Better data in,
better AI out
Generate synthetic datasets with edge cases, curate from production traces, and auto-optimize prompts - all versioned, all connected to every stage of your pipeline.
|
Input (gpt-4o-mini) |
Opener v3 (gpt-4o-mini) |
personalization_v1
|
personalization_v2
|
tone_check
| |
|---|---|---|---|---|---|
| Can you review whether your application is... | Thank you for your request regarding the RAG-based chatbot... | Error | Failed | Passed | |
| Can you check the demo request. I see... | Thank you for your demo request. I understand you're... | Error | Failed | Passed | |
| I see that the development stage of... | Thank you for the summarization application focused on... | Passed | Passed | Passed | |
| Could you review whether your internal... | Thank you for your demo request regarding your RAG-based... | Passed | Passed | Passed | |
| As you're in the chatbot development stage... | Thank you for the RAG-based chatbot concept for students... | Passed | Passed | Passed | |
| Can you check whether your application is... | Thank you for your demo request regarding the multi-step... | Failed | Failed | Passed | |
| Please verify the knowledge base integration... | Hi Sneha, thank you for your inquiry about the vector DB... | Passed | Passed | Passed | |
| Review the summarization pipeline for... | Hi Karan, I've reviewed your summarization pipeline and... | Passed | Failed | Passed | |
| What's the latency impact of adding guardrails... | Hi Priya, the latency impact depends on the guardrail... | Passed | Passed | Failed | |
| How does the agent handle multi-turn context... | Hi Ankit, the multi-turn context window retains up to... | Error | Failed | Passed | |
| Can you evaluate the response accuracy for... | Hi Neha, I've run the accuracy evaluation across your... | Passed | Passed | Passed |
The message lacks specific details from the prospect's submitted form data.
- • While the prospect's name "Amit" is used, no reference is made to their multi-step agent, production stage, or internal workflows.
Not just storage -
datasets that work for you
Don't wait for production data. Generate synthetic datasets with realistic edge cases, adversarial inputs, and multi-turn conversations from day one. Go from zero to evaluating in minutes, not weeks.
Try synthetic generationAdd columns that compute themselves - execute code, call APIs, query vector databases, run classification, or extract entities. Your dataset stays enriched and up-to-date without manual work.
See dynamic columnsRun optimization algorithms directly on prompts stored in your dataset. Six optimizers - including ProTeGi, PromptWizard, and GEPA - automatically find better prompt variants using your evaluation criteria.
Explore optimizersEvery edit creates a new version. Pin experiments to specific dataset states, compare performance across versions, and trace every evaluation result back to the exact data it ran against.
See versioning From day one to
production scale
Bootstrap evaluation from scratch
No production data yet? Generate synthetic datasets with edge cases, adversarial inputs, and multi-turn conversations - start evaluating in minutes.
Turn production failures into tests
Funnel real traces, hallucinations, and error feeds into versioned datasets. Every production failure becomes a regression test.
Auto-optimize prompts against data
Run six optimization algorithms on prompts stored in your dataset. Find better variants automatically using your own evaluation criteria.
Red-team with adversarial suites
Build datasets of prompt injections, jailbreaks, and encoding tricks. Test your guardrails before attackers find the gaps.
CI/CD regression testing
Pin datasets to specific versions and run evaluations on every PR. Catch regressions before they merge - not after they ship.
Multi-modal agent evaluation
Build datasets with text, audio, JSON, and structured data columns. Evaluate agents that work beyond just text-in, text-out.
From zero to evaluating
in three steps
Generate or import your data
Generate synthetic datasets with edge cases in minutes, import from CSV/JSON/Hugging Face, or funnel production traces directly into a versioned dataset.
Enrich with dynamic columns
Add columns that auto-compute - run code, call APIs, query vector DBs, classify, or extract entities. Annotate with your team using built-in review workflows.
Evaluate, optimize, repeat
Run experiments pinned to dataset versions. Auto-optimize prompts with six algorithms. Feed results back into the dataset - every iteration makes your agent better.
Powering teams from
prototype to production
From ambitious startups to global enterprises, teams trust Future AGI to ship AI agents confidently.