AI Agents & Automation: RAG, LangGraph & Real Workflows
We build AI you can put in front of real users: agents that follow a procedure, chatbots grounded in your own documents, and models that hold up in production. LangChain and LangGraph are what we work in most days.
- Grounded in your data, with honest fallbacks
- Human approval where decisions actually matter
- Six AI systems delivered, from HR agents to computer vision
Chatbots, RAG and Multi-Agent Systems
- Chatbots grounded in your own documents (RAG)
- Multi-agent workflows with LangChain and LangGraph
- No-code and low-code automation (n8n, plugins) when it beats a custom build
- Human-in-the-loop approvals for high-stakes steps
- Retrieval over vector databases, including your CSVs and docs
- Computer vision: recognition, detection and model tuning
- Model evaluation and feasibility work before you spend
- Streaming, low-latency chat interfaces
- Deployment, monitoring and ongoing maintenance
How an AI Build Runs
- 1
Assess & scope
A free call and an AI-readiness check: how your systems, data and workflows sit today, and what level of AI they're ready for. You get a written plan, a fixed quote, and an honest answer if a plugin or no-code automation beats a custom build.
- 2
Prototype
We build the smallest version that proves the idea, evaluate the models, and show you real output early.
- 3
Build
We wire it into your product with retrieval, guardrails and human approval where it counts.
- 4
Ship & maintain
We deploy, monitor and keep answers grounded, or hand it over with documentation.
“The team at Prograsec went above and beyond to ensure our project was completed to the highest standard. Their expertise and strong communication made the process seamless.”
Case Studies in This Area
Case study
AI Workplace-Procedures Platform
A LangGraph multi-agent platform that guides HR practitioners through high-stakes workplace procedures, with a human in the loop at every consequential step.
Read the case study →Case study
AI Key-Recognition System
A computer-vision rebuild that roughly doubled key-recognition accuracy for a property-tech product, from 40% to about 80%.
Read the case study →Case study
Cause Systems
A chatbot platform that answers from the open web, a company's own documents, or both, grounded, with fallbacks that keep answers honest.
Read the case study →Case study
Cause News
An AI news-verification engine that cross-checks claims across sources, maps how they relate, and lets you interrogate the result.
Read the case study →Case study
Manyface
A scalable multi-agent chatbot platform built by a five-engineer Prograsec backend team over roughly a year, with low-latency streaming responses.
Read the case study →Asked on Most First Calls
Not sure your business is even ready for AI?
That's what the first call is for. We look at how your systems sit today (is your CRM connected, is your data reachable, do you have end-to-end visibility) and tell you honestly what AI you're ready for. Sometimes that's a chatbot or an automation on top of what you already have; sometimes the smarter first move is tightening the ground layer before any AI goes on it. The assessment is free.
Do you always build custom, or use existing tools?
Whatever actually fits. Not every problem needs a custom LangGraph agent; often an n8n or no-code automation, or an off-the-shelf AI plugin wired into your stack, does the job faster and cheaper. We reach for custom when the problem genuinely needs it, and say so when it doesn't.
Do you build agents, or just chatbots?
Both. A chatbot answers questions; an agent follows a procedure and takes actions. We've built a 12-step workplace-investigation agent in LangGraph as well as document-grounded chatbots, and we scope which one your problem actually needs.
Can the AI use our own data?
Yes. We build retrieval over your documents, databases and CSV files, with fallbacks that keep answers grounded when retrieval comes up empty, so the model doesn't invent things.
How do you keep the AI from hallucinating?
Grounding in your data, retrieval with honest fallbacks, human approval on consequential steps, and evaluation before launch. We'd rather the system say it doesn't know than make an answer up.
Can you improve an AI feature we already have?
Yes. One client's key-recognition feature worked 40% of the time; we rebuilt the module and roughly doubled its accuracy, to about 80%.
What does an AI build cost?
It depends on scope, and anyone quoting a number before scoping is guessing. You get an exact figure in writing after a free scoping call.
Have an AI idea, or not sure where AI fits?
Tell us what you want the AI to do, or just where your business feels stuck. We'll assess what you're ready for and tell you honestly what it would take, custom build or not.
Or email business@prograsec.com, and we reply within one business day.