Science Behind Production-Grade
AI Systems

Amazon Nova Pro delivers 65% lower cost than GPT-4o at comparable accuracy — verified across the CRAG dataset with 2,000+ queries.

When ground truth doesn't exist — medical domains, proprietary data — we generate unbiased pseudo-labels via cross-model consensus scoring for fair evaluation.

Nova Pro is 22% faster than GPT-4o on average — measured end-to-end including retrieval and inference across live workloads.

Semantic chunking with metadata filtering achieved 60% accuracy vs 25% for fixed chunking — with o1-preview and Nova Premier benchmarked across latency and cost dimensions.

End-to-end test harness for GenAI pipelines. Run regression suites across model versions, prompts, and retrieval configs without boilerplate.

Unicode standardization that recovers 25–40 NDCG@1 points lost to encoding artifacts — proven critical for medical and multilingual workloads.

Structured evaluation for zero-ground-truth domains. Cross-model consensus scoring generates unbiased pseudo-labels at scale.

Automated PDF and markdown outputs from benchmark runs. Reproducible, shareable, and CI-friendly by design.

Continuous batching strategies measured across vLLM, TGI, and Amazon Bedrock batch APIs — optimal config mapped per workload type.

Simple queries routed to lightweight models, complex reasoning reserved for frontier models — up to 20% cost reduction with no accuracy drop, validated.

7 embedding models across 2,000 queries. Titan leads on semantic ranking stability. Azure T3-Large best for knowledge-heavy recall. Documented and reproducible.

Properly tuned prompt-response caching reduces token spend by 40–60% on repetitive enterprise workflows — benchmarked across real workload distributions.

We trace accuracy regressions, latency spikes, and hallucination patterns back to specific retrieval gaps or prompt failures — with evidence, not guesses.

Embedding selection, chunking strategy, reranker tuning, and metadata filtering — we run the experiments your team doesn't have time for, with benchmarks to back every call.

Multi-agent pipelines with deterministic fallbacks, cost guardrails, and evaluation checkpoints — built into the orchestration layer from day one, not retrofitted.

Domain-specific eval suites for zero-ground-truth environments. LLM-as-a-Judge pipelines, normalization strategies, and scoring rubrics built for your data.

Privacy-compliant data prep, deduplication, and instruction-pair formatting for healthcare, legal, and financial verticals — the unglamorous work that makes finetuning actually work.

Every finetuned model is benchmarked against its base across accuracy, hallucination rate, and latency — not just reported as "improved" but proven with domain-specific evals.

Parameter-efficient domain adaptation with LoRA adapters. QLoRA extends this to 4-bit quantized base models — full adaptation at a fraction of compute cost.

GGUF, AWQ, and GPTQ formats validated for production. We find the quantization level where accuracy retention meets inference cost targets — not just the smallest model.