Set the standard your AI has to meet. Then teach it what your experts know.
Eben Green records a baseline for every model, holds it to your standard before a person sees it, and turns expert feedback into approved context.
- Fall with ER transport2.1 s
- Medication refusal1.8 s
- Shorthand input2.4 s
- Unverified diagnosis1.6 s
- Behavior incident2.0 s
- Morning hygiene1.4 s
“It guessed what ‘pt refused AM meds’ meant. It should ask, or quote the staff member’s words.”
Works with your model, your docs and your pipeline
Who checks your AI before your customers do?
Most teams learn what their AI gets wrong from the people they built it for. Eben Green holds every model to your standard first, puts your own experts in front of it next, and keeps a record of who checked what, and why it passed.
Teams use Eben Green to
See how it works- Lock a ground truth for every model
- Gate models before experts see them
- Let experts review on your own rubric
- Give your app approved context
- Prove the lift against ground truth
Everything between your model and your customers.
Expert review, eval suites and benchmarks in one private workspace, with a record behind every score.
A locked ground truth
Every model gets a baseline: each eval tried a few times at fixed settings, kept with its spread. Run it from your CI, next to benchmarks like MMLU and BFCL function calling.
Locked per model. Three tries per eval, temperature 0.
A gate from the docs you have
Turn a PDF, Word file, Confluence page or promptfoo config into your standard. A model below the pass mark never reaches your experts.
Experts review on your rubric
Testers join by invite and rate each reply under your brand. Reviewers approve, reject or correct every one.
Refunds are available for 30 days after purchase. Your order is 45 days old, so I can offer store credit instead.
Approved context, by API
Repeated fixes become suggested rules, each linked to the reviews behind it. You approve them, and your app pulls them with one call.
GET /v1/contextProve the lift
Re-run with context on, the same tries as the baseline. A lift only counts when it clears the baseline's spread, and evals that flip are reported on their own. Compare a fine-tune the same way.
Also train your own model
Corrections export as fine-tuning rows and preference pairs, ready for OpenAI, Axolotl or TRL.
Six steps, one audit trail.
The full walkthrough01Connect
Point Eben Green at your model, or run everything from your own code with one API key.
02Ground truth
Record a locked baseline for every model: each eval tried a few times at fixed settings.
03Gate
Hold each model to your pass mark. Below it, nothing reaches your experts.
04Review
Experts join by invite and rate real replies. Reviewers approve, reject or correct each one.
05Learn
Repeated fixes become suggested rules. You approve them, and your app pulls them by API.
06Prove
Re-run with context on. A lift counts only when it clears the baseline's spread.
Built for the fields where AI has to be right, and the teams shipping it.
Healthcare, finance, legal and other expert fields
The people who know the work decide what good looks like, and you keep the record of who signed off.
- A record of who reviewed, corrected and approved every reply
- Each workspace keeps its data separate, and people join by invite only
- Role checks on every page, API call and data query
- Your model and judge run on your keys, encrypted at rest
- Your logo, colors and address for every tester
Teams shipping AI
Know a change is better before customers do.
- Six ways to test: prompt and rubric, chat, quiz, media, API and collection
- Exact checks on every reply, and your own judge model where it takes judgment
- Unlimited evaluations on every plan
See your AI through your experts’ eyes.
Book a 30-minute demo, or start a workspace and set your model’s ground truth today.