The intelligence layer for frontier AI.
Build better models and agents with expert-curated data, production-grade reinforcement-learning environments, and rigorous evaluation.
Have data to license? Discuss a data partnership
Solutions
What your team can build with Synthium
From the data that informs a model to the environments that train it and the evaluations that test it, Synthium supports distinct stages of frontier AI development.
Synthium Data
Data acquisition, licensing, curation and expert-led annotationAcquire, license, curate, and validate data for AI training and evaluation.AddressesValuable data whose rights and origin are unclear, and raw data nobody has reviewed.DeliversExpert-reviewed datasets, with their rights and provenance assessed.Explore Synthium DataSynthium Train
RL environments and expert-generated post-training datasetsBuild realistic RL environments and expert-generated post-training datasets.AddressesAgents need a realistic place to act, and a verifier they cannot see into.DeliversAn environment specification, tasks and a verifier, and separately expert-written datasets.Explore Synthium TrainSynthium Agents
Agent and model evaluationEvaluate agent and model performance on multi-step, real-world tasks.AddressesA score that cannot say why an agent failed.DeliversScores, and an expert diagnosis of each failure.Explore Synthium AgentsSynthium Red Team
Adversarial testing and safety assessmentDiscover, reproduce, and assess safety failures and adversarial weaknesses.AddressesUnsafe behaviour that only domain experts know to probe for.DeliversSeverity-rated findings, each with reproduction steps.Explore Synthium Red Team
Evidence
What a pilot delivers.
We do not publish client names or invented metrics. What we can show is exactly what happens to a single task, and what you receive at the end.
What happens to one task
Task
Scope, context and the definition of done are fixed before anyone starts. An evaluator who has to guess what good looks like is guessing.
Artifact
Scope and a definition of done, written down.
Execution
We keep the trace, not just the final answer: intermediate steps, tool calls, code changes. Most failures are only visible in the path taken.
Artifact
The trace: steps, tool calls and code changes.
Rubric scoring
Written criteria, agreed in advance, so two reviewers looking at the same work are answering the same question.
Artifact
Written criteria, agreed before scoring.
Peer review
A second expert sees the work and the rubric, not the first reviewer's answer. A review that starts from someone else's conclusion is not a second opinion.
Artifact
An independent second verdict.
Adjudication
Where reviewers diverge, a senior expert decides and the reasoning is written down. Averaging two contradictory scores produces a number that describes neither.
Artifact
A decision, with its reasoning written down.
What a pilot delivers
Evidence you can check, not figures to take on trust.
Where applicable to the engagement, a pilot produces the following. We do not publish client names or invented performance numbers.
Where did the data come from?
- Provenance recordsWhere each record came from and on what basis it is used.
- Dataset documentationWhat a dataset contains, how it was processed, how to read it.
Who judged the work, and how were they checked?
- Qualification resultsHow reviewers were assessed before they touched your work.
- Calibration resultsHow closely reviewers matched the gold standard.
What did they conclude, and how firmly?
- Reviewer agreementWhere independent reviewers agreed, and where they did not.
- Evaluation reportScores with written rationale, against the rubric you approved.
Security
Boundaries on every engagement.
Nothing is public, nothing is shared across projects, and nothing outlives the engagement without your say-so. We do not claim certifications we have not earned.
- Access controls
- Project-level data segregation
- Contributor confidentiality and NDAs
- Secure storage
- Data retention and deletion
- Customer data isolation
- Need-to-know contributor access
- Contractual retention and deletion
Get started
Three ways into Synthium.
Every engagement starts as a pilot, so the standard is checked on your work before capacity grows.
AI labs and model teams
Build, train, and evaluate AI systems against real work — with expert intelligence and verifiable outcomes.
- Expert-curated data
- RL environments
- Post-training datasets
- Agent and model evaluation
- Adversarial testing
Organizations with data
Explore a data partnership built around rights, provenance, and clearly agreed permitted use.
- Rights review
- Provenance
- Permitted use
Expert contributors
Put your expertise to work on challenging problems in frontier AI development.
- Engineering
- Science
- Finance
- Legal
- Medicine
Foundation
One shared foundation. Four ways to work with us.
AI can generate answers. The harder problem is teaching it to do real work. Our Expert Network, Data Foundry and Evaluation Infrastructure power every Synthium engagement: the three things that problem depends on, and not separate offerings.
Expert Network
Real decisions require domain knowledge.
Qualified, calibrated domain specialists who judge the work.
How experts are sourced and calibratedData Foundry
The most valuable training signals often exist inside companies.
Selected real-world data, turned into assets with provenance.
How data becomes training and evaluation assetsEvaluation Infrastructure
A model cannot improve against a system that cannot reliably measure it.
Independent checks and documented scoring criteria, agreed before anything is scored.
The quality system
Sourcing is selective: we acquire or license selected data after a rights and provenance review, and nothing enters the pipeline by default.
Why teams choose Synthium
The same task, handled two different ways.
Who does the work
SynthiumPeople who could perform the work being evaluated — engineers, researchers, domain specialists.
Generic teamAnnotators trained to label, pointed at any domain.
How they are sourced
SynthiumRecruited against the expertise one project demands, before it starts.
Generic teamA general pool, matched to the project afterwards.
Whose standard applies
SynthiumYour rubric and your gates. You define what good means; we operate to it.
Generic teamTheir workflow and their quality bar, applied to your work.
How capacity moves
SynthiumThroughput expands and contracts against one calibrated bar.
Generic teamScaling up means a second vendor, and a second standard.
Your unreleased data
SynthiumNDAs before production access, project-level segregation, need-to-know only.
Generic teamBroad access across a shared, general-purpose workforce.
Our advantage
Built from India's deepest technical talent pools.
India holds one of the deepest concentrations of engineers, researchers, doctors, lawyers, finance professionals and multilingual experts in the world. That depth is the raw material. It is not where we outsource work, and we don't sell low-cost labor: we sell expertise, applied through screening, calibration and layered review.
Our storyQuality arbitrage, not labour arbitrage.
- Recruited against the expertise one project demands
- Assessed with project-relevant tests, not résumés
- Calibrated against gold-standard tasks and your rubric
Next step
Tell us the capability you're trying to improve.
Describe the task, the expertise it needs and the standard it has to meet. We will tell you honestly whether and how we can help.