AI & ML Model Taxonomy selecting the right engine.
Comparing frontier LLMs, open-weights, embedding models, and vision architectures. How we select the optimal model for cost, latency, and accuracy.
Using a massive frontier model for every simple query wastes budget. We engineer hybrid architectures that route the right task to the right engine.
Foundation models are not created equal. Choosing the wrong model architecture leads to 10x higher latency, hallucinated outputs, and runaway monthly token bills. Steigern Tech benchmarks, prunes, and fine-tunes the exact model weights suited to your task.
Semantic prompt routing
Directing high-frequency extractions to ultra-fast local models, reserving expensive reasoning engines exclusively for complex analysis.
60% Inference savings
Slashing monthly cloud API bills through smart caching, quantization, and specialized small domain models.
Empirical benchmark gates
Testing models against your real enterprise ground truth rather than generic synthetic public leaderboards.
Six model categories, optimal latency and cost.
High-efficiency model serving optimizes the Pareto frontier: choosing whether to quantize down to 4-bit, distill into a smaller student architecture, or deploy speculative decoding for maximum speed.
Intelligent query dispatcher
Dynamically inspect incoming prompt complexity and route between lightweight 8B models and massive frontier engines.
Frontier reasoning engines
OpenAI o1/GPT-4o, Anthropic Claude 3.7 Sonnet, and Gemini 2.0 reserved for multi-step reasoning and mathematical planning.
Private open-source weights
Meta Llama 3.3, Mistral Large, and DeepSeek running on private vLLM servers with zero external network connectivity.
Dense & sparse vector embeddings
Cohere, OpenAI text-embedding-3, and BGE-M3 models delivering high-accuracy semantic vector representations.
Model quantization & pruning
Compressing 70B parameter models into 4-bit and 8-bit precision to fit within cost-effective single GPU configurations.
Continuous benchmark auditing
Evaluation harnesses running automated regression tests measuring perplexity, schema validity, and hallucination scores.
From model confusion to benchmarked precision, backed by empirical metrics.
We test open-source and proprietary models on your exact dataset — providing empirical benchmarks on latency, memory, cost, and task accuracy.
Evaluation benchmark design
Curate gold-standard ground-truth dataset and automated evaluation metrics (BLEU, ROUGE, LLM-as-judge).
Multi-model tournament
Benchmark Llama 3, Mistral, Qwen, Claude, and GPT-4 on accuracy, context length, and latency.
Quantization & pruning analysis
Evaluate FP16 vs AWQ vs FP8 vs GGUF to identify the optimal balance of inference speed and task fidelity.
Benchmarked serving POC
Deploy selected model on target hardware and demonstrate 3x+ inference speedup with empirical data.
High-concurrency cluster setup
Configure vLLM / Triton cluster with continuous batching, PagedAttention, and speculative decoding.
Continuous regression monitoring
Run scheduled benchmark suites on emerging model releases to capture free speed and accuracy upgrades.
Evaluation benchmark design:Curate gold-standard ground-truth dataset and automated evaluation metrics (BLEU, ROUGE, LLM-as-judge).
production inference cluster deployment with speculative decoding and vLLM
new foundation model evaluations and zero-downtime weight upgrades
Taxonomy Outcomes that control monthly spend.
Right-sizing model choices for latency, budget, and accuracy. Here are the operational advantages of our taxonomy architecture.
60% Reduction in monthly inference spend
Eliminating oversized API calls by serving high-frequency clerical tasks through lightweight domain-tuned models.
Sub-100ms response times for simple queries
Ultra-fast local execution for classification, keyword matching, and structured extraction workflows.
Complete provider independence
Abstracted API layers allowing instant migration between providers when pricing, quotas, or policies change.
High-accuracy complex reasoning
Automatic escalation of multi-step analytical and mathematical queries to state-of-the-art frontier reasoning models.
Total data isolation for sensitive prompts
Semantic routers automatically directing confidential customer data to air-gapped on-premises weights.
Empirically audited model performance
Continuous benchmark scorecards demonstrating quantitative proof of accuracy and latency improvements.
Model compression, quantization & serving engines.
Built on vLLM, TensorRT-LLM, AutoAWQ, GGUF, llama.cpp, Triton Inference Server, and Hugging Face for blazing-fast inference.
Idea to Market–Ready Product
We turn your idea into a secure, compliant, and test-ready product in weeks, not months.
Inquiry & Scoping
We rapidly decompose requirements, pressure-test commercial viability, and align technical scope before capital is committed.
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