About
Expert judgment, built into how frontier AI is trained and evaluated.
Synthium Labs supplies the human expertise, realistic environments and independent evaluation that frontier AI teams need to improve capable, increasingly autonomous systems, and to know whether they have.
The problem
Capable models need better signal than they are given.
As AI systems take actions instead of only producing text, a failure stops being a poor answer and becomes a wrong action. The cost of weak training signal and weak evaluation rises with every step of autonomy.
The gap is concrete. It is a shortage of reliable training signals, of realistic environments, and of evaluation a team can trust.
The training signal is only as good as its judges
A model improves against the signal it is given. If labels come from reviewers who were never qualified or calibrated, or if disagreement is averaged away, the model learns the noise.
Agents act, and static data does not exercise that
Agents now work across many steps with tools. Training them takes runnable tasks with explicit completion criteria, not isolated questions and answers.
What cannot be measured cannot be reliably improved
Judging an agent by the wording of its answer misses whether the work was done. Checking the resulting state and behaviour, independently of the agent, does not.
The Synthium approach
Judgment, environments and verification, together.
Each is weak alone. Experts without environments judge static outputs. Environments without expert-written criteria reward the wrong behaviour. Evaluation without calibrated experts measures whatever is easy to measure.
Synthium combines them, so the standard of judgment that defines a task also checks the result, and the check does not depend on the agent being tested.
- Expert judgment. Qualified, calibrated people decide what good looks like and judge the work.
- Training environments. Realistic, runnable tasks where an agent acts and its work can be checked.
- Independent verification. Criteria written before scoring, checked apart from the agent.
- Where they overlap. A result a team can train on or decide with, and the reasoning behind it.
What we build
Four offerings. One foundation.
Data, Train, Agents and Red Team are related but distinct. Each can be engaged on its own. All four run on the same expert network, data operations and evaluation infrastructure.
Synthium Data
Acquire, license, curate and validate data for AI training and evaluation, with expert-led annotation and adjudication.
Explore Synthium Data- The problem it addresses
- The most valuable training signals often sit inside companies, with unclear rights and no record of where they came from.
Synthium Train
Runnable reinforcement-learning environments with independent verification, and expert-written post-training datasets.
Explore Synthium Train- The problem it addresses
- An agent needs somewhere realistic to act and a verifier it cannot see into. Static prompt-and-answer data cannot teach that.
Synthium Agents
Evaluation of agents and models on multi-step tasks, with execution-trace analysis of how each task was, or was not, completed.
Explore Synthium Agents- The problem it addresses
- A score does not say why an agent failed. Experts diagnose each failure, so the next change has something to aim at.
Synthium Red Team
Authorized, scoped adversarial evaluation of models and agents by domain experts.
Explore Synthium Red Team- The problem it addresses
- Unsafe behaviour is found by people who understand the domain. Every finding ships with evidence your team can reproduce.
Operating principles
Practices, not slogans.
Expert qualification
Contributors are assessed with tests relevant to the project, then trained, before they are given production access. We screen for the capability a task needs, not for availability.
Calibration
Gold-standard tasks, scored against your rubric, fix where the bar sits. Calibration shows who meets it and where re-training is needed, before anything is produced.
Adjudication
When reviewers disagree, the item goes to senior adjudication. We do not average disagreement away. Low agreement tells us to retrain, refine the rubric or recalibrate.
Provenance
Every unit of work can be traced to a contributor, a task version and a review chain. For data we may acquire or license, ownership, origin and permitted use are assessed before any commercial conversation.
Verifiability
Completion criteria and scoring rules are written down before work starts and checked independently. Environments are verified by state-based checks, and red-team findings ship with reproduction steps.
Quality arbitrage, not labour arbitrage.
Our contributor network draws on the depth of India's technical talent: software engineers, researchers, doctors, lawyers, finance professionals and multilingual experts. That depth lets us assemble an expert pod for a specific task. India is not where we outsource judgment, and we are not selling cheap labor. The product is calibrated expert judgment.
Evidence and credibility
What we can show, and what we cannot.
We would rather be exact about our evidence than impressive about it. This is what exists today, and in what form.
- Published
Method
How we qualify experts, hold the quality bar, run an engagement and protect data is described in full on Expertise, Quality, How it works and Security. These pages describe the system. They are not results.
- Illustrative
Worked samples
Where a service page shows a worked sample, such as an environment, a finding or an evaluation, it is synthetic and labelled as illustrative. It shows the shape of a deliverable. It is not customer output.
- In preparation
Research
Research lists the areas we are studying and notes in preparation. They are directions we are investigating, not published findings.
- Under NDA
Engagement results
Pilot results, qualification and calibration pass rates, reviewer agreement and rework rates belong to real engagements. They are shared in a pilot or under confidentiality, and customer names are not published. See Case studies.
- Not published
Team, certifications, metrics
This site does not publish team biographies, customer names, performance metrics or security certifications, and nothing here should be read as implying them. What we do and do not claim on security is on the Security page. If one matters to your engagement, ask, and we will say what we can share.
Next step
Bring the capability you're trying to improve.
Bring the problem and the quality bar. We'll scope whether we can staff and operate it, honestly, before any work begins. If you are an expert who wants to contribute, see our open roles.