© 2035 by leadmavenservices.com
How it Works
Define tasks, languages, eval rubrics, pass criteria, and SLAs.
Scope & Metrics
1
1–2 week pilot; align on rubrics, inter-rater reliability, and reports.
Pilot & Calibrate
2
Elastic teams, SOPs, and dashboards; hit throughput & quality targets.
Scale Production
3
Why Companies Choose Us
We don’t just label data.
We shape model behaviour.
Domain Expertise
Engineers & SMEs across software, data, security, & education (STEM)
Reproducible Human Judgments
Guideline-driven, reproducible judgments (not crowdsourced noise)
Built for Modern LLM Pipelines
Designed for modern LLM pipelines (SFT, RLHF, reward modelling)
Evaluation at Production Scale title
Scalable workflows aligned with OpenAI-style safety and eval standards
100+
Expert Evaluators
1M +
Code Results Reviewed
95%+
QA Accuracy
<24h
Turn Around Options
LLM Pre-Training & Post-Training Services
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Supervised Fine-Tuning (SFT)
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Instruction tuning & prompt-response datasets
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Preference ranking & comparison data
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RLHF / RLAIF data generation
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Reward model training support
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Hallucination detection & factual consistency checks
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Long-context and reasoning evaluation
Proven Impact
LLM Safety Team
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Need: Stress-test for jailbreaks + leakage.
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Approach: Red-team suite with seeded exploits & continuous regression.
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Outcome: 60% reduction in successful jailbreak patterns quarter-over-quarter.
EdTech Evaluations
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Need: Consistent grading for student code + feedback clarity.
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Approach: Prompt redesign + structured hints, partial-credit rubric.
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Outcome: 22% higher learner satisfaction; faster resolution times.
AI Data Platform
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Need: Validate 100k+ code generations/month across 6 languages.
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Approach: 40-person EITL pod, test cases + scoring schema, weekly error taxonomy.
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Outcome: 95%+ rubric adherence; 28% drop in critical errors in 6 weeks.
What Customers Say
A few words from our clients.
“The red teaming suite they developed uncovered vulnerabilities our internal team had missed. Their adversarial prompts and continuous regression testing made our model much more resilient.”
Head of Safety, LLM Lab
“We were struggling with inconsistent grading from our automated systems. The EITL team refined prompts, built rubrics, and ensured human validation. Our learner satisfaction scores jumped significantly.”
VP of Product, EdTech Startup
“Their expert-in-the-loop reviewers became an extension of our own engineering team. Code eval accuracy went up, release cycles sped up, and we finally had the confidence to scale our copilots.”
Director of AI Engineering, Global Platform