Data Audit
Week 1
Inventory your data sources, assess quality, and define chunking and embedding strategy.
LLM Integration
LLM integration means connecting a large language model (such as GPT-4o, Claude, or Gemini) to your organization's private data, documents, and internal tools. The result is AI that answers questions from your actual knowledge base, takes actions in your systems, and stays grounded in your data rather than generating generic responses.
Last reviewed: February 2026
We connect large language models to your internal data, documents, and tools so your 10–200 person team gets accurate, grounded AI answers instead of generic chatbot responses. We build retrieval-augmented generation pipelines, function-calling APIs, and fine-tuned models scoped to your domain.
Who is this for?
Mid-market teams (10–200 employees) that want to use LLMs with their own data, not just a generic ChatGPT wrapper. Specifically:
- ◆Teams with internal knowledge bases, SOPs, or documentation that employees search daily
- ◆Companies that need AI to take actions in their systems, not just answer questions
- ◆Organizations handling sensitive data that can't be sent to public APIs without guardrails
- ◆Product teams embedding AI features (search, summarization, generation) into their own software
What do you get?
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A deployed LLM pipeline connected to your data sources and tools
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Retrieval-augmented generation (RAG) over your internal documents
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Function-calling / tool-use APIs so the model can take actions in your systems
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Evaluation suite measuring accuracy, latency, and cost per query
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Data privacy architecture: VPC hosting, zero-retention agreements, or on-prem options
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Documentation, runbooks, and 30 days of post-launch tuning
What does the timeline look like?
Pipeline Build
Weeks 2–3
Build the RAG pipeline or function-calling layer. Connect to your APIs and data stores.
Eval & Tune
Weeks 3–4
Run structured evaluations on your real queries. Tune retrieval, prompts, and model selection.
Ship & Monitor
Week 4+
Deploy to production. Set up monitoring for accuracy, latency, and cost. 30 days of post-launch support.
What are the common pitfalls?
Skipping evaluation
Teams that deploy without a structured eval set end up guessing whether the model is working. We build eval suites before shipping so you can measure accuracy on your actual queries.
Treating all LLMs as interchangeable
GPT-4o, Claude, and Gemini have different strengths in reasoning, instruction-following, and context length. Choosing the wrong model wastes money or sacrifices accuracy. We benchmark on your workload.
Ignoring cost at scale
LLM API costs can grow fast. A pipeline that costs $50/month in testing can cost $5,000/month in production. We optimize with caching, smaller models for simple tasks, and batched inference.
No grounding strategy
Out-of-the-box LLMs hallucinate. Without RAG or other grounding techniques, the model will confidently generate wrong answers. We anchor every response to your source documents.
FAQ
What's the difference between RAG and fine-tuning?
RAG feeds your documents to the model at query time, with no retraining needed. Fine-tuning adjusts model weights on your data for specialized tone or deep domain knowledge. Most teams start with RAG; fine-tuning makes sense when you need consistent output formatting or niche expertise.
Which LLM providers do you work with?
We're model-agnostic. We work with OpenAI (GPT-4o, o1), Anthropic (Claude), Google (Gemini), and open-source models (Llama, Mistral) hosted on your infrastructure. We recommend what best fits your latency, cost, and accuracy needs.
How do you handle sensitive or proprietary data?
Data privacy is designed in from the start. Options include VPC-hosted or on-premise models, zero-data-retention API agreements, and encryption at rest and in transit. Your data never leaves boundaries you haven't approved.
What if the model hallucinates?
We design for it. RAG pipelines ground responses in your actual documents. We add citation tracking so users can verify sources. For high-stakes workflows, we implement confidence scoring and human review checkpoints.
How long does a typical integration take?
A basic RAG pipeline can ship in 2–3 weeks. Function-calling integrations typically take 3–4 weeks. Fine-tuning projects run 4–6 weeks including data prep and evaluation. We show working software every week.
Ready to connect an LLM to your data?
Book a free 20-minute discovery call. We'll assess your data landscape and recommend the right architecture.
Book a Strategy Call →Sources
- Lewis et al., "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" (original RAG paper)
- OpenAI, "Function calling and tool use documentation"
- Anthropic, "Retrieval-augmented generation best practices"
- Google Cloud, "Grounding LLMs with real-world data"
- a16z, "Emerging Architectures for LLM Applications"
- Pinecone, "Chunking strategies for RAG applications"