Introducing Mercor Enterprise AI

At Mercor, we’ve spent years on the frontier working with AI labs to accelerate model capabilities through human expertise. We see firsthand how raw intelligence compounds daily. However, enterprises still haven’t seen that same compounding value in their own AI deployments. What’s the bottleneck?

Stop guessing about your agents

Most enterprise agent deployments follow the same pattern: a team guesses at what the agent should do, hand-writes prompts, manually configures tool calls, and hopes it performs as intended. When it doesn't, they guess again. And again. This is why enterprise AI stalls.

Not because the models aren't smart enough, but because your agent development approach is backwards.

Here's the guesswork loop most enterprises are stuck in:

They guess where to start. Companies don’t know which agents to deploy because no one has the full picture. The teams closest to the work know which tasks are tedious, but only see their slice of a cross-functional, multi-step web. The people who own the function see the end-to-end process, but are a step removed from where real friction lives. So someone picks a use case that sounds promising, and the team runs with it, without evidence of where agents will actually create value.

They speculate on how agents should behave. The agents they do build rely on improvisation: hand-tuned prompts, ad hoc tool selection, and configurations based on what one person thinks the workflow should look like — not how the highest performers actually do the work.

They assume agents work as intended. Errors compound because there's no clear way to tell whether an agent's output is acceptable, good, or quietly degrading. The bar becomes "it seems to work," but teams don’t know what’s failing or what could be better. They discover failures weeks later through customer complaints or downstream breakdowns, and spend more time repairing damage than the agent saved.

They improve agents on vibes, not verification. There's no systematic way to improve agents over time. Updating workflows means painstaking manual reviews, rewriting prompts from scratch, and repeating the whole cycle.

If you're guessing at any of these steps, you're guessing about your agents. No amount of model intelligence closes that gap on its own.

Introducing the Mercor Enterprise AI Platform: Groundwork for your agents

Breaking the guesswork loop requires the opposite approach. You start by understanding how your work actually gets done. Then, you programmatically convert that understanding into agent behavior, and continuously measure output against quality. The gap between the output and the desired quality becomes the feedback signal that improves the agent over time. Together, these are the groundwork that separates agents that compound in value from agents that stall.

The Mercor Enterprise AI platform delivers these three core capabilities:

1. Know where and what to build: understand organizational context

Solid groundwork starts with evidence. We map your workflows by understanding how your people actually work — screen-level workflow capture, extraction from internal wikis and application system logs, and AI-led employee interviews that surface the institutional knowledge living in people's heads. The judgment calls, the edge-case handling, the "we do it this way because of X" reasoning that never makes it into documentation.

We capture all of it as structured, machine-readable data, so you know exactly where agents will create value and what they need to do it well.

2. Teach agents how to behave: programmatically translate context into agent behavior

Understanding context is only valuable if you can systematically convert it into agent behavior. We programmatically translate organizational context into agent behavior specs and quality guardrails. Instead of hand-tuning prompts, we generate a robust set of evaluations and success criteria from how your top performers work, and then automatically optimize agent behavior against them. Agents are deployed in hours rather than days, and at a higher baseline quality than manual specification can achieve.

3. Learn continuously: measure agent output against defined standards

Agents without a quality signal fail silently, and fixing them is manual. We catch errors in real time using quality verifiers that compare every output against your organization's definition of "good." When an agent's confidence is low, we flag it for human review before it causes damage. When corrections come in — from automated detection or human feedback — they feed directly back into the agent's behavior specs and organizational context.

Every failure becomes a permanent improvement.

This closes the remaining gap: you stop guessing whether agents are working and how to fix them — because quality is measured continuously and corrections are systematic.

Tools built on frontier AI infrastructure

This platform evolved from the same technologies we've pioneered to help the world’s leading frontier AI labs organize and structure human expertise to teach their models. The tools we use to capture expert behavior, build evaluation criteria, and systematically close performance gaps have been tested at scale across the most advanced AI development pipelines.

Now we apply that same technology to your organization.

We deliver the groundwork through a set of discrete, composable tools designed to scaffold your agents on any orchestration platform:

Organizational context graph: Structured map of how your organization actually works, built from data integrations with your organization’s systems and tools (e.g. ticketing platforms, CRMs, communication tools, codebases, knowledge bases, internal docs), application traces, team workflow capture, and AI-moderated interviews that surface institutional knowledge in people’s heads.

Agent Specification Engine: Human-readable, machine-executable agent specs translated from your context graph. Instructions are derived from how your best people actually work.

Quality Guardrails: Task-output scoring functions that compare agent output against expert-defined quality thresholds and automatically gate outputs that fall below your standards for human review.

Continual Learning Harness: Machine- and human-led feedback loops that automatically update agent behavior based on evals — task objectives, agent behavior, and output quality scoring functions — that pinpoint where and why your agents fail. Every correction makes the system better.

Agentic Workflow Data: Proprietary datasets of completed tasks and reasoning traces across economically valuable domains — law, finance, HR, accounting, software engineering, market research, competitive analysis, and more — so your agents don't start from scratch.

The modularity means we can deploy the right combination of components for your specific challenge — capturing context for a single workflow, deploying quality guardrails on agents you've already built, or running the full pipeline end to end.

Why Mercor

Three things set us apart.

We've proven this with frontier AI labs. We are a leading partner to frontier labs for organizing and deploying human expertise to improve model performance. The infrastructure we use to capture expert behavior, build evaluation criteria, and systematically close performance gaps is battle-tested at billion-dollar scale. We understand what actually matters for strong agent performance at a deeper level than anyone else — because we've been defining and measuring it for years.

We've deployed this for ourselves. Mercor runs highly sophisticated, self-improving agents at scale — including AI-driven hiring processes that evaluate millions of candidates for our expert network. This isn't theoretical. We've iterated these systems to a level of quality that our competitors cannot demonstrate.

We understand self-learning. We are the best in the world at teaching models to improve using benchmarks. We’ve spent years working with labs and developing benchmarks like APEX to measure whether AI models can perform economically valuable work in law, finance, and software engineering. Through that work, we’ve built technology that pinpoints exactly where and why an agent fails. We deeply understand the common patterns behind agent failures: confusing implications with grounded facts, insufficient output verification, incomplete tool access, and more. That same expertise and technology is the foundation of everything we’ve built. Without measurable, specific corrections, no amount of context helps. Your agent will keep making the same mistakes.

Your best people become coaches to the agents they work with every day. The agents remember and operate by your gold standards, continuously learning from real evidence of what great work looks like.

Build on groundwork, not guesswork.