.

The constellation

All 200 agents. Click any one.

Each agent is a single-purpose specialist with defined inputs, outputs, and quality bar. Filter by category, search by name, or browse the full grid.

⌕
Live workflows

Watch the agents collaborate.

Three pre-built workflows that orchestrate dozens of agents in seconds. Click run on any one to see the chain of work — same as your founders will.

The outcome

What founders feel within 30 days.

01

⚡ Faster decisions

Move quicker. Make the right call — backed by always-current numbers.

02

📊 Cleaner numbers

Accurate, reliable, investor-ready. No more spreadsheet roulette.

03

🎯 Better capital strategy

Raise smarter. Deploy better. Know your terms before the term sheet.

04

🕒 Less manual work

Automate the complex. Keep your finance team focused on growth.

AgentOps — Live Agent Observability

📡 Live Trace Feed

📊 Session Metrics (24h)

Total Sessions2,847
Avg Latency1.4s
P95 Latency3.1s
Error Rate0.3%
Tool Calls12,284
HITL Escalations47
RAGAS GatePASS ✓

💰 Cost & Tokens

Cost (24h)£847
Input Tokens48.2M
Output Tokens12.4M
Cache Hit Rate67%
Cost/Session£0.30

🎯 RAGAS Quality Scores

Faithfulness0.94 ✓
Answer Relevance0.91 ✓
Context Precision0.89 ✓
Context Recall0.93 ✓
Hallucination Rate0.8%

🤖 Agent Health

All agentsHealthy
OrchestratorActive
Tool registryOnline
MCP serversConnected
Memory storeHealthy
MLOps / LLMOps — Model Lifecycle

🧠 Model Registry

claude-sonnet-4-5 PRODUCTIONPrimary
claude-haiku-4-5 ROUTINGFast path
claude-opus-4-5 SHADOWComplex
text-embedding-3-large RAGVectors

Automatic fallback routing. Versioned in MLflow. Prompt changes require RAGAS eval gate pass.

📈 Drift Detection

Faithfulness drift (7d)+0.02 stable
Latency drift (7d)+120ms watch
Output length driftWithin ±5%
Sentiment driftNo anomaly
Alert thresholdΔ>0.05 → PagerDuty

🔀 A/B Experiment Controller

Prompt v2.3 vs v2.4Running
CoT vs DirectStaging

Statistical significance (p<0.05) required before promotion.

🏪 Feature Store

Vector IndexPinecone
Dimensions3,072
Indexed Docs284K
Retrieval P9542ms

📦 Prompt Version Control

System promptsGit-tracked
Few-shot examplesVersioned
Eval datasetsDVC tracked
DevSecOps — Security-First CI/CD Pipeline

🚀 CI/CD Pipeline

🔍SAST — Semgrep + BanditPASS
📦SCA — SBOM + TrivyPASS
🧪Unit + Integration tests847/847
🎯RAGAS eval gate (≥0.92)0.94 ✓
🔐Secrets scan — GitleaksCLEAN
🐳Container scan — Grype0 CRITICAL
🚢Deploy → KubernetesDEPLOYED

🔐 Security Posture

RBAC — Role-based accessEnforced
API keys — HashiCorp VaultRotated 30d
mTLS — Istio service meshActive
PII scrubbing — NeMoActive
Audit log — ImmutableCloudWatch
Pen testQuarterly
SOC 2 Type IIIn progress
ISO 27001Compliant

🏗 Infrastructure as Code

TerraformCloud infra
HelmK8s workloads
ArgoCD GitOpsSynced
Kustomize overlaysdev/stg/prd

♻️ Rollback & DR

RTO Target<15 min
RPO Target<5 min
Blue/Green DeployActive
Auto-rollbackError rate >1%

📋 Regulatory Compliance

GDPR Art. 22 HITLEnforced
EU AI Act Art. 9Documented
NIST AI RMFMapped
ISO/IEC 42001Compliant
AI Observability — OpenTelemetry + Langfuse

🔭 Observability Stack

L1TracesOpenTelemetry → Jaeger
L2MetricsPrometheus → Grafana
L3LLM TracesLangfuse (self-hosted)
L4LogsFluentd → OpenSearch
L5AlertsAlertManager → PagerDuty

📊 SLO Dashboard

Availability SLO99.9% target
Current (30d)99.96%
Error Budget73% remain
P50 Response0.8s
P95 Response3.1s
P99 Response7.4s

