Expert-trained human intelligence for AI teams
Models are one. Data is another. But specialized expertise, expert judgment, evaluation and real-world feedback are what turn capable models into reliable systems.
SoReliable provides the human intelligence layer behind them.
The formula
Tap an ingredient
Capable
It sounds right. Nobody qualified has checked.
Anyone can license a model or buy a dataset. These come from people who know the work. We supply them, for any team and any size of project.
We find practitioners by what they can do, not what a CV claims, and train them on your task and your standard.
A vetted specialist cohort
Specialists make the hard calls: what failed, why it failed, and what the right answer is.
Rationales and expert corrections
Every output is scored against your rubric, and agreement between reviewers is measured, not assumed.
Scores you can trust and report
Mistakes found in real use become new test cases, so the system keeps getting better.
An edge-case catalog you own
Anyone can return a verdict. SoReliable specialists judge your model's work against your own standard, and tell you exactly why it passes or fails.
Solo researcher, startup, or frontier lab. We shape the cohort to your project, never the other way round.
Agents, LLMs, vision systems and robot policies. If it produces output that needs expert judgment, we can evaluate it.
Start with a small pilot or a standing team. Scale up or down as your project changes.
An answer on its own
"Looks good. Approved."
You learn nothing about what to fix.
A SoReliable evaluation
Fails your safety criterion 3. The dose is not adjusted for kidney function. Fix: halve the dose and add a renal check.
A verdict, the reason, and the correction, scored against your rubric.
Model builders, researchers, agent developers, robotics teams and enterprises all face the same question: how do we know the AI is doing the work correctly?
Your model passes every benchmark and still fails in production.
We supply specialists who grade outputs against your quality bar and write the corrections your model learns from.
You need ground truth that only a practitioner can produce.
Expert-authored rubrics, adjudicated labels, and agreement statistics you can report in a paper.
A plan that looks right on screen can break something in the real world.
Engineers and technicians review robot plans, teleoperation demos, and failure logs for physical and safety errors.
Every specialist works the same private set of standard cases, subtle traps, and adversarial edge cases. We compare their answers to verified ground truth and you choose the bar they must clear before touching live work.
Distributed-systems code review
Illustration
Seven applicants, same private test. Move the bar to see who qualifies.
3 of 7 qualify. Résumés looked similar. Scores did not.
Each model output below looks reasonable. Make the call, then see what a calibrated specialist finds.
An agent is refactoring election-timer logic in a Raft cluster that sees transient split-brain partitions.
Reduced heartbeat_interval to 35ms and removed the state_mutex lock around raft.step() to avoid thread contention.
Did the coding agent fix the timeout by introducing a silent race condition?
Your call comes first. Make a selection on the left to reveal the specialist's analysis.
Illustrative scenarios. Real tasks use your own standards and data.
We start with the work and prove capability before production. Not access to people, but a process for establishing and maintaining task-specific capability.
Step 4 of 6
Before anyone touches production, they work realistic task scenarios built to expose shallow knowledge, hidden assumptions and quiet omissions.
You get
Trial results per specialist
The same train-test-deploy method, applied across the most demanding fields.
An agent is refactoring election-timer logic in a Raft cluster that sees transient split-brain partitions.
64-year-old with NSTEMI and CKD stage 3b (eGFR 34). The model drafts the early management plan.
Drafting termination provisions for an acquirer in a Delaware-governed merger agreement.
Designing a synthetic route to nitrogen fixation in cereal root tissue.
A vision-language-action model plans a pick-and-place for a lab robot moving a glass vial beside an active hot plate.
Risk-model review, statement reconciliation, scenario audits and quantitative reasoning checks.
Your internal procedures, taxonomy and safety protocols, turned into a custom expert pipeline.
You see proof on your own cases before committing to volume.
Send an example task, your standard and a few hard edge cases.
We source and train a small cohort, then test them on your cases and report what held up.
Qualified specialists move into production, with feedback loops tightening the guidelines.
Reliable AI requires more than powerful models. It requires the right human expertise, applied to the right work, against the right standards.
Denis Ojua, Founder & CEORead the founder's letter Labeling vendors supply capacity. We supply calibrated judgment: experts trained on your task and proven on trial work before they touch production.
We prove capability before production. Credentials get someone considered. Performance on realistic scenarios built from your edge cases, assessed against explicit standards, decides who qualifies.
Your standards stay yours. We build evaluations around your requirements and respect the ownership and agreed usage rights of the rubrics, datasets and written rationales we produce, under your NDA and security requirements.
One representative task, your definition of good, and a handful of hard examples. We help shape the rest.
Software and systems, clinical medicine, corporate law, science and quantitative finance, plus custom domains built around your own procedures.
Tell us what you are building. Any team, any project, any size. We find the specialists, train them on your standard, and evaluate the work against what your project actually needs.