/ services
Test it. Train it. Know it works.
AlgoQX helps teams evaluate their agents and models honestly, improve them through post-training, and build the verifiers and environments that make both trustworthy.
/ what we do
Four ways we help.
Most work starts with evaluation: you cannot improve what you cannot measure honestly.
- 01 / test your agent
Agent and model evaluation
Find out whether your agent or model actually does the job, on the tasks your users bring, not just on a demo.- Task suites built from your real workflows
- Executable checks where they fit, rubric-based human review where they do not
- Held-out sets kept apart from anything you tune on
- A failure report with concrete examples, not a single score
- 02 / train it
Post-training
Improve an open model on your task with human feedback (RLHF), verifiable rewards (RLVR) or preference optimization, with evaluation built in from the start.- Feedback and data design for your task
- Reward and verifier design, and audits for loopholes
- Training runs on open-weight models
- Before and after comparison on held-out tasks, with the conditions stated
- 03 / environments
Learning environments and verifiers
Executable tasks with controlled state and repeatable checks, for coding, SQL and bounded tool use.- Sandboxed environments your training and evaluation can share
- Verifier reviews: what passes that should not, and why
- Versioned tasks so results stay comparable
- 04 / collaborate
Research collaboration
Work with us on an open question in post-training or evaluation, from a shared problem statement to a write-up.- A scoped question and a plan to test it
- Shared code and data where you allow it
- Results written up with their limits
/ systems
The engineering underneath.
Every service relies on practical systems work: efficient inference, reliable rollout execution and clear resource boundaries. We bring that with us, so experiments run on infrastructure you can inspect.
/ how it works
From first message to hand-over.
Conversation
You describe the task, the model or agent, and what is not working. No confidential data needed yet.
Scope
We agree what success means, which data and access are involved, and what you receive at the end.
Pilot
A small first slice, usually an evaluation, so both sides see real results before going further.
Build or train
The main work, with regular check-ins and the evaluation running alongside it.
Hand over
Code, data, evaluation suites and a report you can rerun without us.
/ talk to us
Start with a conversation.
Tell us about the agent or model and the problem. We will tell you honestly whether we can help.