Reasoning, agentic coding & STEM evaluation teams

Advanced Reasoning, Agentic Coding and STEM Evals

A dedicated team of technical specialists who evaluate reasoning, generated code, tool use, and multi-step agent behavior by running it, not by reading it.

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01Why Hugo

Managed Teams, Measured Results

100+
Domain, technical, and language specializations
10K+
Managed specialists across the globe
98%+
Quality performance across key programs
90%+
Team retention across programs

02What the team does

Evaluation That Executes, Not Just Reads

Sandbox & Environment Verification
Isolated execution environments, dependencies, and configurations validated, so an evaluation measures the model, not the setup.
A failed test from a broken environment looks exactly like a failed model.
Code Execution & Unit Testing
Generated code run, outputs validated, and unit-test and pass@k results verified against expected behavior.
Reading code tells you it looks right. Running it tells you it is.
Reasoning Trace & Logic Auditing
STEM specialists check intermediate steps for mathematical errors, false logic, and flawed conclusions that final-answer scoring misses.
A right answer reached by a wrong path is a failure waiting for a new prompt.
Agent Trajectory & Tool-Use Evaluation
Repository navigation, terminal commands, tool calls, error recovery, and multi-step behavior assessed across whole trajectories.
Agent failures hide in the middle of a trajectory, not at its end.
Reward Hacking & Failure Taxonomy
Cases where the model passes the test but misses the point, cataloged into a failure taxonomy your team can train against.
A pass rate without a failure taxonomy tells you what, never why.
Expert Code & Security Auditing
Senior engineers assess code quality, algorithmic efficiency, architecture, and security beyond pass/fail.
Tests don’t catch the vulnerability that passes them.

03Who it’s for

Built for Teams Shipping Models That Code and Reason

Frontier labs, coding-assistant and agent products, and research teams who need evaluations that hold up on real repositories and real problems.

Build Your Agentic & STEM Evals Team

Built for

Coding models & agents
Model evaluation leads
Post-training for reasoning
STEM research
Developer tools
Safety & red teams

The team you get

Software engineers who run and review the code
STEM specialists in mathematics, physics, and the sciences
Agent-trajectory reviewers trained on your tools
A QA and calibration lead who owns your standards
A team lead who owns throughput and reporting

04How we hold quality

Quality Is a Process, Not a Promise

Every Hugo team runs on written guidelines, a calibration set, and measured agreement. These are the mechanisms behind this one.

Environment errors counted as model errors: Every sandbox verified before an evaluation batch, with environment failures logged separately from model failures.
Graders who can’t follow the reasoning: Evaluators matched to the domain and tested on a calibration set of solved problems before production.
Rubrics that reward the wrong thing: Rubrics written with you, failure modes cataloged as they appear, and graders re-calibrated when the rubric changes.
Benchmark contamination: Held-out tasks written and reviewed in-house, with duplicate and overlap checks against public benchmarks.

A Team, Not a Queue

Every program gets a team lead who owns the workflow, a QA and calibration lead who owns the standards, and specialists matched to the work, trained together and kept together.

Team lead: workflow owner
QA & calibration lead: quality and standards
Specialists matched to workflow complexity
90%+ team retention across programs

05How it works

From First Call to a Live Team

Hugo sources, trains, assembles, and manages the team around your workflow, tools, and quality bar, then keeps managing it.

Define the Workflow and the Team
We map the task, the tools, the rubric or taxonomy, the volume, and the quality bar with you.
Recruit and Vet the Specialists
Hugo sources and tests the people the work needs, from 10K+ managed specialists across 100+ specializations.
Train on Your Tools and Edge Cases
The team learns your platform, your guidelines, and a library of edge cases, and is tested on them.
Calibrate Before Production
A calibration set, agreement measured, and the guidelines tightened until the team converges.
Launch, Manage, and Scale
The team goes live with reporting per batch. Hugo manages performance, re-calibration, and scale from there.

Built with Speed, Designed for Quality

We can stand up specialized AI ops teams faster than traditional hiring — typically within days to weeks, depending on the workflow and complexity.

Getting Faster

We’re continuously developing pre-trained agents and reusable workflows to reduce the ramp time as we scale.

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06Managed team vs crowd

What a Dedicated Team Changes

Unmanaged Crowds

Fragmented, transactional, and hard to calibrate.

Code read, not run
The final answer scored
A pass rate

Hugo AI Operations Teams

Connected, managed, and built for long-term performance.

Code executed in a verified sandbox
Every reasoning step audited
A pass rate and a failure taxonomy

Need a more reliable human operations model?

Build a dedicated AI operations team aligned to your workflow.

Build Your Agentic & STEM Evals Team

07Trust & security

Security and Operational Control for Sensitive AI Workflows

AI operations involve sensitive data, from proprietary outputs and source code to customer information. Hugo provides secure, quality-controlled workflows that protect data without sacrificing performance.

08FAQs

Agentic & STEM Evals FAQs

Answers to common questions about how Hugo builds and manages a dedicated evaluation team.

Running the model’s code and tool calls in a verified sandbox, checking unit-test and pass@k results, reviewing the whole trajectory for navigation, error recovery, and tool use, and cataloging the failures.
Software engineers and STEM specialists across 100+ specializations, matched to the domain, tested on a calibration set, and calibrated on your rubric before production.
Yes. Tasks and reference solutions are written and verified in-house, with duplicate and overlap checks against public benchmarks, so your evaluation stays uncontaminated.
Every environment is verified before a batch runs. Setup and dependency errors are logged on their own, so your pass rate reflects the model.
Yes. The team works inside your sandboxes, harness, and rubric, and delivers results in the shape your pipeline expects.
Typically within days to weeks, depending on the domains and the tooling. Hugo recruits and vets the engineers, trains them on your rubric, and calibrates them before production.