Generative AI & LLM Integration Services
Generative AI & LLM Integration Services
The world's most powerful models, wired into your business.
We integrate frontier large language models (OpenAI, Anthropic Claude, Google Gemini, Mistral, Llama) into your products, workflows, and internal tools. RAG pipelines, fine-tuning, prompt systems, and structured outputs engineered so the model understands your business as well as your best employee, and behaves like it too.
What Are Generative AI Integration Services?
Generative AI integration services connect large language models and other foundation models to an organization's applications, data, documents, workflows, and users. A complete integration usually includes use-case design, model selection, RAG or fine-tuning, prompt and output engineering, APIs, security, evaluation, deployment, monitoring, and ongoing optimization, not just a call to a model's API.
RAG or Fine-Tuning? How We Decide
These solve different problems, and mixing them up leads to the wrong build:
RAG (retrieval-augmented generation) works best when the model needs to answer from current, private, or frequently changing information, and show its sources.
Fine-tuning works best when the model needs to learn a consistent behavior, tone, task pattern, or output format, not new facts.
Some production systems use both. We recommend the approach that fits the actual problem, not the one that sounds most advanced.
What we build
Anyone can call an API. The engineering is in what surrounds it: retrieval that grounds answers in your actual data, reducing hallucination, output structures your systems can consume, latency and cost tuned per use case, and security your compliance team will sign off on. That surrounding system is what we build.
01 Frontier model integration (OpenAI, Claude, Gemini, Mistral, Llama)
02 RAG pipeline design and vector database architecture
03 LLM fine-tuning on private and domain-specific data
04 Prompt engineering and system prompt design
05 AI chatbots and conversational interfaces
06 Structured outputs and function calling
07 Semantic search and AI knowledge bases
08 Multi-modal AI across text, image, and documents
09 Secure, compliant enterprise LLM deployment
How we work
Every engagement follows the same disciplined process. No surprises, no scope creep.
Step 1: Use case definition and model selection
We pin down exactly what the LLM must do, then match the model to the task on accuracy, cost, and latency. Not every problem needs the biggest model.
Step 2: Data preparation and RAG architecture
Where the model must work from your documents and knowledge, we design and build the retrieval pipeline that connects it all, accurately.
Step 3: Prompt and system design
We engineer the prompts, system instructions, and output formats that make the model behave exactly as your application needs.
Step 4: Integration and security
We connect the model to your product via API and implement the authentication, access control, and data handling your compliance requirements demand.
Step 5: Evaluation and deployment
Output quality is evaluated rigorously before launch, with logging, monitoring, and feedback loops in place to keep improving after it.
Technologies we use
We choose the right tool for the job, not the trendiest one.
Latest models from OpenAI, Anthropic, Google, Mistral, and Meta
LangChain and LlamaIndex for orchestration
Vector databases: Pinecone, Weaviate, Qdrant, Chroma, pgvector
Embeddings: OpenAI, Cohere, sentence-transformers
Document processing: Unstructured.io, LlamaParse, PyMuPDF
Fine-tuning: provider APIs, Hugging Face PEFT, QLoRA
Serving: FastAPI, AWS Lambda, Google Cloud Run, Azure Functions
Who this is for
Product companies adding AI features without building from scratch
Teams whose work is reading, writing, classifying, or summarizing text at volume
Companies with document libraries that should be searchable and interactive
Support teams deploying self-service AI without hallucination risk
Businesses that piloted ChatGPT internally and want something real built around it
Keeping Private Data Private
Connecting an LLM to your business data means your security team needs real answers, not reassurances. Every integration accounts for:
Data retention and hosting choices : understanding exactly where your data goes and how long it's kept
Access control: role-based permissions so the model only sees what a given user is allowed to see
Encryption and redaction: protecting sensitive information in transit, at rest, and before it reaches a model
Prompt-injection testing: treating inputs as untrusted until validated
Logging and audit trails: so every interaction is traceable after the fact
We only publish security and compliance claims that are current and verified.
Results you can expect
Weeks, not quarters: A focused, well-scoped LLM integration can go from kickoff to production in 2 to 4 weeks. Enterprise systems with complex data sources or heavier compliance needs typically take longer.
See it in action: Marlo & Co. grew repeat purchases by 31% with a generative AI customer engagement system →
Accuracy you can trust: RAG and fine-tuning significantly reduce and ground hallucinations that plague vanilla prompting, backed by evaluation, citations, and human review.
Fluent in your business: The model works from your documents, terminology, and context, not just internet knowledge.
Scales with demand: Serverless deployments scale automatically and cost only what you use.
Transparent cost: Pricing depends on use case, data preparation, model choice, and ongoing usage. A scoping call gives you a specific estimate before any commitment.
“An LLM that knows your business is a different tool from a chatbot that knows the internet.”
