Production LLM Deployment: Scalable, Secure & Domain-Optimized at Enterprise Scale

The Solution: Private Fine-Tuned LLM Deployment with Full LLMOps Stack

Spundan designed and deployed an end-to-end private LLM infrastructure — from model selection and fine-tuning to scalable inference serving and production monitoring. Key strategic components included:

1. Model Selection & Evaluation: Benchmarked open-weight LLMs (Mistral, LLaMA, Falcon) against the client's legal tasks to identify the optimal base model balancing performance, inference cost, and hardware requirements.
2. Domain-Specific Fine-Tuning: Applied parameter-efficient fine-tuning (QLoRA) on a curated dataset of legal contracts, judgments, and clause libraries to produce a domain-adapted model with significantly higher accuracy on legal NLP tasks.
3. Private Inference Infrastructure: Deployed the fine-tuned model on private GPU infrastructure (AWS EC2 G5 instances) using vLLM for high-throughput, low-latency inference with PagedAttention and continuous batching.
4. RAG Integration: Augmented the LLM with a Retrieval-Augmented Generation layer, connecting it to the client's internal contract database for grounded, citation-backed responses on specific document queries.
5. Model Versioning & Canary Deployment: Established an LLMOps pipeline with model registry, versioned deployments, and canary release strategies to safely roll out model updates without disrupting live users.
6. Guardrails & Output Validation: Implemented input/output guardrails using NeMo Guardrails to detect and filter hallucinations, out-of-scope responses, and PII leakage before outputs reached end users.
7. LLM Observability: Deployed prompt logging, response quality scoring, latency tracking, and token usage dashboards to maintain full visibility into model behavior in production.

Implementation Steps

The LLM deployment was executed through a disciplined, phased approach that prioritized accuracy, security, and production readiness at each stage:

• Use Case Definition & Data Audit: Defined the three primary LLM tasks (contract summarization, clause extraction, risk flagging), audited available training data, and established ground-truth evaluation sets with the legal domain experts.
• Base Model Benchmarking: Ran structured evaluations of 5 open-weight LLMs on the client's legal tasks using ROUGE, BERTScore, and human evaluation metrics to select the best-fit base model.
• Dataset Preparation & Fine-Tuning: Cleaned and formatted 50,000+ legal document samples into instruction-tuning format; ran QLoRA fine-tuning on 4× A100 GPUs with iterative evaluation checkpoints.
• Inference Server Setup: Configured vLLM inference server with OpenAI-compatible API endpoints, enabling seamless integration with existing product backends with minimal code changes.
• RAG Pipeline Construction: Built document ingestion pipelines, embedded the contract corpus into a Qdrant vector store, and integrated semantic retrieval into the LLM prompt chain for grounded document Q&A.
• Guardrails & Security Hardening: Implemented PII detection, prompt injection defenses, and output content filtering; conducted red-team adversarial testing on the deployed model before go-live.
• LLMOps Pipeline & CI/CD: Built model registry workflows, automated evaluation gates in the CI/CD pipeline, and configured canary deployment with automatic rollback on quality regression.
• Pilot & Production Rollout: Launched with 10 internal legal analysts for two weeks of shadow testing, incorporated feedback, then rolled out to the full product user base with live monitoring from day one.

Results

The private LLM deployment delivered transformative gains in accuracy, cost, latency, and compliance — enabling features that were previously impossible with public API approaches:

• Domain Accuracy Improvement: Fine-tuned model achieved 38% higher accuracy on legal clause extraction compared to the best-performing general-purpose public LLM API tested during evaluation.
• Inference Latency: Average response time dropped to under 800ms for document summarization tasks, enabling real-time in-product usage for the first time.
• Cost Reduction: Private deployment reduced per-query inference cost by 78% compared to public API pricing at equivalent query volumes.
• Hallucination Rate Reduction: Guardrails and RAG grounding reduced hallucinated outputs by 91% compared to vanilla LLM API responses on legal queries.
• Full Data Privacy: Zero client documents transmitted to any external API — achieving 100% data residency compliance and clearing the compliance team's blocker to production launch.
• Legal Analyst Productivity: Contract review time per document reduced by 60%, enabling analysts to process three times more contracts in the same workday.
• Stable Model Versioning: Zero unplanned model behavior changes in production post-launch, compared to three breaking changes experienced with the public API in the prior six months.