Build, train, and deploy enterprise-grade Large Language Models customized with your proprietary data, private architecture, and strict security compliance.
From domain adaptation and parameter-efficient fine-tuning to high-throughput inference serving, we engineer customized LLMs that deliver unmatched precision.
Develop foundational domain models from scratch or train open-weight architectures on your private enterprise token datasets.
Adapt leading open-source models (Llama 3, Mistral, Qwen) using parameter-efficient fine-tuning for deterministic business outputs.
Ground your LLMs with real-time vector search, contextual chunking, re-ranking models, and hybrid retrieval-augmented generation.
Compress models via AWQ, GPTQ, and FP8 quantization while maximizing token throughput using high-performance inference engines.
Implement RLHF, DPO, strict PII masking, toxic prompt defense, and constitutional AI guardrails to eliminate hallucination risks.
Deploy custom LLMs into air-gapped VPCs, private clouds, or bare-metal GPU infrastructure with zero data leakage guarantees.
Unlock proprietary intellectual property, sovereign data governance, and high-precision language reasoning.
Deep comprehension of industry-specific taxonomy, technical documents, and internal operational SOPs.
Complete data sovereignty without streaming confidential enterprise prompts to third-party public clouds.
Eliminate volatile per-token public API bills by operating optimized, self-hosted private inference pipelines.
Maintain full ownership of your custom fine-tuned weights, embeddings, synthetic training data, and pipelines.
Optimize proprietary reasoning models built on your private enterprise data.
Retain 100% intellectual property ownership of your customized weights, training sets, and operational codebases.
Engineered with vLLM, TensorRT-LLM, and flash attention to deliver lightning-fast streaming generation speeds.
Deterministic system architectures backed by automated synthetic evaluations, ground-truth benchmarks, and RAG validation.
Process mission-critical internal data without relying on public cloud vendors or exposing sensitive customer PII.
Trained and aligned directly on your specialized terminology, eliminating generic responses and boilerplate content.
Scale workloads horizontally across GPU clusters with dynamic batching and autoscaling inference pipelines.
From data curation and tokenizer calibration to fine-tuning, alignment, and deployment, we execute a scientific machine learning lifecycle.
Data cleaning, deduplication, synthetic dataset generation, and formatting instruction-tuning datasets.
Benchmark and select the right foundation model (Llama 3, Mistral, Gemma, or Small Language Models) for your compute budget.
Execute parameter-efficient fine-tuning (LoRA/QLoRA) or full-weight adaptation on high-performance multi-GPU clusters.
Apply RLHF or DPO techniques to align model outputs with enterprise tone, safety guardrails, and compliance standards.
Optimize weights with AWQ/GPTQ and configure vLLM/TensorRT-LLM for sub-second response times.
Deploy to private VPC or on-prem clusters with automated drift detection, hallucination monitoring, and red teaming.
PromptApps provides flexible engagement models tailored to your machine learning roadmap.
Integrate senior PyTorch, CUDA, and NLP engineers directly into your existing machine learning sprints.
An end-to-end autonomous pod of ML researchers, MLOps leads, and data annotators building your custom models.
Guaranteed timelines and fixed pricing for discrete deliverables like fine-tuning, RAG setup, or evaluation audits.
Python
PyTorch
Hugging Face
vLLM Engine
Docker
Kubernetes
AWS Bedrock
pgvector
Redis Vector
LangChain
LlamaIndex
Triton Server
Specialized legal language model fine-tuned on 2M+ contract clauses with 98.4% extraction accuracy.
Read Full Case Study →High-security hybrid RAG engine parsing SEC filings with sub-second retrieval and zero token leakage.
Read Full Case Study →Air-gapped 70B healthcare model deployed on private hardware, cutting external API bills by 72%.
Read Full Case Study →Everything you need to know about custom LLM development, fine-tuning, and deployment.
Tell us about your proprietary data, use-case, or infrastructure requirements.
