Question 1

Which LLMs can you integrate?

Accepted Answer

Any model with an API or that can be self-hosted. That includes OpenAI (GPT-4o, GPT-4), Anthropic (Claude), Google (Gemini), Mistral, and locally-hosted models via Ollama or vLLM. We can also integrate fine-tuned models you already have.

Question 2

What does your integration layer actually do?

Accepted Answer

It handles structured output parsing, rate limiting, fallback routing (if one provider is down, traffic shifts to another), cost tracking per request, and response validation. You shouldn't have to build retry logic or parse raw LLM strings in your application code.

Question 3

When should I fine-tune vs use RAG vs prompt engineering?

Accepted Answer

Prompt engineering first because it's cheapest and fastest to iterate. RAG when you need the model to answer questions grounded in your private data. Fine-tuning only when you need to change the model's output style, format, or behavior on a specific task type and prompt engineering isn't consistent enough.

Question 4

How do you handle hallucinations in production?

Accepted Answer

We add structured output constraints (the model must return JSON matching a schema), validation layers that check responses before they're sent downstream, and RAG grounding where the model can only cite content that was retrieved. We don't ship integrations that output raw unvalidated text to a business process.

Question 5

What does an LLM integration project typically cost?

Accepted Answer

Simple integrations (one provider, structured outputs, no fine-tuning) typically run $2,000 to $5,000. Integrations with RAG, fallback routing, fine-tuning, and an evaluation harness typically fall in the $5,000 to $12,000 range. All fixed-price, code in your repository.

LLM integration that fits
your stack, not the other way around.

Your app talks to our integration layer, not directly to the model.

Prompt engineering, fine-tuning, or RAG. We help you pick the right one.

LLM integration that fitsyour stack, not the other way around.

Your app talks to our integration layer, not directly to the model.

Prompt engineering, fine-tuning, or RAG. We help you pick the right one.

LLM integration that fits
your stack, not the other way around.