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

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.

FAQ

Common questions

Connect your stack

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

Start a conversation

Connect your stack to language models. Properly.

LLM and AI tool integration

RAG pipelines

AI agents

Embedding and search

LLM fine-tuning

LLM evaluation and guardrails

API integration

Tools and models we integrate

How we build LLM integrations

Define the use case and constraints

Design the retrieval and context strategy

Evaluate outputs systematically

Deploy with observability

Common questions

01Should we use GPT-4 or a smaller open-source model?

02How do you handle hallucinations in RAG systems?

03Can you build this inside our infrastructure (no third-party LLMs)?

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

Connect your stack to language models. Properly.

LLM and AI tool integration

RAG pipelines

AI agents

Embedding and search

LLM fine-tuning

LLM evaluation and guardrails

API integration

Tools and models we integrate

How we build LLM integrations

Define the use case and constraints

Design the retrieval and context strategy

Evaluate outputs systematically

Deploy with observability

Common questions

01Should we use GPT-4 or a smaller open-source model?

02How do you handle hallucinations in RAG systems?

03Can you build this inside our infrastructure (no third-party LLMs)?

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