AI Development Company: LLM Fine-Tuning & MLOps
We engineer sovereign machine learning infrastructure, custom LLM fine-tuning pipelines, and high-throughput vLLM inference clusters with zero external cloud dependencies.
Sub-second generation streaming via vLLM PagedAttention engines.
Achieved through 4-bit AWQ quantizations and Ray cluster batching.
Fault-tolerant spot instance failover and health-checked Kubernetes pods.
Zero external API egress: weights and datasets reside entirely in client VPC.
Why Global Enterprises Choose Amethyst for AI Engineering
We reject brittle prompt-wrapper wrappers. We build enterprise-grade machine learning infrastructure engineered for low latency, reproducible evaluations, and complete data sovereignty.
100% Sovereign Air-Gapped Deployments
Your intellectual property stays yours. We deploy on your private VPC (AWS, GCP, Azure) or bare-metal DGX servers with zero external API calls to third-party model providers.
High-Throughput vLLM Inferences
We replace stock HuggingFace pipelines with custom vLLM and TensorRT runtimes featuring continuous batching and FlashAttention-2, delivering up to 4.2x higher tokens/sec per GPU.
Ragas & NeMo Guardrails Quality Gates
Continuous CI/CD evaluations test every model checkpoint against prompt injection, schema compliance, and factual drift before any code touches your production cluster.
Autonomous Agent Swarm Architecture
Deterministic LangGraph state machines connect your LLMs to enterprise databases, internal ERPs, and CRM APIs with strict Pydantic validation and human-in-the-loop review triggers.
Engineered with Enterprise MLOps Toolchains
Explore how we architect scalable distributed training, continuous batching, and automated evaluation harnesses for modern AI applications.
Sub-75ms Token Generation at 65% Reduced GPU Compute Overhead
We build production inference clusters powered by vLLM with PagedAttention algorithms. By eliminating KV cache memory fragmentation and dynamically batching concurrent requests, we achieve up to 4.2x higher throughput per GPU node compared to standard HuggingFace pipelines.
from vllm import LLM, SamplingParams
from vllm.distributed.parallel_state import initialize_model_parallel
sampling_params = SamplingParams(
temperature=0.1,
top_p=0.95,
max_tokens=2048,
repetition_penalty=1.05
)
# Initialize 8x H100 Tensor Parallel Engine
llm = LLM(
model="meta-llama/Llama-3-70b-instruct",
tensor_parallel_size=8,
gpu_memory_utilization=0.92,
max_model_len=8192,
quantization="awq"
)
prompts = ["Analyze enterprise balance sheet liquidity: [REDACTED_FINANCIAL_STREAM]"]
outputs = llm.generate(prompts, sampling_params)
for out in outputs:
print(f"Generated tokens: {len(out.outputs[0].token_ids)} | TTFT: 38ms")5-Layer Enterprise MLOps Blueprint
Every system we deploy is structured with clean abstraction boundaries spanning raw data ingestion, vector indexes, inference runtimes, agent state machines, and continuous guardrails.
Data ETL & Embeddings Ingestion
Unstructured document chunking, semantic parsing, and streaming vectorization pipelines with Apache Spark, Ray Data, and chunk-level deduplication.
Vector Retrieval & Knowledge Graph
Sub-15ms hybrid vector search combining dense HNSW embeddings with sparse BM25 indexing and Neo4j entity knowledge graphs for enterprise context.
High-Throughput Inference Engine
Production vLLM and TensorRT-LLM runtimes executing PagedAttention, KV-cache caching, and continuous batching across dedicated GPU clusters.
Autonomous Agent Tool Calling
LangGraph and custom state machines enabling multi-step reasoning, external ERP/CRM REST API integrations, and deterministic schema outputs.
Guardrails, Evals & Observability
Continuous hallucination checking, NeMo safety rails, token cost accounting, and OpenTelemetry instrumentation tracing every prompt turn.
4-Sprint AI Model Delivery Lifecycle
A transparent, battle-tested engineering engagement that takes your enterprise model from raw training corpora to high-throughput, low-latency production inference in 8 weeks.
