AI Agent Development
AI Agents Builtfor Production,Not Just Demos
Redevon IT designs, builds and integrates AI agents into the workflows your team already runs, and stays on to support them after launch.
What we build
AI Agents Designed Around Your Business
Five shapes cover most of what companies ask us for. Anything that does not fit one of them gets built to shape.
Prototype vs Production
Building the Agent Is the Easy Part. Making It Reliable Is the Real Work.
Most agent projects stall in the gap between these two columns. It is the part that rarely makes it into a demo.
Prototype AI 06
- Impressive demo
- Curated inputs
- Limited integrations
- No failure handling
- Little monitoring
- Ideal conditions only
Production AI 07
- Real business data
- Live integrations
- Permissions & access control
- Error handling
- Human escalation
- Evaluation & monitoring
- Cost controls
Redevon IT focuses on production AI.
Everything in the right hand column is in scope from the first week, not added once something breaks.
Architecture
How We Build AI Agents Into Your Existing Systems
Nothing here is a black box. Every step is a decision you sign off on before we build it.
The dashed return is the feedback loop: traces and corrections go back into the agent, which is how it improves after launch rather than drifting.
CRM / ERP / Helpdesk / APIs / Databases / Document Stores / Email / Cloud Platforms
Evaluation & operations
Production AI You Can Test, Measure and Operate
The same agent, on both sides of launch. What we prove before it goes live, and what we watch once it is.
Reference implementation metricsFor systems that need ongoing engineering ownership after launch, see our Managed Platform Engineering & Operations service.
Relevant work
Production AI in Practice
A support triage agent running against a seeded CRM and help desk. It answers from the knowledge base, checks the account before it promises anything, and stops at a human whenever the next step would cost money or leave the system in a state it cannot undo.
For what separates a reference implementation like this from a demo, see AI Agents in Production: What Changes After the Prototype.
Process
From Opportunity Discovery to Production
A typical production engagement runs around 10 to 14 weeks, depending on integrations, data readiness and approval requirements. The stages overlap on purpose: nothing waits for a sign off that could have happened in parallel.
Workflow Assessment / Opportunity Mapping /Data & Integration Review / Technical Architecture / Risk Analysis / Implementation Roadmap / Optional Prototype
Fixed scope, fixed priceStage 01 runs on its own. If you are not sure where AI agents fit into your business yet, start there: two to three weeks, fixed price, and everything produced is yours whether or not we build the rest.
Discuss an AI Opportunity SprintDiscuss an AI Opportunity SprintOpenAI / Anthropic / Gemini / Azure OpenAI / LangGraph / Python / AWS / Google Cloud / Azure
Why Companies Work With Redevon IT for Production AI
Common questions
What teams usually
ask first.
A working prototype on your own data lands around week five to seven. It is deliberately not a demo: it runs the real workflow against real systems, which is the only way to find out whether the idea survives contact with your business.
No. Agents sit on top of what you run today (CRM, ERP, helpdesk, databases, document stores) through their existing APIs. If a system has no API, we say so during the sprint rather than discovering it in week nine.
It is designed on the assumption that it will. Permissions limit what it can touch, thresholds you set send anything consequential to a human first, and every run leaves a trace you can read. When something does go wrong, you can see exactly which step caused it.
Whichever one fits the workflow and the budget. We build behind an abstraction so the model is a decision you can revisit rather than a dependency you are stuck with. This matters, because the best model for a workflow changes quickly.
You do, including everything produced during the Opportunity Sprint. Your data stays in your own environment wherever the architecture allows it, and we will tell you plainly when it cannot.
AI agent development costs depend on workflow complexity, integrations, data readiness, evaluation requirements and the level of production engineering involved. We usually start with a scoped Opportunity Sprint, then provide an implementation budget once the architecture and risks are understood.
Model usage plus hosting, and both are visible from day one: cost per run is one of the metrics we monitor rather than a surprise on an invoice. Most workflows we ship sit in the low hundreds per month at production volume.
Start here
Tell Us the Workflow. We Will Tell You If an Agent Belongs There.
Bring one process that costs your team more time than it should. That is enough to start a useful conversation, and often enough for us to say it is not worth automating yet.