Enterprise LLM Development Services
Move beyond generic ChatGPT wrappers and fragile prompt engineering. We design, fine-tune, and deploy production-ready Large Language Models, enterprise Retrieval-Augmented Generation (RAG) pipelines, and autonomous multi-agent architectures that operate directly on your proprietary data, engineered for enterprise security and scaled globally.
Zero data retention by model providers; SOC 2 Type II & HIPAA compliant in your VPC.
Hardware-accelerated inference utilizing vLLM, TensorRT-LLM, and 4-bit/8-bit quantization.
Rigorous evaluation frameworks (RAGAS, TruLens) delivering hallucination rates < 1%.
Enterprise Governance & Compliance Standards
Inference Latency
Domain Accuracy
Token Compute Cost
Data Sovereignty
Comprehensive LLM Development Services Built for Mission-Critical Production
Off-the-shelf foundation models lack your organization's domain knowledge, compliance guardrails, and proprietary workflows. We engineer specialized systems.
Domain-Specific LLM Fine-Tuning
Align open-weight models (Llama 3.3, Mistral, DeepSeek-V3, Qwen 2.5) to proprietary business logic, regulatory rules, and niche enterprise vocabularies with mathematically certified precision.
- PEFT, LoRA & QLoRA parameter-efficient adaptation
- Direct Preference Optimization (DPO) & RLHF
- Synthetic dataset generation & deduplication pipelines
Multi-Agent Swarm Orchestration
Engineered multi-agent systems where specialized LLMs collaborate deterministically to execute complex enterprise workflows, call APIs, query data warehouses, and trigger software actions.
- Deterministic state graphs via LangGraph & CrewAI
- SQL query generation, schema routing & self-correction
- Human-in-the-loop (HITL) approval checkpoints
Self-Hosted & Private VPC Deployments
Maintain 100% intellectual property ownership. We deploy models inside your AWS, Azure, or GCP private cloud or bare-metal GPU infrastructure with zero external telemetry.
- Air-gapped on-premise Kubernetes & vLLM setups
- Confidential computing & encrypted tensor memory
- No vendor data retention or external model dependencies
Advanced Enterprise RAG Pipelines
Eliminate hallucinations with production-grade Retrieval-Augmented Generation that connects LLMs to real-time internal databases, ERPs, CRMs, and unstructured document repositories.
- Hybrid dense-sparse retrieval (BM25 + BGE/Voyage)
- Cohere contextual reranking & semantic deduplication
- Knowledge graph integration (GraphRAG) for multi-hop logic
LLM Guardrails & Real-Time Alignment
Protect your enterprise brand with real-time semantic firewalls that detect prompt injection, enforce role-based access control, sanitize PII, and block adversarial attempts.
- NeMo Guardrails & Llama Guard policy enforcement
- Automated PII/PHI scrubbing before inference
- Red-teaming & automated jailbreak penetration tests
Model Distillation & Edge Optimization
Condense 70B+ parameter capabilities into compact 3B–8B parameter Small Language Models (SLMs) that run blazingly fast at 1/10th the inference compute cost.
- Knowledge distillation from frontier teacher models
- INT4, INT8, and AWQ/FP8 hardware quantization
- Speculative decoding for 2x–3x generation throughput
Off-the-Shelf Commercial APIs vs. Custom Enterprise LLM Engineering
Understand the architectural, economic, and security differences between relying on public SaaS wrappers versus owning your language intelligence layer.
