How is Wajo different from Portkey or Langfuse?

Portkey and Langfuse track API costs for apps you build. Wajo covers developer tools (Claude Code, Cursor, Copilot), connects spend to outcomes, and implements routing fixes in your environment — not just a dashboard.

What does a Wajo AI spend audit cost?

Our standard 2-week AI Spend Audit is $15,000 fixed, with 50% on signing and 50% on delivery. Includes spend analysis by tool/developer/feature, waste identification, implemented routing rules, and a 30-day projection.

CTOs and VP Engineering at companies spending $10K–$100K/month on AI tools who cannot connect that spend to business outcomes.

Wajo — AI Agent Operating Layer | Connect AI Spend to Outcomes

Core TechnologiesT

Agent Verification & Evaluation

Behavioral benchmarks and adversarial eval suites. Agents verified before deployment, monitored continuously for drift.

Self-Improving Flywheels

RL training pipelines built from real enterprise trajectories. RLHF, DPO, and constitutional AI techniques applied at every deployment cycle.

Human-in-the-Loop Infrastructure

Greenlight routing between autonomous action and human review. Configurable trust thresholds, approval queues, full audit trails.

What We're Building W

Vertical Agent Solutions

Personal agents, domain expert agents, and agent marketplace SKUs — deployed across verticals, reusable by design.

Software Factories, Agent-Native

Engineering organizations running on measured, self-improving agents. The software factory re-instrumented for the agentic era.

Enterprise Data, Frontier Lab Ready

Human-annotated RL trajectories from real enterprise workflows. Anonymized, outcome-labeled, packaged for frontier model training.

Vertical Case StudiesV

Case Study

Errand Agents that Get Shit Done

Agent RL, context engineering, and action research combined into a personal agent that guarantees task completion — not assistance. Closed loops. Zero drift. Built on fundamentals of outcome verification, flywheeling, and human↔agent collaboration research.

Case Study

Coworking Team of SWE Agents

70% productivity gains when AI-native engineering teams transition to agent-native. Self-improving SWE flywheels that compound sprint over sprint. The research is in. The question is when you make the switch.

Interested? C

Early access. Limited engagements.

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Making agents work reliably for your enterprise.

Agent Verification & Evaluation

Self-Improving Flywheels

Human-in-the-Loop Infrastructure

What We're Building W

Interested? C