🚨 Active Alerts

Latency P95Normal
Error rate0.3% ✓
Token budget84% remain
RAG recall0.93 ✓
Latency drift+120ms watch

🔬 Langfuse Trace Explorer

📈 Avg Span Breakdown

API Gateway12ms
Auth + RBAC8ms
RAG retrieval42ms
Guardrail check18ms
LLM inference1,240ms
Tool execution84ms
Total E2E1,452ms
Guardrails — Responsible AI Framework

🛡 NeMo Guardrails — Active Rails

✅ Human-in-the-Loop (HITL) Gate
All consequential actions require human approval before execution. Confidence <0.85 always escalates. GDPR Article 22 compliant — no fully automated consequential decisions.
🔍 PII Detection & Scrubbing
Microsoft Presidio + custom patterns. Names, emails, NI/SSN, card numbers scrubbed from all LLM I/O before logging. 47 entity types across 12 jurisdictions.
🚫 Toxicity & Hallucination Filter
NeMo topic rails block off-topic responses. Factual grounding check cross-references every claim against retrieved context. Hallucination >5% triggers human review queue.
⏱ Rate Limiting & Abuse Prevention
Per-user token budgets at API gateway. 10× anomalous usage triggers suspension + security alert. Cloudflare WAF DDoS protection.

📋 Audit Trail & Explainability

📝 Immutable Decision Log
Every AI recommendation logged: input context, retrieved docs, reasoning chain, confidence, model version, user ID, timestamp. 7-year retention for regulated decisions.
🔎 Explainability (XAI)
Every recommendation includes source citations, confidence intervals, alternatives considered, and limitation disclosures. SHAP attribution for structured ML models.
⚖️ Bias Monitoring
Fairness metrics tracked across protected characteristics. Disparate impact analysis monthly. EU AI Act Article 10 data governance requirements met.
🏛 Regulatory Mapping
GDPR Art. 5/22 · EU AI Act Art. 9/10/13/14 · NIST AI RMF · ISO/IEC 42001 · IEEE 7001 Transparency. Compliance evidence pack generated quarterly.
0.3%
Hallucination Rate
Target <2%
100%
HITL Coverage
Consequential acts
0
PII Leaks (30d)
Target: 0
A+
Security Grade
Mozilla Observatory
Multi-Agent Architecture — Mesh & Orchestration

🕸 Agent Mesh Topology

Orchestrator
Agent 1
Agent 2
Agent 3
Agent 4
Agent 5
Agent 6

Orchestrator decomposes tasks, routes to specialists, aggregates results, handles conflicts. All inter-agent communication via typed schemas. No agent takes external action without Orchestrator validation.

⚙️ Agent Patterns

ReAct — Reason + Act loopsAnalytical
Reflection — Self-critique cyclesHigh-stakes
Planning — Hierarchical decompositionMulti-step
RAG — Retrieval-augmented genKnowledge
HITL — Human-in-the-loopAll consequential
Tool Use — Function callingAll agents

🔄 Temporal.io Orchestration

Active Workflows2,847
HITL Signals Pending47
Retry PolicyExp backoff ×3
Saga PatternCompensating txns
Durable ExecutionCrash-safe ✓

📨 Kafka Message Bus

Topics47 agent topics
Throughput12K msgs/s
Consumer Lag<100ms
Schema RegistryConfluent
Dead Letter QueueMonitored

🔌 MCP Integration Layer

MCP — Data sourcesActive
MCP — CRM/ERPActive
MCP — Document storeActive
OAuth 2.0 authAll connectors
JSON Schema validationAll tools
Evaluation Framework — Continuous Quality Gates
0.94
Faithfulness
Gate ≥0.92 ✓
0.91
Answer Relevance
Gate ≥0.88 ✓
0.89
Context Precision
Gate ≥0.85 ✓
0.93
Context Recall
Gate ≥0.90 ✓

🧪 Eval Suite Composition

Golden dataset2,847 Q&A pairs
Unit evals (per agent)120–400 cases
Integration evals84 end-to-end flows
Adversarial probes47 jailbreak tests
LLM-as-judgeclaude-opus-4-5
Human eval cadenceWeekly 5% sample

