Evaluate AI on the work that actually matters.
We build evaluation datasets, testing frameworks and training workflows that help teams understand how AI systems perform on real-world tasks.
From RAG and coding systems to autonomous agents, multimodal models and model comparisons, we design evaluations around the tasks your AI actually needs to perform.
RAG Evaluation
Measure retrieval precision, context recall, citation faithfulness, and answer relevance across domain-specific knowledge bases and documents.
Model Training
Task-specific supervised fine-tuning (SFT), domain adaptation, instruction curation, and preference alignment pipelines for enterprise workloads.
Dataset Preparation
High-signal benchmark creation, hard-negative mining, deduplication, schema normalization, and multi-annotator agreement rubrics.
Coding Evaluation
Deterministic sandboxed test execution, multi-file refactoring validation, compilation checks, and downstream regression pass rates.
Agent Evaluation
Multi-turn tool execution fidelity, environment state transitions, transient error recovery loops, and trajectory step efficiency.
Multimodal Evaluation
Cross-modal reasoning, OCR table extraction accuracy, chart interpretation fidelity, and visual grounding across complex documents.
Model Comparison
Controlled, side-by-side empirical benchmark harness measuring task accuracy, token economics, latency bounds, and error boundaries.
Scope & Rubric Formulation
Define task boundaries, pass/fail criteria, acceptable error margins, and deterministic assertion rules before looking at outputs.
Dataset Curation
Construct versioned, balanced test sets with representative domain inputs, hard-negatives, and edge cases.
Controlled Execution
Execute candidate models inside isolated sandboxes with frozen parameters, temperature controls, and structured logging.
Failure Classification
Classify every failure into concrete taxonomies (retrieval omission, prompt confusion, schema break, reasoning loop).
Targeted Remediation
Deliver actionable engineering recommendations: prompt tuning, chunking adjustments, tool parameter schemas, or fine-tuning datasets.
Regression Defense
Re-run the frozen baseline suite against the updated checkpoint to ensure fixes do not create silent behavioral regressions.
| Evaluation Metric | Baseline (v1.1) | Candidate Model | Delta | Mechanism |
|---|---|---|---|---|
| Context Precision | 88.2% | 91.4% | +3.2% | Deterministic ground truth |
| Context Recall | 86.9% | 88.7% | +1.8% | Source reference sweep |
| Faithfulness (Groundedness) | 94.4% | 92.1% | -2.3% | Model judge + citation check |
| Latency (p95) | 1,300ms | 1,120ms | -180ms | Deterministic harness timing |
While the candidate model reduced response latency by 180ms, faithfulness regressed by -2.3% on multi-hop compliance queries where documents contained conflicting effective dates.
Do not promote candidate model to production for tier-1 compliance queries. Refine prompt instructions on date disambiguation and execute regression sweep on conflict subset.
Deterministic API sandboxing, reproducible test harnesses, containerized runtimes, git patch application, and multi-file code diff parsers.
Deterministic regex assertions, edge-case boundary testing, negative test vectors, fault injection scenarios, and continuous regression suites.
Task-specific fine-tuning, prompt calibration, LLM-as-a-judge rubric tuning, multi-model comparison, and inference cost optimization.
Ground-truth synthesis, hard-negative mining, schema normalization, deduplication, and version-controlled evaluation repositories.
Scope an Evaluation for Your Workload
Connect directly with our engineering team to discuss your model architectures, target tasks, and evaluation criteria under mutual NDA.