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LLM Development Services

Custom
LLM Solutions

Go beyond generic AI. We train, fine-tune, and deploy domain-specific Large Language Models that understand your corporate DNA and industry nuances.

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The Fine-Tuning Gap

Is Your AI Suffering from
Generic Intelligence?

Off-the-shelf models lack your proprietary knowledge and specific terminology, leading to hallucinations and inaccurate results.

Hallucination Risks

General LLMs confidently provide incorrect information about your internal policies or niche products.

Data Leakage

Sending sensitive data to public APIs for inference poses significant compliance and security risks.

Latency Issues

Unoptimized models lead to slow response times, hurting user experience and increasing operational costs.

LLM Capabilities

We engineer models that go beyond pattern matching to deep semantic understanding.

Domain Fine-Tuning

Customize foundation models using PEFT and QLoRA techniques to master your specific industry jargon and regulations.

  • Industry-Specific Nuance
  • Parameter Efficient Training

Scalable RAG Arch

Connect your LLM to live enterprise data sources for grounded, factual, and hallucination-free responses.

  • Real-time Data Sync
  • Source Attributions

Model Distillation

Shrink massive models into smaller, faster versions that maintain performance while drastically reducing compute costs.

  • 90% Latency Reduction
  • Edge Deployment Ready

LLM Evaluation & RLHF

Rigorous testing frameworks and human-in-the-loop training to align models with safety and business ethics.

  • Systematic Benchmarking
  • Preference Alignment

Governance & Guardrails

Implement real-time content filters, PII detection, and prompt-injection defense for secure enterprise usage.

  • PII Protection
  • Adversarial Defense

High-Throughput Serving

Deployment of models using vLLM, TensorRT, and DeepSpeed for low-latency, multi-concurrent user environments.

  • GPU Optimization
  • Dynamically Scalable

LLM Infrastructure

We utilize the most advanced tools and frameworks to build models that push the boundaries of AI.

State-of-the-Art Training

Frameworks

PyTorch, TensorFlow, JAX, Hugging Face

Compute

NVIDIA H100, A100, CUDA, TensorRT

Data Store

Milvus, ChromaDB, Pinecone, Weaviate

Inference

vLLM, Text-Generation-Inference, Ollama

Ops

Weights & Biases, MLflow, LangSmith

Cloud

Azure ML, AWS SageMaker, GCP Vertex AI

Safety

NeMo Guardrails, Guardrails AI, Lakera

Lang-Stack

Python, Rust, Mojo, LangChain

Why Trust Constelly
with Your LLM Development?

We don't just connect APIs�we build robust, vertically integrated language systems. From model training to secure cloud deployment, we ensure your AI is a competitive asset, not a liability.

Architectural Mastery

We implement cutting-edge RAG and Agentic architectures that minimize hallucinations and maximize utility.

Compute Optimization

Our expertise in model quantization and pruning allows you to run powerful models on existing hardware, saving thousands in OpEx.

Data Sovereignty

Deploy models within your own VPC or on-premise servers to ensure your data stays under your control at all times.

40%

Less Hallucination

90%

Faster Inferences

100%

Data Sovereignty

Tier-1

Model Accuracy

LLM Development FAQ

Everything you need to know about custom Large Language Models.

Custom LLM development involves taking foundation models (like Llama, Mistral, or GPT) and adapting them to specialized business needs through fine-tuning, RAG (Retrieval Augmented Generation), and custom interface building.
Standard ChatGPT is a generalist. Custom LLMs are specialists; they are trained on your company's data, use your internal vocabulary, and can be hosted on your own servers to ensure total data privacy.
Fine-tuning is a training process that adjusts a model's weights to better fit a specific dataset. PEFT (Parameter Efficient Fine-Tuning) and QLoRA allow us to do this with much less compute power while maintaining high quality.
We use local, offline training environments and VPC-contained cloud instances. We ensure no sensitive data is leaked back to the original model providers or exposed to the public internet during the training phase.
Retrieval Augmented Generation (RAG) allows the model to "look up" facts in a private database before answering. This significantly reduces hallucinations and ensures the model is always using the most up-to-date company info.
Yes. We specialize in models that can process not just text, but also images, audio, and structured data, allowing for deeper insights from diverse enterprise datasets.
A Proof of Concept (POC) with basic RAG can be built in 2-3 weeks. A full-scale fine-tuning project typically takes 4-8 weeks depending on the complexity of the data and the desired accuracy levels.
Costs vary based on model size and compute requirements. However, we focus on model distillation and optimization to significantly reduce your long-term token costs compared to using generic paid APIs.
Absolutely. We are advocates for open-source AI and specialize in deploying these models on private infrastructure to give our clients 100% control over their AI stack.
We start with a "Data Readiness Audit" to see if your proprietary data is clean and sufficient for training. From there, we build a tailored roadmap for your custom model development.

Harness Large Language Models

Develop sophisticated NLP applications with custom LLM finetuning. Enhance communication and analysis with state-of-the-art language AI.

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