🔁 Eval-Driven Dev Flow

1
Change proposed → PR opened
Automated eval suite runs against golden dataset in CI. Results posted to PR.
2
RAGAS gate enforced
All metrics must meet thresholds. Failure blocks merge.
3
Canary deploy (5%)
Langfuse online evals on live traffic. Drift alerts trigger auto-rollback.
4
Full rollout + monitor
Weekly human eval sample. Monthly RAGAS full re-run.
Infrastructure — Kubernetes · Scale · Resilience

☸️ Kubernetes Cluster

ClusterEKS / GKE / AKS
Node pools3 (system · app · GPU)
HPA targetCPU 70% → scale
KEDA triggersKafka consumer lag
Spot instances80% non-critical
Multi-AZ3 zones

💾 Data Architecture

PostgreSQL (RDS)Operational
Redis (ElastiCache)Session + cache
Pinecone / pgvectorVector search
S3 Intelligent TierDocuments
Kafka (MSK)Event streaming
Snowflake / BigQueryAnalytics DWH

💰 Cost Architecture

LLM API (Anthropic)~45% of AI cost
Vector DB~12% of AI cost
Compute (K8s)~28% of AI cost
Prompt cache savings−67% input tokens
Haiku fast-path saving−40% LLM spend
Est. monthly total£8–28K

🔁 Disaster Recovery

1
Primary failure detected (<2 min)
Route53 health check fails → DNS failover. Temporal promotes standby. Kafka MirrorMaker live.
2
DR validates (<5 min)
Smoke tests auto-run. PagerDuty alert to on-call. RTO target: 15 minutes.
3
Data reconciled (<15 min)
PostgreSQL read replica promoted. S3 cross-region lag <5min. RPO: 5 minutes.

📊 Capacity Planning

  • Baseline: 3 app nodes · 2 vCPU · 8GB RAM each
  • Scale trigger: Kafka consumer lag >10K msgs
  • Max scale: 20 nodes via KEDA + HPA
  • LLM concurrency: 50 parallel sessions managed
  • Vector search: Pinecone p1 → p2 at 500K docs
  • DB connections: PgBouncer pool (max 500)
Documentation — Deployment Guide & Runbook

🚀 10-Week Deployment Guide

1
Week 1–2: Data Foundation & Infrastructure
Deploy K8s cluster. Provision Temporal.io, Kafka, PostgreSQL, Pinecone. Connect source systems via MCP. Establish data governance and RBAC. Run baseline eval on golden dataset.
2
Week 3–4: Core Agents Live
Deploy first 3 highest-value agents. Wire HITL approval workflows in Temporal. Configure NeMo guardrails and PII scrubbing. Set up Langfuse tracing and RAGAS eval gate.
3
Week 5–7: Full Agent Mesh
Deploy all agents. Configure Orchestrator routing. A/B test prompt variants. Enable drift detection. Train end-users on HITL workflow.
4
Week 8–10: Production Hardening
Pen test + SAST/DAST scan. Load test 10× baseline. Configure PagerDuty. Compliance review (GDPR, EU AI Act). Produce runbook. Go-live.

🏗 7-Layer Platform Stack

L7PresentationReact · Next.js · SSO
L6API GatewayFastAPI · OAuth2 · WAF
L5OrchestrationTemporal.io · LangGraph
L4Agent RuntimeNeMo · RAGAS · Tools
L3Model + ToolsClaude API · MCP servers
L2Data + IntegrationKafka · PostgreSQL · Redis
L1ObservabilityOTel · Langfuse · Grafana

🔌 Integration How-To

  • MCP server per data source (REST/GraphQL/gRPC)
  • OAuth 2.0 service account per enterprise system
  • Kafka topics per agent capability namespace
  • Schema registry for typed message contracts
  • Data lineage via OpenLineage → Marquez
  • Webhooks for real-time event ingestion
  • dbt + Airflow for batch data refresh

👤 RBAC User Roles

ViewerRead dashboards
AnalystRun queries + export
ApproverHITL decisions
ManagerConfig + agents
AdminFull platform
AI EngineerModels + prompts

IdP via Okta/Azure AD. MFA enforced for Approver+.

📞 Incident Runbook

  • High latency (>5s): Check Langfuse trace → vector store → LLM API status
  • RAGAS gate fail: Roll back last prompt change → notify AI engineer
  • Error spike: Circuit breaker → fallback to previous version
  • PII leak: Suspend session → DPO notification within 24h
  • HITL queue backup: Escalate to senior approver
  • Cost overrun: Auto-throttle → route to Haiku