Should we use GPT-4 or a smaller open-source model?

Depends on accuracy requirements, latency, and cost. We evaluate both options for your specific task. Many use cases work well with smaller fine-tuned models at 10-20% of the cost.

How do you handle hallucinations in RAG systems?

We ground answers in retrieved documents, use citation-based output formats, and build automated hallucination detection as part of the eval pipeline.

Can you build this inside our infrastructure (no third-party LLMs)?

Yes. We deploy open-source models in your VPC for full data privacy. We've run Llama and Mistral on-premise for regulated clients.

AI Tools & Integration

Connect your stack to language models. Properly.

LLM integration, RAG pipelines, retrieval systems, and agent workflows: built for reliability and production, not demos.

Talk to an engineer Book a 30-min Call

Free, no commitment. Takes about 30 seconds.

6 wksMedian to production

40%Avg manual work cut

3 daysFirst engineer shortlist

What we build

LLM and AI tool integration

RAG pipelines

Retrieval-augmented generation over your private document corpus. Accurate answers grounded in your data, with citations.

AI agents

Autonomous agents that plan, use tools, and execute multi-step tasks. Built on LangChain, LlamaIndex, or custom frameworks.

Embedding and search

Semantic search infrastructure: embedding pipelines, vector stores, and retrieval APIs that replace keyword search.

LLM fine-tuning

Fine-tune open-source models (Llama, Mistral, Falcon) on your domain data for improved performance and lower costs.

LLM evaluation and guardrails

Automated evaluation pipelines for LLM outputs. Hallucination detection, output classification, and content filtering.

API integration

Integrate OpenAI, Anthropic, Cohere, or open-source models into your product with proper error handling, caching, and cost control.

Stack

Tools and models we integrate

OpenAIAnthropicCohereMistralGroqLlama 3MistralFalconPhiGemmaLangChainLlamaIndexHaystackCrewAIPineconeWeaviatepgvectorChromaFAISS

Process

How we build LLM integrations

enzrossi-ai, process

$ step.01

$ step.02

$ step.03

$ step.04

step 01

Define the use case and constraints

We document what the LLM needs to do, what data it accesses, latency requirements, and cost budget.

step 02

Design the retrieval and context strategy

For RAG: chunk strategy, embedding model selection, retrieval architecture, and context window management.

step 03

Evaluate outputs systematically

We build an evaluation dataset and automated scoring pipeline before any code goes to production.

step 04

Deploy with observability

Logging, latency tracking, token cost monitoring, and output quality metrics from day one.

Before you decide

The doubts everyone has before hiring us.

And what we do about each one.

AI projects never make it past the prototype.
Your data is assessed in the first week, and you get an honest answer on whether there is enough signal to build something useful.
Data assessment in week one
This will just be a chatbot on top of an API.
Language models are used when they fit, like text and language tasks.
The right tool for the problem
Our data is too sensitive to leave our infrastructure.
Model training can run entirely in your cloud, so your data never leaves your infrastructure.
Training in your cloud
We'll end up locked in to you.
Trained weights, training code, data pipelines and all documentation transfer to you on delivery.
Models and code are yours

FAQ

Common questions

Connect your stack

Tell us what you want LLMs to do in your product.

Start a conversation Book a 30-min Call

Free, no commitment.Prefer email? contact@enzrossi.com

What happens next

1
You tell us what you need. It takes about 2 minutes.
2
A real person replies within 4 business hours.
3
A 30-minute call to understand the project and agree on next steps.