AI Training & Evaluation
Human judgment your model can actually learn from.
SynapseSoft provides RLHF, model evaluation, red-teaming, and expert-generated training data for teams building and fine-tuning language models — produced by specialists qualified in the domain being evaluated.
The work
What human feedback actually does.
When a model answers well, that traces back to people who compared responses, ranked them, corrected them, and explained why one was better than another.
Those judgments are what a reward model is fit to, and the reward model is what steers the language model during fine-tuning. It follows that the quality ceiling of the finished model is set by the quality of the human judgment underneath it — which is why who does this work, and how carefully, matters more than how much of it you buy.
A generalist can tell you which of two answers reads better. Only someone who knows the field can tell you which one is right — that the code compiles but leaks a file handle, that the clinical reasoning skips a differential, that the contract clause does not mean what it appears to mean. That gap is the whole argument for expert evaluation.
Capabilities
What we can take on.
Engagements usually combine several of these. The rubric work is often the part that decides whether the rest is worth anything.
Preference Data & RLHF
Side-by-side response comparisons, rankings, and written rationales — the human judgment a reward model is fit to.
Model Evaluation & Benchmarking
Structured evaluation against rubrics you define, with per-criterion scoring and the disagreement data behind each result.
Red-Teaming & Safety Testing
Adversarial prompting to surface failure modes — jailbreaks, harmful completions, and reasoning that breaks under pressure.
Expert Data Generation
Original prompts, reference answers, and worked solutions written by people qualified in the field being covered.
Data Annotation & Labeling
Classification, extraction, and span-level labeling for text, code, and structured data against documented guidelines.
Rubric & Guideline Design
Turning a fuzzy quality goal into criteria annotators can apply consistently — usually the highest-leverage part of the work.
Multilingual Evaluation
Evaluation and data generation in languages beyond English, by speakers who use the language professionally.
Evaluation Infrastructure
Pipelines, tooling, and dashboards so evaluation runs continuously against your models rather than once per release.
Domain coverage
Panels matched to the field being evaluated.
We assemble a panel per engagement rather than routing work to whoever is free. If we cannot field the expertise your task needs, we say so.
Software Engineering
Code review, debugging traces, systems design, and agentic task evaluation
Data & Machine Learning
Model reasoning, statistics, experiment critique, and ML tooling
Mathematics & STEM
Proofs, quantitative reasoning, physics, chemistry, and engineering problems
Medicine & Life Sciences
Clinical reasoning review and scientific accuracy, by qualified practitioners
Law & Compliance
Legal reasoning, document analysis, and jurisdiction-specific accuracy
Finance & Accounting
Financial analysis, modeling critique, and regulatory interpretation
Writing & Linguistics
Instruction following, tone, factuality, and long-form coherence
Multilingual
Translation quality, cultural context, and non-English evaluation
How an engagement runs
From a capability gap to data that closes it.
- 01
Define the task
We start from what you are trying to move — a benchmark, a failure mode, a capability gap — and work backward to the data that would actually shift it.
- 02
Design the rubric
Criteria, edge cases, and worked examples, written so two qualified people applying them independently reach the same answer.
- 03
Assemble the panel
Specialists matched to the domain, screened against the rubric on a calibration set before they touch production data.
- 04
Produce and audit
Work runs in batches with gold-standard items seeded throughout, blind re-review on a sample, and agreement tracked per contributor.
- 05
Deliver and iterate
You get the data, the disagreement cases, and what we learned about the rubric — which usually sharpens the next round.
Quality & integrity
The parts that are easy to claim and hard to do.
Evaluation data is difficult to audit from the outside, which is exactly why these commitments are worth stating plainly.
Contributors work under their own identity
Every person on a panel is the person whose qualifications we matched to the work. We do not operate accounts on anyone else's behalf, and we do not put one person's name against another person's judgment.
No model-generated submissions
The value of this work is that a human produced it. Contributors may not submit AI-generated content, and we screen for it. It is grounds for removal.
Measured agreement, not assumed quality
Calibration sets before work starts, inter-annotator agreement tracked throughout, and gold-standard items seeded into live batches.
Documented provenance
For each item you can see which rubric version applied, the contributor's domain qualification, and whether it passed audit.
Confidentiality by default
Prompts, model outputs, and evaluation criteria are treated as your confidential material, under NDA and with access limited to the assigned panel.
Honest reporting
If agreement is low, a rubric is ambiguous, or a batch underperformed, you hear it from us before you find it in the data.
Questions
Common questions.
- What is RLHF, in practical terms?
- Reinforcement Learning from Human Feedback. People compare model outputs, rank them, and explain why one is better. Those judgments train a reward model, which then guides the language model toward responses people actually prefer. The quality ceiling of the finished model is set by the quality of that human judgment.
- How is this different from a crowdsourcing platform?
- Open microtask platforms optimize for volume and low unit cost. We assemble small panels of qualified specialists against a rubric designed for your task, and we report agreement and audit results. It suits work where being wrong is expensive — code, clinical reasoning, legal analysis, safety evaluation.
- Who actually does the work?
- Specialists from our network, matched to the domain and screened on a calibration set. They work under their own names and credentials, and you can see the qualification profile behind a panel before work begins.
- How do you keep quality consistent?
- Rubric calibration before production, gold-standard items seeded into live batches, blind re-review on a sample of completed work, and per-contributor agreement tracking. Contributors who drift are recalibrated or removed.
- Can you work in our tooling?
- Usually. We can work inside your annotation platform and export format, or stand up the pipeline ourselves if you would rather not build one. We will tell you which is the better use of your time.
- What size of engagement makes sense?
- A scoped pilot — one capability, one rubric, a bounded batch — is normally the right first step. It tells both sides whether the rubric holds and whether the data moves what you need it to move before anyone commits to volume.
Get started
Start with a scoped pilot.
One capability, one rubric, a bounded batch. It tells both of us whether the data moves what you need it to move before anyone commits to volume.