AI Engineer
An AI Engineer builds the brain of your business. We design, deploy, and orchestrate custom Large Language Model (LLM) agents that automate complex cognitive tasks.
What is an AI Engineer?
AI Engineering is not just about writing prompts. It's about building robust software systems around LLMs. It involves vector databases, RAG (Retrieval-Augmented Generation), tool calling, and memory management.
We bridge the gap between a raw model (like GPT-4) and a reliable business application that can execute workflows without hallucinating.
Why choose zenrev?
- Production-Grade Systems: We build agents that are reliable, observable, and secure—not just demos.
- Model Agnostic: We use the best model for the job, whether it's OpenAI, Anthropic, or open-source Llama.
- Business Focus: We engineer AI specifically for ROI-driven use cases like sales, support, and operations.
Who Needs an AI Engineer?
Companies ready to move beyond ChatGPT and build custom IP.
SaaS Startups
Enterprise Innovation Teams
Customer Support Leads
Data-Driven Agencies
Core Capabilities
Custom Agents
- Build autonomous agents
- Implement tool calling (Function Calling)
- Design multi-agent swarms
RAG Systems
- Vector database setup (Pinecone/Weaviate)
- Semantic search implementation
- Knowledge base ingestion
Prompt Engineering
- Advanced chain-of-thought prompting
- System prompt optimization
- Few-shot learning techniques
LLM Integration
- OpenAI API / Anthropic API
- LangChain / LlamaIndex
- Vercel AI SDK implementation
Fine-Tuning
- Fine-tune models on your data
- Reduce latency and cost
- Improve domain-specific accuracy
AI Security
- Prompt injection defense
- PII redaction
- Enterprise-grade governance
How We Build AI
Use Case Definition
We identify high-value tasks that are suitable for AI automation vs. deterministic code.
Data Preparation
We clean, chunk, and embed your proprietary data to give the AI context.
Agent Architecture
We design the cognitive flow: planning, reasoning, tool execution, and critique.
Evaluation (Evals)
We build automated test suites to measure accuracy and prevent regression.
Deployment & Monitoring
We deploy to production with tracing (LangSmith) to monitor costs and quality.
Example Agent Config
A simplified configuration for a Research Agent.
// Agent Definition: Market Researcher
const researcher = new Agent({
model: "gpt-4-turbo",
systemPrompt: `You are an expert market researcher.
Your goal is to find specific buying signals for B2B SaaS.
Always cite your sources.`,
tools: [
new WebSearchTool(),
new ScraperTool(),
new LinkedInLookupTool()
],
memory: new VectorMemory({ index: "market-data" }),
maxSteps: 10
});
// Execution
const report = await researcher.run(
"Find 50 SaaS companies in Dubai hiring for Sales roles."
);Alt: TypeScript code defining an AI agent for market research, created by zenrev using the Vercel AI SDK.
Global AI Deployment
Frequently Asked Questions
What does an AI engineer do?
An AI Engineer designs and builds applications powered by Large Language Models. We handle the prompt engineering, context management, tool integration, and infrastructure required to make AI useful.
What is RAG (Retrieval-Augmented Generation)?
RAG is a technique where we connect an LLM to your private data (PDFs, databases, Notion). This allows the AI to answer questions based on your specific business knowledge without hallucinating.
Do you build custom models or use APIs?
We mostly use frontier model APIs (OpenAI, Anthropic) because they offer the best performance-to-cost ratio. However, we can fine-tune open-source models (Llama 3, Mistral) for specific enterprise use cases.
How do you handle data privacy?
We design systems where your data is either processed ephemerally or stored in your own private cloud (AWS/GCP). We can also implement PII redaction layers before data is sent to any model provider.
Can AI replace my support team?
AI can resolve 40-80% of routine queries instantly, allowing your support team to focus on complex, high-value issues. We build 'human-in-the-loop' systems where the AI hands off to a human when unsure.
What is the difference between a Chatbot and an Agent?
A chatbot just talks. An Agent can DO things. Agents have access to tools (email, calendar, CRM) and can execute multi-step workflows to complete a goal.
How much does custom AI development cost?
Simple RAG chatbots start at $5k. Complex multi-agent systems with custom infrastructure typically range from $15k to $50k depending on scope.
Do you use LangChain?
Yes, we use LangChain and LlamaIndex for orchestration, but we also write lightweight custom implementations when performance and control are critical.
Build your AI workforce.
Chat with our AI assistant to discuss your use case, or book a consultation with a Lead AI Engineer.