| Evaluation Dimension | Generic Commercial API (e.g. Public ChatGPT) | Our Custom Enterprise LLM SolutionRECOMMENDED |
|---|---|---|
| Data Privacy & IP Protection | Data transmitted over public internet; dependent on vendor privacy terms. | 100% data stays in your VPC; zero external logs, complete model weight ownership. |
| Domain Accuracy & Hallucinations | Generic knowledge base; prone to hallucinating on internal nomenclature. | Fine-tuned on your proprietary data + grounded in verified RAG knowledge bases (<1% error). |
| Operational & Inference Costs | Metered per-token pricing scales linearly with usage, becoming cost-prohibitive. | Fixed GPU infrastructure cost; 60%–80% lower total cost of ownership at scale. |
| Latency & Throughput SLAs | Shared public infrastructure subject to rate limits, outages, and variable latency. | Dedicated inference clusters (vLLM / TensorRT) with guaranteed sub-200ms TTFT. |
| Vendor Lock-In & Portability | Proprietary APIs lock workflows to a single closed provider. | Open-weight models (Llama, Mistral, DeepSeek) run anywhere: cloud, on-prem, or hybrid. |
Enterprise AI & LLMOps Technology Stack
We build with battle-tested open-source and enterprise AI frameworks, ensuring high throughput, deterministic routing, and cloud portability.
Open-weight and frontier foundation architectures tailored to task complexity.
Distributed training engines, parameter adaptation, and quantization kernels.
Stateful agent DAGs, structured tool calling, and workflow controllers.
Enterprise vector databases for hybrid search and sub-millisecond retrieval.
High-throughput inference engines and production observability pipelines.
What We Built, and What It Solves
Real enterprise architectures delivered to production with measurable business ROI and zero data leakage.
HIPAA-Compliant Clinical Trial Matching & EHR Extraction Engine
The Enterprise Challenge
A clinical research consortium needed to extract patient eligibility criteria from unstructured electronic health records (EHR) across 14 hospital networks without patient data ever leaving the institutional perimeter.
The Custom LLM Architecture
Fine-tuned Llama 3.3 70B on 45,000 anonymized clinical notes using QLoRA. Grounded in institutional clinical trial databases via hybrid Qdrant dense retrieval and Cohere reranking inside a HIPAA-compliant AWS VPC.
Extraction Precision
over standard GPT-4
Inference Latency
batch processing
Private Autonomous Financial Underwriting & Regulatory Audit Agent
The Enterprise Challenge
A commercial lending institution required automated financial risk auditing across 800+ page SEC 10-K filings, loan agreements, and cash-flow sheets with zero hallucination tolerance and strict SOC 2 compliance.
The Custom LLM Architecture
Engineered a deterministic LangGraph multi-agent swarm. Agent 1 extracts balance sheet line-items; Agent 2 runs Python-verified financial ratio calculations; Agent 3 audits against FDIC underwriting guidelines.
Audit Acceleration
hours to minutes
Financial Calculation Error
verified by code sandbox
Air-Gapped Multi-Jurisdiction M&A Due Diligence System
The Enterprise Challenge
An elite corporate law firm handling multi-billion dollar cross-border acquisitions needed an on-premise AI system capable of analyzing thousands of confidential contracts without cloud transmission.
The Custom LLM Architecture
Deployed quantized Mistral Large and Qwen 2.5 on self-hosted dual NVIDIA H100 servers. Integrated GraphRAG to map entity ownership chains and indemnification clauses across disparate merger agreements.
Air-Gapped Isolation
no external network access
Review Time Saved
per deal closure
Our 5-Phase LLM Delivery Process: Architecture to Scaled Production
Predictable engineering cadences, continuous milestone verification, and automated regression testing from day zero.
Discovery & ADR
Dataset evaluation, KPI framing, security audit, and formal Architecture Decision Record (ADR).
Data Curation & Tokenization
PII scrubbing, synthetic augmentation, custom tokenization, and vector index ingestion.
Fine-Tuning & Agent Build
LoRA/QLoRA adaptation, DPO policy alignment, agent graph construction, and tool integration.
Red-Teaming & Verification
Adversarial prompt injection testing, RAGAS faithfulness benchmarking, and compliance sign-off.
Production Deployment
vLLM containerization in your private VPC, telemetry dashboards, caching, and CI/CD LLMOps.
Transparent Engagement Models Built for Rapid Velocity
Tailored collaboration structures designed to match your technical roadmap, internal capabilities, and timeline demands.
