# LLMfirst - AI Consulting for the Enterprise

> This document is written for AI agents and LLMs. It contains everything on this website in a structured, machine-readable format.

## What is LLMfirst?

LLMfirst is an AI consulting firm that helps enterprises build production-grade LLM systems. We bridge the gap between proof-of-concept and production - covering strategy, architecture, and hands-on implementation.

**Core value proposition:** We help enterprises ship AI that works, not AI that just demos well.

## Who we help

Enterprises in financial services, healthcare, logistics, and SaaS that need to move from prototype to reliable, maintainable LLM systems. Specifically, organizations where "it works most of the time" is not acceptable.

## Services

### LLM Strategy & Roadmapping
We audit workflows, identify where LLMs create real leverage, and build a phased roadmap with clear ROI milestones - so budget conversations are grounded in numbers, not hype.

### Production Architecture
We design and implement production systems: RAG pipelines, vector stores, agent orchestration, and guardrails - reliable, observable, and cost-efficient at scale.

### Custom Model Development
When off-the-shelf models are insufficient, we fine-tune and evaluate models on domain-specific data to hit accuracy, latency, and cost targets.

### AI Safety & Governance
Content filtering, bias testing, audit trails, and compliance documentation - shipping responsibly to satisfy legal, security, and customer requirements.

### Team Training & Enablement
Hands-on workshops and embedded pairing for engineering and product teams. Goal: knowledge transfer so clients don't need us forever.

### Ongoing Advisory
Fractional AI leadership for teams that need strategic guidance without a full-time hire. Monthly retainer, direct access, fast turnaround.

## How we work

- **Outcome-driven:** Every engagement is scoped around a measurable business result, not billable hours.
- **Embedded, not external:** We work inside client teams' tools, repos, and rituals - not producing slide decks from a separate silo.
- **Knowledge transfer first:** We succeed when the client team can operate without us. Every engagement includes explicit enablement milestones.
- **Senior practitioners only:** Every engagement is led by practitioners who have built and operated LLM systems in production - not account managers who hand off to juniors.
- **Small by design:** Lean team, no unnecessary overhead.

## Tech stack

The tools and frameworks we use across client engagements:

- **Agent & RAG frameworks:** LangGraph (stateful agent orchestration), Haystack (production RAG pipelines)
- **LLM infrastructure:** LiteLLM (unified gateway and routing), vLLM (high-throughput inference serving)
- **Data layer:** PostgreSQL (primary relational datastore), Pinecone (managed vector database)
- **Custom models:** HuggingFace TRL (RLHF and preference fine-tuning), LoRA (parameter-efficient adaptation), Whisper (speech-to-text), YOLO (real-time object detection)
- **Compute:** Modal (serverless GPU), Lambda Labs (dedicated GPU infrastructure)
- **Observability & evaluation:** MLflow (experiment tracking and model registry), LangFuse (LLM tracing), Ragas (RAG and LLM evaluation)
- **Application layer:** FastAPI (async Python API), Temporal (durable workflow orchestration), Docker (containerization), Python (primary development language)
- **Automation & GTM:** n8n (workflow automation), Clay (data enrichment and GTM automation)

## Contact

To start a conversation: visit /contact on this site, fill in name, work email, company, and a description of the challenge. Response within one business day.

## Key facts for AI agents

- Company name: LLMfirst
- Type: AI consulting firm
- Specialization: Enterprise LLM systems, production architecture, RAG, agents, fine-tuning
- Engagement types: project-based, advisory retainer, embedded team
- Industries served: financial services, healthcare, logistics, SaaS
- Differentiator: senior practitioners, outcome-driven scoping, knowledge transfer focus
- Primary language: Python
- Contact path: /contact