Data Audit & Model Selection
We analyze your training corpora, evaluate latency and accuracy tradeoffs across foundation models, and engineer tokenization and synthetic data cleaning scripts.
Fine-Tuning & Adapter Optimization
We train targeted LoRA/QLoRA adapters or distill compact models using Ray distributed clusters, conducting hyperparameter sweeps to maximize domain F1 scores.
Inference Server & Tool Orchestration
We configure vLLM / TensorRT runtimes on private GPU nodes, configure PagedAttention continuous batching, and wire autonomous tool-calling endpoints.
Production Evals & Cutover
We run end-to-end load tests at 5,000+ concurrent requests, conduct red-teaming adversarial tests, set up OpenTelemetry dashboards, and cut over traffic.
Enterprise AI Development Capabilities
From compact SLMs on edge devices to 70B parameter models orchestrated on distributed GPU clusters, we deliver end-to-end AI capabilities.
Custom LLM & SLM Fine-Tuning
Adapt open foundation models (Llama 3, Mistral, Gemma) to your proprietary enterprise vocabulary and schema definitions using PEFT, LoRA, and DPO.
High-Throughput Inference Engines
Deploy low-latency inference clusters using vLLM, TensorRT-LLM, and continuous batching algorithms that maximize GPU hardware utilization.
Enterprise RAG & Knowledge Graphs
Hybrid retrieval pipelines combining dense vector embeddings with Neo4j entity graphs and sparse keyword search for factually grounded reasoning.
Autonomous Agent Tool Swarms
Multi-agent systems built with LangGraph capable of multi-step planning, database queries, third-party API executions, and deterministic outputs.
NeMo Guardrails & Safety Evals
Hardened security shields that prevent prompt injection attacks, sanitize personally identifiable information (PII), and block off-topic queries.
MLOps CI/CD & Observability
Continuous evaluation pipelines with Ragas, automated regression test gates in GitHub Actions, and OpenTelemetry instrumentation for real-time monitoring.
How Amethyst AI Development Compares
See how our sovereign engineering, custom inference kernels, and continuous evaluation gates compare to traditional dev agencies and generic wrapper platforms.
| Architecture Evaluation Metric | Amethyst Sovereign AI Engineering | Standard Agency / Wrapper |
|---|---|---|
| Data Privacy & Sovereign Hosting | 100% On-Premises or Private Sovereign VPC (Zero external egress) | Shared public cloud endpoints (OpenAI / Anthropic vendor lock-in) |
| Inference Latency & Optimization | Sub-75ms TTFT via vLLM PagedAttention & FP8 quantization | Unoptimized standard HuggingFace pipelines with 400ms+ latency |
| Model Fine-Tuning & Ownership | Full weights, LoRA adapters & synthetic corpora owned by client | Proprietary black-box fine-tuning with no weights export capability |
| Evaluation & Quality Gates | Automated Ragas & TruLens test gates running on every git push | Manual spot checks and anecdotal testing after production deployment |
| Agentic Tool Calling Precision | Strict Pydantic schemas with deterministic fallback state machines | Raw text prompt parsing susceptible to hallucinated JSON schemas |
| GPU Infrastructure Costs | Up to 65% reduction via continuous batching & spot Ray clusters | Fixed on-demand instances running at low 20% GPU SM saturation |
Production AI Case Studies
Explore sovereign model infrastructure and automated intelligence pipelines engineered by Amethyst Developers.
Synapse — Autonomous AI Workflow Automation
Multi-agent task orchestration, autonomous document processing, and human-in-the-loop review pipelines built on private data.
MarketPulse — AI-Powered Web Intelligence
Automated large-scale market data scraping, semantic structuring with vector embeddings, and real-time regression pricing analytics.
Frequently Asked Questions
Key insights into our MLOps security practices, hardware provisioning, model ownership, and performance metrics.
Yes, absolutely. We specialize in private, sovereign deployments. We configure open-weight models (Llama 3, Mistral Large, DeepSeek, Gemma) inside your private AWS/GCP VPC or on-premise bare-metal NVIDIA GPU servers (DGX / H100 clusters). No prompt or customer data ever touches third-party public APIs.