AI Automation Services
AI automation services from GrowthVector build production AI workflows and agents that take repetitive work off your team: research, data entry, document handling, content operations, and reporting. Each workflow plugs into the tools you already use, with a human review step wherever a mistake would cost money.
What our AI automation services include
- AI workflow automation: multi-step pipelines that read, decide, and write across your tools
- AI agents built with LangChain and LangGraph, with clear limits on what they can do
- Retrieval-augmented generation (RAG): knowledge bases that answer from your own documents with sources
- LLM integrations with Claude, OpenAI, and Gemini models inside your existing apps and back office
- Automations in n8n or Zapier where a no-code tool is the right fit, and custom code where it is not
- Deployment on Google Cloud or AWS
- Human-in-the-loop review steps, guardrails, and approval queues for high-risk actions
Workflows teams usually automate first
- Keyword research and ad copy drafts generated from your own data
- Sorting, tagging, and routing incoming documents, tickets, or leads
- Internal search across docs, policies, and past projects
- Recurring reports assembled from several data sources
- Data cleanup and enrichment before records enter your CRM
How we work
- 1Tell us about the project in the form on this page: what you need, your website, and any deadline.
- 2We talk it through. That is usually a series of calls and emails back and forth, until the goals, constraints, and what done looks like are clear on both sides.
- 3You get a written proposal with the scope, the timeline, and the price.
- 4We do the work and keep you updated as it moves. At the end you get a clean handoff, or ongoing support if you want it.
Pricing
Every AI automation project is quoted after scoping. The price depends on the number of steps and tools in the workflow, the data the model needs, how much human review the process requires, where it runs, and the expected volume, which also sets the ongoing model and hosting costs.
Common Questions
How much do AI automation services cost?
We quote after scoping. Build cost follows the number of steps and integrations and the amount of review and testing the workflow needs. There is also a running cost for model usage and hosting, which depends on volume, so the proposal estimates both before you commit.
What is the difference between an AI agent and an AI workflow?
A workflow follows steps you define, with a model doing specific tasks inside them, such as classifying a document or drafting a reply. An agent decides its own next step and which tool to call. Workflows are more predictable and cheaper to run, so most business processes start as workflows, and agents are used where the path really varies.
How do you stop an AI automation from making costly mistakes?
The model only gets the tools and data the task needs, and high-risk actions wait for a human approval before they run. Guardrails check the model's output against rules you set, so a bad result is caught before it reaches a customer or your records.
Do you use n8n, Zapier, or custom code?
Whichever fits. n8n and Zapier are quick to build and easy for a team to adjust, which suits simple integrations. Custom code in Python or TypeScript suits workflows with complex logic, high volume, retrieval over your documents, or strict review and logging needs.
Get a quote
Tell us what you need and we will reply with next steps. See all services.