LLM Integration and Fine-Tuning Services

We ship in 3 directions: integrating commercial or self-hosted LLMs, adapting open-source models through fine-tuning, and building custom small language models. Every engagement includes a reference architecture, an evaluation suite, and production operations, so you can use the increments from the first sprint.

Specialization is king. We focus on path-honest, tech-agnostic engineering, where the decision framework depends on the exact use case and situation of the client. With deliverables, you receive hundreds of try-and-miss efforts in the background that help to modernize, build, and innovate business solutions faster and on the first attempt.

Most buyers should use RAG first. Fine-tuning is justified only when you have measurable evidence that the base model is the real bottleneck with enough high-quality training data. In this case, our decision tree maps this per feature in the feasibility session.

Pricing depends on the requirements and niche, starting from a $7,500 feasibility session to embedded pods at $32,000/month. Hourly bands for an LLM architect vary between $160–220 and $120–170 for an ML/fine-tuning engineer.

To get an exact quote for your project, book a free assessment session.

Yes. We deliver it via 5 integration patterns (RAG, in-product copilot, tool-use automation, document intelligence, and multimodal) against a documented reference architecture, with per-data-class routing for compliance. Moreover, if you need an extended solution, custom LLM development services scale from one layer to the full stack.

Definitely. We help to choose and self-host Llama 3.3, Mistral, Qwen 2.5, and Phi-4 in your VPC or on-prem, with quantization (AWQ, GPTQ) and vLLM, TGI, or Triton serving for cost and latency control, all depending on your requirements.

LoRA trains small adapter layers cheaply and leaves the base model intact. While full SFT trains all parameters for deeper behavior change. In its turn, DPO applies pairwise preference training to align tone, safety, and quality, usually after SFT. So all three are applicable depending on the phase and the desired result.

Yes. You own all increments of the custom LLM/SLM development, the LLM development solution for enterprise, and other services, including the model weights and all the code we write for you. This is firmly and unequivocally stated in the contracts.

The feasibility report takes up to 1 week. An MVP ships a production-ready feature between 8–14 weeks, depending on the complexity. This timeline holds for LLM integration for enterprise platforms with standard data-access requirements.

We use a structured decision framework based on business objectives, data availability, latency requirements, compliance constraints, and expected ROI.

The objective is to select the simplest architecture that achieves production requirements without unnecessary complexity.

Our LLM integration architecture typically includes:

• User application layer
• Authentication and access control
• Orchestration layer
• Retrieval and knowledge services
• Model routing and inference layer
• Observability and evaluation systems
• Security, governance, and audit controls

to integrate AI features into existing software without a rewrite while maintaining high performance metrics.

At Devox Software, a production fine-tuning pipeline typically includes:

• Dataset collection and preparation
• Data cleaning and annotation
• Training and validation split creation
• Baseline model evaluation
• Fine-tuning using LoRA, PEFT, or full SFT
• Automated evaluation and benchmarking
• Human review and validation
• Production deployment and monitoring

Each stage includes quality controls to ensure improvements are measurable and repeatable.

Every project includes a structured evaluation framework covering answer accuracy, groundedness, citation quality, hallucination rate, retrieval performance, latency, cost per request, safety, and compliance. This ensures decisions are based on measurable outcomes rather than subjective impressions.