Rapid Enterprise PoC
2 to 4 Weeks
Validate feasibility, benchmark accuracy on your proprietary data, and establish clear ROI before scaling.
- Custom RAG pipeline on sample enterprise data
- Benchmark report: accuracy, latency & cost
- Architecture Decision Record (ADR)
- Executive demonstration sandbox
End-to-End LLM Build
8 to 12 Weeks
Turnkey development and deployment of a custom fine-tuned model and multi-agent pipeline in your VPC.
- Domain fine-tuning & DPO policy alignment
- Complete agent workflow orchestration
- Semantic guardrails & PII sanitization
- Private cloud or on-prem deployment
- Full ownership of weights and codebase
Dedicated AI Engineering Squad
Ongoing Monthly
Embedded senior AI researchers, MLOps architects, and backend engineers scaling your internal AI roadmap.
- Senior PyTorch & LLMOps engineers
- Continuous model retraining & dataset curation
- Latency profiling & GPU cost optimization
- Weekly sprint cadence & Slack integration
FAQ
Common questions.
Everything technical decision-makers need to know about custom LLM architectures, data sovereignty, and production timelines.
RAG (Retrieval-Augmented Generation) connects an existing foundation model to live internal documents or databases, fetching relevant facts dynamically at query time without altering model weights. Fine-tuning adjusts the model's underlying neural weights, teaching it domain-specific terminology, strict JSON outputs, or customized analytical reasoning. High-performance enterprise systems typically combine both: fine-tuning for deterministic tone and formatting, coupled with RAG for dynamic factual truth.
We operate under a strict Zero Data Retention framework. For cloud deployments, models are containerized entirely within your private Virtual Private Cloud (VPC) on AWS, Azure, or GCP. Your data is never transmitted to public model providers or used to train external models. For regulated banking or healthcare institutions, we deploy open-weight models on self-hosted, air-gapped on-premise hardware with zero internet egress.
For enterprise RAG pipelines, you can begin immediately with your existing unstructured PDFs, markdown files, Notion pages, or SQL databases. For Parameter-Efficient Fine-Tuning (PEFT/LoRA), quality dramatically outweighs quantity: a clean, curated dataset of 1,000 to 5,000 high-quality domain instruction-response pairs is typically sufficient to achieve state-of-the-art domain performance.
We are model-agnostic. Depending on your latency, regulatory, and cost requirements, we deploy open-weight models (Meta Llama 3.3, Mistral Large, DeepSeek-V3/R1, Qwen 2.5) or integrate frontier API models (Anthropic Claude 3.5 Sonnet, OpenAI GPT-4o, Google Gemini 1.5 Pro).
We combat hallucinations through a multi-tier defense: (1) Hybrid dense/sparse RAG grounding models in verified enterprise facts; (2) Contextual re-rankers (Cohere Rerank) ensuring only high-relevance chunks are passed; (3) NeMo semantic guardrails verifying factual claims; and (4) Automated RAGAS evaluation loops measuring faithfulness before output delivery.
A production-ready Proof of Concept (POC) is delivered within 2 to 4 weeks. Full enterprise deployments, including custom fine-tuning, security guardrails, multi-agent orchestration, and LLMOps telemetry, typically take 8 to 12 weeks. Engagements can be structured as fixed-price milestones or dedicated monthly squads.
Yes. We specialize in containerized deployments using Docker, Kubernetes, and optimized inference engines like vLLM and TensorRT-LLM, allowing your models to run entirely within your private data center or private cloud infrastructure without external API dependencies.
Yes. We provide continuous LLMOps monitoring covering token consumption, latency benchmarks, output drift, and semantic failure logging. We also establish automated retraining pipelines as your enterprise documentation evolves.
READY TO ARCHITECT
Ready to Architect Your Enterprise LLM?
Bring us your proprietary datasets, accuracy requirements, and latency constraints. We will diagnose architectural trade-offs, evaluate open vs. frontier models, and outline a concrete deployment roadmap.