Hire Your First
AI Employee.
Stop paying humans to do robot work. We build autonomous AI Agents that integrate with your data, execute complex workflows, and work 24/7 without burnout.
From Chatbots to "Doing-bots".
Most companies are stuck using ChatGPT for basic text generation. That is Level 1. Level 2 is building Autonomous Agents that have access to your live data and tools.
At Citual, we build agents that can read an invoice, verify it against a PO in your ERP, and schedule the payment—all without human intervention. This is Agentic AI.
Process Mining (Discovery)
AI isn't magic; it's math. We start by auditing your manual workflows. Where are your people spending time copy-pasting data? Where are the decision bottlenecks?
We map the process and calculate the Potential ROI. If an AI agent can't save you at least 30% efficiency, we don't build it.
RAG Data Architecture
Generic AI hallucinates. To fix this, we build a Retrieval-Augmented Generation (RAG) system. We convert your PDFs, Notion docs, and SQL data into "Vector Embeddings."
This gives the AI a "Long-Term Memory" specific to your business, ensuring it answers questions with 100% factual accuracy based on your data.
Agent Logic Design
We design the "Brain." Using frameworks like LangChain or AutoGPT, we define the agent's reasoning loops.
For complex tasks, we use Multi-Agent Systems—where one AI "Researcher" gathers data and passes it to an AI "Writer" to draft the report, mimicking a human team structure.
Tool Use & Integration
An agent that can't "do" anything is just a chatbot. We give your agent Tools.
We connect it to your APIs (Slack, HubSpot, Gmail) using Function Calling. Now, your agent can actually send the invoice or update the CRM record autonomously.
Guardrails & Safety
You can't have an AI going rogue. We implement strict Input/Output Guardrails.
We ensure the AI refuses off-topic requests ("Write a poem") and sanitizes any PII (Personally Identifiable Information) before processing, keeping your enterprise data compliant.
Development & Fine-Tuning
We code the agent logic in Python/TypeScript. We rigorously test System Prompts to ensure the AI adopts the right tone (Professional vs. Casual).
In rare cases, we perform Fine-Tuning on open-source models (Llama 3, Mistral) if you need the AI to learn a very specific internal language or format.
Red Teaming (Testing)
Before launch, we try to break it. Our "Red Team" attacks the agent with adversarial prompts to see if we can trick it into revealing sensitive data.
We fix these leaks immediately, ensuring the agent is robust enough for real-world user interaction.
Deployment & Observability
We deploy the agent to your cloud (AWS/Azure). But we don't walk away. We set up LLM Observability (using LangSmith or Helicone).
We monitor token costs, latency, and user feedback in real-time, optimizing the model continuously to improve accuracy and lower costs.
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