Embed intelligence into your existing product. We integrate GPT-4, Claude, Gemini, and open-source models, fine-tuned on your domain data for maximum accuracy. Your product gets smarter without a rebuild.
We evaluate GPT-4, Claude, Gemini, Llama, Mistral, and specialized models against your use case, balancing accuracy, latency, and cost.
We clean, format, and augment your domain data into training sets and retrieval corpora optimized for your specific tasks.
We fine-tune the selected model on your data and build a RAG pipeline for real-time knowledge retrieval, so answers stay current.
We embed the model into your product via a clean API layer, with streaming, caching, fallback routing, and usage monitoring built in.
Which LLMs do you work with?
GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, Llama 3, Mistral, and a range of open-source models. We evaluate options against your use case, not every task needs the most expensive model.
What is RAG and do I need fine-tuning?
RAG (retrieval-augmented generation) lets the model answer questions using your documents without retraining. Fine-tuning adjusts the model on your domain data. Most clients start with RAG, it's faster and more maintainable.
How do you handle data privacy and security?
We can deploy models inside your own cloud environment so data never leaves your infrastructure. For API-based models, we advise on data masking and residency requirements. We don't store your data on our systems.
How long does LLM integration take?
A straightforward integration into an existing product takes 3–5 weeks. RAG pipelines with knowledge bases take 4–8 weeks. Fine-tuning projects vary based on data availability and task complexity.
Core capabilities delivered in every LLM Integration & Fine-tuning engagement.
We build RAG pipelines that embed your documents into a vector store, retrieve the most relevant chunks at inference time, and inject them into the model prompt. Your LLM answers questions based on your actual data, not its training cutoff.
When a general-purpose model consistently underperforms on your specific task, we fine-tune using your labeled examples. Fine-tuned models outperform prompting alone for structured output extraction, classification, and consistent tone-sensitive generation.
Production LLM systems route different request types to different models based on complexity and cost. We build routers that send simple requests to fast, inexpensive models and escalate complex reasoning tasks to more capable ones automatically.
Long-form generation in product interfaces requires streaming. We implement token-streaming APIs that begin delivering output within 200 milliseconds, with proper SSE handling, error recovery, and client-side rendering for smooth user experience.
Identical or semantically similar requests are served from cache rather than triggering a new inference call. Caching reduces latency, cuts API costs by 30–60% on high-traffic endpoints, and improves consistency of repeated queries.
We build evaluation frameworks that measure model accuracy on held-out test cases, track performance over time, and detect prompt drift before it reaches production. Systematic evals replace subjective vibe-checking of AI output quality.
We work with teams across these functions and industries.
Companies adding AI-powered features to an existing product without building an ML team. We integrate LLMs into your backend via a clean API layer, with streaming, caching, and model fallback handled transparently for your engineering team.
Law firms, accountancies, and consultancies with large document volumes. We build document intelligence systems that extract key clauses, classify documents, generate summaries, and flag anomalies against defined templates at scale.
Organizations where critical knowledge is locked in documents, wikis, and email threads. A RAG-based knowledge base lets employees query the full institutional knowledge base in natural language, reducing search time by 40–60% per user.
Clinical teams processing EHR notes, discharge summaries, or research literature at scale. We fine-tune models on clinical text to extract structured data, flag risk indicators, and generate compliant documentation drafts for clinical review.
API cost reduced 40–60% on high-traffic endpoints through semantic caching and intelligent model routing
RAG pipeline accuracy reaches 88–94% on domain-specific questions with well-designed retrieval and reranking
Fine-tuned models outperform GPT-4 prompting on domain-specific extraction tasks by 20–35% in structured output accuracy
Document review time for standard contract clauses reduced by 70–80% with LLM-assisted extraction and flagging
LLM integration means embedding large language model intelligence directly into your existing product or internal workflows. The most common integration patterns are RAG (retrieval-augmented generation), where the model answers questions by retrieving relevant documents from a knowledge base at inference time; fine-tuning, where the model is retrained on your domain-specific data to improve accuracy on specialized tasks; and API integration, where the model is called as a service within your existing application or automation pipeline.
The model selection decision is often the most consequential early choice in an LLM integration project. GPT-4o, Claude Sonnet, and Gemini 1.5 Pro all perform well on most tasks, but they have meaningful differences in instruction-following reliability, output format consistency, behavior on long contexts, and pricing. For structured data extraction tasks, Claude's instruction-following tends to produce more consistent JSON output. For creative generation and tone matching, GPT-4o is often preferable. For very long document processing, Gemini's 1-million-token context window changes the architecture options significantly. We evaluate models against your actual use case before recommending one.
The engineering around the LLM call is as important as the model itself. A production LLM integration needs prompt versioning — tracked like code, with the ability to roll back to a previous prompt if a model update changes behavior. It needs output validation — structured parsing of the model's response with fallback handling for malformed outputs. It needs cost and latency monitoring — tracking token usage per request, P95 latency, and error rates. And it needs evaluation infrastructure — a way to measure whether model performance is improving or degrading over time as prompts and models change.
Kodesinc has shipped LLM integrations across document intelligence, customer support automation, internal knowledge management, healthcare clinical tools, and SaaS product features. We work with GPT-4o, Claude, Gemini, Llama 3, Mistral, and domain-specific models. Integrations are delivered with clean API layers, comprehensive documentation, and evaluation frameworks so your team can measure and improve performance after handover.
Prompt engineering at the level required for production deployments is more systematic than iterative. A prompt that performs well during testing may degrade in production when it encounters a distribution of inputs different from the test set. We treat prompts as code: version-controlled, peer-reviewed, deployed through a defined release process, and monitored in production for accuracy regressions. Every prompt in our integrations includes explicit output format specifications, few-shot examples covering the most common input variations, and chain-of-thought scaffolding for tasks requiring multi-step reasoning. When a model update changes behavior, the version control history lets you identify exactly which prompt change or model change caused the regression.
Data privacy is a frequent concern for organizations integrating LLMs, particularly those in healthcare, financial services, and legal services. The architecture options range from API-based integration with commercial providers like OpenAI and Anthropic, where you need to carefully manage what data is included in requests; to private cloud deployments of open-source models like Llama 3, where all data processing stays within your own infrastructure. For clients in regulated industries, we design integrations that never send identifiable or sensitive data to external APIs, instead using anonymization, de-identification, and synthetic data substitution before any external call is made. We document the data flow for each integration as part of the compliance deliverables.
The integration architecture we build includes cost controls from day one. LLM API costs scale directly with usage, and a production system that makes thousands of API calls per day can generate significant costs if calls are not engineered carefully. We implement prompt compression to minimize token usage without degrading accuracy, semantic caching to serve repeated or near-identical queries from cache rather than triggering new inference, model routing to direct simple classification tasks to faster and cheaper models while reserving the most capable models for complex reasoning, and budget alerting to notify you before costs breach defined thresholds. Cost awareness is an architectural decision, not an afterthought.