Model agnostic
Swap models per task based on cost and performance without rebuilding the workflow. Reach models through OpenRouter or Bedrock, or host privately with vLLM so data never leaves your environment.
The AI Accountability Layer
Endeavor is an AI accountability platform. It is the operations and governance layer where mid-market enterprises prototype, evaluate, govern, and deploy first-party AI agents that automate real work — with auditable evidence at every stage.
Most enterprise AI never leaves the pilot
Mid-market firms are stuck in POC purgatory. Not because the models are weak, but because nobody can prove an agent is safe to trust. Every platform you already run — ERP, CRM, HRIS — now ships its own assistant, and none of them talk to each other.
Ad-hoc prompting does not create repeatable, auditable business processes. Prompt engineering is not innovation. What closes the gap is treating an agent the way you would treat any other production system: decomposed into tasks, tested against real cases, measured, governed, and monitored.
Infrastructure, not a companion
There are two philosophies of enterprise AI. One builds a witty, proactive assistant that makes autonomous decisions on your behalf. The other builds the ship's computer: zero personality, purely functional, executing only on authorised command, serving an entire crew rather than one person.
Endeavor is deliberately the second. It does not just use a computer — it encodes the business logic of the organisation.
| Dimension | Personal AI assistant | Endeavor |
|---|---|---|
| Primary goal | Executing personal tasks in a UI | Managing multi-departmental workflows |
| Interaction model | One-to-one companion | Central platform serving many teams |
| Agency | Makes autonomous decisions | Executes on authorised command |
| Governance | Scoped to one user's permissions | Standardised protocols and enterprise indexing |
| Interface | Conversational | Structured data and systems monitoring |
| Failure mode | Hallucinates an action | Guardrails stop it, and the log shows why |
The Accountability Cycle
Four stages that carry a business problem to a governed production agent, and keep it accountable once it is there.
1. Friction Discovery
Compass AI reads process documents and domain-expert interviews, then decomposes messy human workflows into discrete tasks, each with a business objective and KPIs attached from day one.
2. Agent Prototyping
Endeavor Studio is the workbench. A task is defined by its input and output schemas, model selection and parameters, prompt templates, MCP tool capabilities, and evaluation metrics. Multiple versions of the same task run side by side.
3. Scientific Evaluation
Test cases replace opinion. Domain experts score outputs directly in the platform using qualitative measures such as Likert scales and confabulation checks, alongside computed metrics. You choose the cheapest, simplest task definition that performs.
4. Production Monitoring
Every agent and every individual task is exposed as a secure API endpoint, callable in sequence from your own applications, with real-time token spend so you can calculate the exact ROI of each automated task.
Proven in production
90%
Less manual review time, supply chain risk analytics
75%
Higher accuracy on the same workload
33%
Less review time, cybersecurity vendor questionnaires
What makes Endeavor different
Cloud agnostic
Kubernetes on AWS, Azure, Google Cloud, or on-premises. Private namespace, bring your own keys, full data jurisdiction.
Accountability over hype
Evaluation, governance, and visibility are first-class, not add-ons. Every agent is auditable before it is trusted and monitored after it is deployed.
Built for the people who own the outcome
- Domain experts design, test, and supervise agents through repeatable scientific evaluation.
- IT governs the infrastructure while business owners govern each agent's instructions.
- Start with one high-friction task and scale without re-architecting.
- Human-in-the-loop scoring creates a clear path to green-lighting an agent.
- Every interaction is logged at user and agent level for a non-repudiable audit trail.
Frequently asked questions
What is Endeavor?
Endeavor is an AI accountability platform: an operations and governance layer that lets mid-market enterprises prototype, evaluate, govern, and deploy first-party AI agents. It sits over existing cloud and data platforms rather than replacing them.
How is Endeavor different from an AI assistant like a copilot?
A copilot is a personal assistant scoped to one application and one user's permissions. Endeavor is shared infrastructure: it orchestrates multi-step, multi-department workflows across systems, executes only on authorised command, and logs every step for audit. It has no personality by design.
Does Endeavor replace AWS, Azure, or Google Cloud?
No. Endeavor is the operations layer above them, bringing their capabilities together into a single governed business process. It runs on Kubernetes in your cloud or on-premises.
Do we need machine learning engineers to use it?
No. Endeavor is built by AI engineers but designed so domain experts can define, score, and supervise agents. Business stakeholders own the agent's instructions and success metrics; IT governs the infrastructure.
What does it take to run Endeavor?
A Kubernetes cluster and a PostgreSQL database. No GPUs are required to get started - GPUs are only needed if you choose to host models locally instead of using an API.