AI Evaluation
Structured evaluation assets designed to measure model behavior, identify failure modes and provide evidence for informed AI decisions.
1. Evaluation Framework
2. Test Datasets
3. Scoring Rubrics
Structured evaluation methodology, objectives, test criteria and assessment protocols.
Curated test cases and evaluation datasets aligned to defined use cases and risk areas.
Clear scoring criteria and evaluation guidelines for consistent human or automated assessment.
4. Benchmark Results
5. Failure Taxonomy
6. Findings & Report
Structured performance results showing model behavior across defined evaluation dimensions.
Categorization of observed failures such as hallucination, instruction-following issues, retrieval errors and other defined failure modes.
A documented report summarizing results, significant findings, evidence and recommended next steps.
AI Data Annotation & RLHF
High-Precision Annotation & RLHF Deliverables
Human-reviewed datasets and quality-controlled annotation outputs supporting AI model development, evaluation and alignment workflows.
1. Annotation Guidelines
2. Annotated Datasets
Clear task definitions, labeling rules, edge-case guidance and detailed instructions ensuring consistent annotator execution across teams.
Structured, quality-reviewed datasets formatted for immediate integration into model training, fine-tuning or validation pipelines.
3. Consensus & Audit Logs
4. RLHF Preference Datasets
Inter-annotator agreement metrics, verification records and quality assurance reporting detailing label distribution and confidence.
Calibrated RLHF preference pairs and ranking datasets supporting model alignment, reward modeling and supervised fine-tuning.
Document Intelligence
Document Intelligence Deliverables
Production-ready extraction schemas, evaluation parser suites and verified training corpora for enterprise document processing.
1. Extraction Schemas & Rules
2. Benchmark Collections
3. Accuracy Audit Reports
Validated JSON schemas, field mapping specifications and extraction taxonomy rules tailored to complex document sets.
Representative ground-truth collections of multi-page unstructured documents for parser verification and regression testing.
Granular precision and recall evaluations per extraction field, table structure and document layout variation.
Clear scope. Transparent pricing. No surprises.
Our pilot project assignment starts from $ 499/- (USD)
Our every engagement is scoped before work begins. Pricing depends on the scope, complexity, number of models or test cases, evaluation requirements, and final deliverables. We provide a clear proposal and quote before starting the work.
Discuss your evaluation, annotation or document intelligence requirements with our senior practitioners.




