AI Development Services

Generative AI & LLM Engineering

Architect production-grade Generative AI applications. Explore our technical methodologies for Advanced RAG, QLoRA fine-tuning, and deterministic Agentic Workflows.

Architect Your System

Table of Contents

Deploying Large Language Models (LLMs) in a commercial environment extends far beyond API wrapping. Enterprise Generative AI demands strict determinism, sub-second latency targets, and robust data isolation. FNA Technology architects end-to-end LLM applications, specializing in Advanced Retrieval-Augmented Generation (RAG), Parameter-Efficient Fine-Tuning (PEFT), and deterministic Agentic orchestration systems.

Advanced RAG: Grounding Generation in Enterprise Context

Standard naive RAG models often fail in production due to context dilution—injecting irrelevant chunks into the LLM's finite context window. To solve this, FNA Technology deploys Advanced RAG architectures that utilize multi-stage retrieval pipelines.

We begin with document parsing and semantic chunking, converting unstructured enterprise data into dense vector embeddings. Retrieval relies on Hybrid Search, blending dense embeddings (via Cosine Similarity) for semantic intent with sparse lexical representations (BM25) for precise keyword matching.

Advanced Retrieval-Augmented Generation (RAG) Architecture

Mathematical Foundation of Vector Retrieval

Dense retrieval calculates the distance between the embedded query vector $\vec{q}$ and document chunk vectors $\vec{d}$ using Cosine Similarity, maximizing the dot product of normalized vectors:

\text{sim}(\vec{q}, \vec{d}) = \frac{\vec{q} \cdot \vec{d}}{\|\vec{q}\| \|\vec{d}\|} = \frac{\sum_{i=1}^{n} q_i d_i}{\sqrt{\sum_{i=1}^{n} q_i^2} \sqrt{\sum_{i=1}^{n} d_i^2}}

Mitigating Context Dilution via Cross-Encoder Re-Ranking

After retrieving the top- $K$ documents from the vector database, we apply a Cross-Encoder re-ranking model. Unlike Bi-Encoders used for initial retrieval, Cross-Encoders process the query and document simultaneously through the Transformer layers, computing deep cross-attention. This drastically improves precision, ensuring only the most relevant context chunks are injected into the final generator LLM prompt.

QLoRA: Parameter-Efficient Fine-Tuning

When foundational models fail to capture specific industry dialects (e.g., legal phrasing, medical taxonomy) or structural syntaxes (e.g., proprietary JSON formats), we employ Quantized Low-Rank Adaptation (QLoRA) to fine-tune open-weight models (like Llama 3) efficiently.

QLoRA quantizes the base model weights to 4-bit NormalFloat (NF4), drastically reducing VRAM requirements. During backward propagation, only the low-rank adapter matrices ( $A$ and $B$ ) receive gradient updates, keeping the base model frozen. The total weight update is expressed as:

QLoRA Parameter-Efficient Fine-Tuning Architecture

W = W_{\text{NF4}} + A \cdot B

$W_{\text{NF4}}$ : Frozen 4-bit quantized base weights.
$A \in \mathbb{R}^{d \times r}, B \in \mathbb{R}^{r \times d}$ : Trainable low-rank matrices where rank $r \ll d$ .

Agentic Workflows & The ReAct Paradigm

Transitioning from passive chatbots to active, autonomous systems requires Agentic Workflows. FNA Technology builds agents that possess the ability to invoke external tools (APIs, SQL databases, calculators) to augment their capabilities. We implement the ReAct (Reasoning and Acting) paradigm, forcing the LLM to output its internal trace before executing a tool.

The ReAct loop operates in continuous cycles of Thought $\rightarrow$ Action $\rightarrow$ Observation. By enforcing this determinism, agents can resolve complex, multi-step tasks without derailing into infinite loops or hallucinations. For complex systems, we deploy multi-agent orchestration frameworks (like LangGraph) where specialized agents route tasks among themselves.

Production LLM Evaluation & Guardrails

"Vibes" are not a viable metric for production. We implement rigorous, programmatic evaluation pipelines utilizing the RAGAS framework. During CI/CD, synthetic datasets are used to score the architecture across vital dimensions:

Evaluation Metric	Measurement Goal	Impact
Faithfulness	Measures if the generated answer is entirely inferable from the retrieved context.	Directly quantifies hallucination rates.
Answer Relevance	Scores how directly the generated answer addresses the user's initial query.	Prevents tangential or evasive responses.
Context Precision	Evaluates whether the most relevant chunks were ranked highest.	Validates the effectiveness of the Re-ranker.

Engineering Pricing & Engagement

Our Generative AI solutions are engineered to order. Pricing is dictated by the complexity of the data ingestion pipelines, the necessity for custom fine-tuning, and the depth of system integrations.

