CUSTOM LLM INTEGRATION SERVICES
We connect and customize large language models – GPT, Claude, Gemini, and others – for your specific business logic. We embed them directly into your existing workflows, then fine-tune them on your proprietary data so they understand your domain, your terminology, and your customers – with measurably higher accuracy, fewer hallucinations, and lower operating costs.
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LLM Integration & Fine-Tuning Services We Deliver
LLM API Integration & Orchestration
We connect GPT, Claude, Gemini, and open-source models to your stack through secure, production-grade APIs – from single endpoints to multi-model layers that route each prompt to the best-fit LLM – with authentication, rate limiting, and fallback logic built in.
Domain-Specific LLM Fine-Tuning
Generic models hallucinate on niche topics. We fine-tune LLMs on your proprietary data – support tickets, contracts, medical records, filings – using supervised fine-tuning, RLHF, and parameter-efficient methods like LoRA and QLoRA for higher accuracy and lower token cost.
RAG & Knowledge-Base Integration
Not every use case needs fine-tuning. When your data changes often or answers must cite their sources, we build RAG pipelines – vector databases, embedding models, chunking strategies, and re-ranking – that ground responses in your live knowledge base.
Data Preparation & Prompt Engineering
Prompt engineering can lift base-model performance before any tuning begins. We design system prompts and few-shot templates and turn raw data into clean, anonymized, training-ready datasets, so every fine-tuning run starts from a strong baseline.
Model Selection & Strategy Advisory
Fine-tuning, RAG, or prompt engineering? We benchmark multiple models against your data and recommend the approach that fits your accuracy, cost, and compliance needs – architected for portability, so you're never locked into a single provider.
Evaluation & Continuous Improvement
Deploying a model is the starting line. We build evaluation harnesses and monitoring dashboards that track latency, cost-per-query, hallucination rate, and user satisfaction – then retrain or re-tune when performance drifts, keeping your LLM sharp as your business evolves.
Technologies Behind Our LLM Solutions
We select base models and training methods for each use case based on accuracy, cost, and data-privacy needs, and build on open tooling – so you can change models without redoing the pipeline.
Base Models
Proprietary APIs (OpenAI GPT, Anthropic Claude, Google Gemini) and open-weight families (Llama, Mistral, Qwen, Gemma) – benchmarked on your data, so we recommend the model that fits your accuracy, cost, and residency needs.
Training Platforms & Evaluation
AWS SageMaker, Google Vertex AI, Azure Machine Learning, or on-prem NVIDIA GPU clusters for training; MLflow and Weights & Biases for experiment tracking; lm-evaluation-harness, DeepEval, and promptfoo for benchmarks and regression tests.
Serving & Integration Layer
vLLM, SGLang, and NVIDIA Triton with TensorRT-LLM for scalable inference; LiteLLM as a gateway for multi-model routing, fallbacks, and rate limiting, with LangChain or LlamaIndex where orchestration is needed.
Fine-Tuning & Training
PyTorch with Hugging Face Transformers, PEFT (LoRA, QLoRA), and TRL for SFT, preference tuning, and RLHF; Axolotl, Unsloth, and DeepSpeed for efficient multi-GPU runs; bitsandbytes for quantization.
Data Preparation & Labeling
Label Studio and Argilla for annotation and human feedback; Microsoft Presidio for PII anonymization; Hugging Face Datasets and Great Expectations for cleaning, de-duplication, and quality checks on instruction–response data.
How We Build Your LLM Solution
Our delivery process mirrors the proven workflow we use across all AI engagements – adapted for the data preparation, training, and validation that LLM projects demand.
Discovery & Data Audit
We audit your business logic, compliance constraints, and success metrics, plus your training corpus – volume, quality, label coverage, and bias risk – then recommend the optimal strategy: full fine-tune, LoRA, prompt tuning, or RAG.
Data Preparation & Prompt Engineering
We clean, de-duplicate, and anonymize your data, then structure it into instruction–response pairs or conversational turns. In parallel, our prompt engineers design system prompts and few-shot templates that maximize base-model performance.
Model Training & Validation
On AWS SageMaker, Google Vertex AI, Azure ML, or on-prem GPU clusters, we run fine-tuning jobs with hyperparameter sweeps. Every checkpoint is tested against held-out sets and domain benchmarks, so you see accuracy, F1, and hallucination metrics at every stage.
Deployment, Integration & Handover
We containerize the fine-tuned model, deploy it behind a scalable inference endpoint, and wire it into your application layer. You get full documentation and runbooks – and, if needed, we train your ML team to manage retraining cycles independently.
Industries & Use Cases
Our LLM solutions serve organizations across sectors – wherever domain knowledge, documents, or customer interactions demand accuracy that generic models can’t deliver.
Software Engineering
Code assistants and text-to-SQL models fine-tuned on your repositories, schemas, and conventions, so suggestions match your internal frameworks instead of generic patterns.
Pharmaceuticals
Models fine-tuned on internal SOPs and safety reports for adverse-event extraction, regulatory-document drafting, and literature triage – trained and hosted inside your own environment.
Customer Service & BPO
Models fine-tuned on historical tickets to classify requests, draft replies in your brand voice, and handle multiple languages at lower per-query cost.
Frequently Asked Questions
Everything you need to know before integrating or fine-tuning an LLM.
How long does an LLM integration or fine-tuning project take?
We can kick off within 1–2 weeks, starting with a data audit that tells us whether you need API integration, RAG, or fine-tuning. Timelines depend mainly on data volume and quality, the number of systems involved, and how many training and evaluation cycles the use case needs. You get a clear timeline after discovery.
LLM fine-tuning vs. RAG – which do we need?
It depends on your data and goals. RAG suits data that changes often or answers that must cite sources, while fine-tuning suits domain-specific language and tasks where generic models hallucinate. We audit your data first and recommend the right approach – or combination – so you invest only where it pays off.
How do you handle data security and avoid vendor lock-in?
We anonymize sensitive data where required and can train and deploy on AWS, Google Cloud, Azure, or on-prem GPU clusters within your compliance and data-residency requirements. Our architectures are model-agnostic, so we benchmark multiple models on your data and you’re never locked into a single provider.