AI that ships, not demos
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
- Agents, RAG, and computer vision in production
If you already have a public website and want an assistant answering from those pages, request a website preview.
what we build
Chatbots, RAG and Multi-Agent Systems
Custom agents, retrieval, and automation on your data. If you only want an assistant on an existing public website, that is a different offer.
- 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 we work
How an AI Build Runs
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. Bounded work can have a fixed quote after that. If the idea is still uncertain, we start with a scoped prototype. We will say when a plugin or no-code automation beats a custom build.
Prototype
We build the smallest version that proves the idea, evaluate the models, and show you real output early.
Build
We wire it into your product with retrieval, guardrails and human approval where it counts.
Ship & maintain
We deploy, monitor and keep answers grounded, or hand it over with documentation.
“Prograsec consistently delivers top-notch results. Their dedication to understanding our requirements and delivering beyond our expectations is truly impressive. They not only provided excellent development work but also offered valuable feedback on how we could improve our platform. We look forward to working with Prograsec again!”
questions
Before we start
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. If you only want an assistant on an existing public website, request a private preview on the website assistant page.
Can the AI use our own data?
Yes. We build retrieval over your documents, databases and CSV files. Evaluation, refusal paths and human review reduce the risk of unsupported answers. The system can still make mistakes, so we agree how failures are caught and handled.
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?
There is no package price. An indicative estimate needs clear assumptions about the data, workflow and required behavior. Uncertain work may start with a separately scoped prototype or discovery before a production quote. Monitoring and model upkeep after launch are quoted separately. The guide What It Costs to Run an AI Feature in Production shows how to turn a published token rate into a monthly model bill and a reviewer bill.
Before you hire
Plan your next step
Buyer guide
What It Costs to Run an AI Feature in Production
A worksheet for AI feature cost: published token rates, cache hits, retries, and reviewer time. Send it when you request a project scope.
Read the guideBuyer guide
How to Hire an AI Development Agency Without Getting Burned
Evaluate an AI development agency with a test-set checklist, sample failure cases and questions about data access, costs and handover.
Read the guideBuyer guide
LangChain vs LangGraph: Which Does Your AI Project Need?
Compare LangChain’s agent interface with direct LangGraph orchestration. When to use each, when a retrieval pipeline is enough, and a 12-step workflow example.
Read the guideHave 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. If you already have a public website and want an assistant on it, use the website assistant preview instead.
Or email business@prograsec.com. We reply within one business day.