Advanced RAG Pipelines: Knowledge extraction from standard enterprise formats (PDF, Confluence) with hybrid search and guardrails. Typical execution spans 4 to 8 weeks, ranging from $25,000 to $45,000.
Agentic Orchestration Systems: Multi-agent ReAct workflows deeply integrated into core APIs (e.g., Salesforce, SAP) capable of autonomous execution. Execution spans 8 to 14 weeks, ranging from $60,000 to $95,000.
On-Premise / Open-Weight Deployments: Secure deployment of fine-tuned open models (Llama, Mistral) on dedicated GPU clusters using vLLM for data sovereignty. Scoped independently based on infrastructure requirements.

Architect Your System

Frequently Asked Questions

Retrieval-Augmented Generation (RAG) is optimal for injecting external, frequently updating knowledge into an LLM at runtime without modifying its weights. Fine-tuning should not be used for knowledge injection; instead, it is required when adapting the model's tone, dialect, or strict structural output format (e.g., generating precise JSON schemas or specific programming languages). In many enterprise systems, we deploy both simultaneously.

We enforce determinism through a multi-layered approach: 1) Utilizing Cross-Encoder re-ranking to ensure only highly relevant context enters the context window. 2) Employing strict prompt engineering with negative constraints. 3) Implementing output validation layers (guardrails) that parse and reject non-compliant outputs. 4) Continuously evaluating the pipeline using RAGAS metrics like 'Faithfulness' and 'Answer Relevance'.

Agentic workflows involving tool use (via ReAct paradigms) inherently increase latency due to multi-step reasoning cycles. A single user query might trigger 3-5 sequential LLM inferences. To mitigate this in production, we leverage smaller, faster models (e.g., Llama 3 8B or GPT-4o-mini) fine-tuned specifically for tool-calling, coupled with aggressive semantic caching (e.g., using Redis) for repeated intermediate steps.

Yes. For organizations with strict data residency constraints, we bypass proprietary APIs (like OpenAI) and deploy open-weight models (e.g., Llama 3, Mistral) on dedicated hardware using vLLM or TensorRT-LLM frameworks. This ensures complete data privacy and fixed inference costs, decoupled from per-token billing.

Back to Services

AI Development Services

Generative AI & LLM Engineering

Architect production-grade Generative AI applications. Explore our technical methodologies for Advanced RAG, QLoRA fine-tuning, and deterministic Agentic Workflows.

Architect Your System

Table of Contents

Advanced RAG: Grounding Generation in Enterprise Context

Mathematical Foundation of Vector Retrieval

Dense retrieval calculates the distance between the embedded query vector $\vec{q}$ and document chunk vectors $\vec{d}$ using Cosine Similarity, maximizing the dot product of normalized vectors:

\text{sim}(\vec{q}, \vec{d}) = \frac{\vec{q} \cdot \vec{d}}{\|\vec{q}\| \|\vec{d}\|} = \frac{\sum_{i=1}^{n} q_i d_i}{\sqrt{\sum_{i=1}^{n} q_i^2} \sqrt{\sum_{i=1}^{n} d_i^2}}

Mitigating Context Dilution via Cross-Encoder Re-Ranking

QLoRA: Parameter-Efficient Fine-Tuning

W = W_{\text{NF4}} + A \cdot B

$W_{\text{NF4}}$ : Frozen 4-bit quantized base weights.
$A \in \mathbb{R}^{d \times r}, B \in \mathbb{R}^{r \times d}$ : Trainable low-rank matrices where rank $r \ll d$ .

Agentic Workflows & The ReAct Paradigm

Production LLM Evaluation & Guardrails

Evaluation Metric	Measurement Goal	Impact
Faithfulness	Measures if the generated answer is entirely inferable from the retrieved context.	Directly quantifies hallucination rates.
Answer Relevance	Scores how directly the generated answer addresses the user's initial query.	Prevents tangential or evasive responses.
Context Precision	Evaluates whether the most relevant chunks were ranked highest.	Validates the effectiveness of the Re-ranker.

Engineering Pricing & Engagement

Our Generative AI solutions are engineered to order. Pricing is dictated by the complexity of the data ingestion pipelines, the necessity for custom fine-tuning, and the depth of system integrations.

Advanced RAG Pipelines: Knowledge extraction from standard enterprise formats (PDF, Confluence) with hybrid search and guardrails. Typical execution spans 4 to 8 weeks, ranging from $25,000 to $45,000.
Agentic Orchestration Systems: Multi-agent ReAct workflows deeply integrated into core APIs (e.g., Salesforce, SAP) capable of autonomous execution. Execution spans 8 to 14 weeks, ranging from $60,000 to $95,000.
On-Premise / Open-Weight Deployments: Secure deployment of fine-tuned open models (Llama, Mistral) on dedicated GPU clusters using vLLM for data sovereignty. Scoped independently based on infrastructure requirements.

Architect Your System