AI Agent Development
AI agent development services for business workflows
Build an AI agent around a defined task, the systems it needs to use and the decisions your team needs to control. Redevon IT develops AI agents and integrations for existing business workflows, and plans how each one will be evaluated and supported after launch.
What we build
AI Agents Designed Around Your Business
Five workflow types we assess and build. Each describes a typical scope; the first step is confirming that an agent, rather than a fixed rule or a conventional integration, fits your process.
Prototype vs Production
Building the Agent Is the Easy Part. Making It Reliable Is the Real Work.
Many 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.
The right hand column is planned into the scope of a production engagement, sized to the workflow, rather than added once something breaks.
Architecture
How We Build AI Agents Into Your Existing Systems
An example design, adapted to each workflow. The agent reads what it needs, its requests are checked against permissions, and consequential changes wait for a person before they reach your systems.
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
Agree what a successful result means for the workflow before building it. These are the areas checked before an agent goes live, and what is watched once it is.
Changes to models, prompts, source information or connected systems are checked before wider use; read AI agents in production for the detail. For ongoing ownership after launch, see Managed Platform Engineering & Operations, and for the infrastructure underneath, DevOps consulting and cloud engineering.
Relevant work
Production AI in Practice
A support triage agent running against a seeded CRM and help desk with test data. 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
First stage of the engagementNot sure where an agent fits yet? Stage 01 exists to answer that before a build is committed. Bring one workflow and we will assess it with you.
Assess an AI WorkflowAssess an AI WorkflowOpenAI / 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.
Cost depends on the workflow, integrations, source information, permitted actions, evaluation and operational requirements. Projects start from US$3,000 (£3,000). Start with the process you need to improve so the work can be scoped. Read our AI agent cost guide; its planning ranges are not a project quote.
Running costs include model usage, hosting, retrieval or storage, external tools and monitoring. 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.
A conversational interface may only need to answer questions. An agent can also select and request permitted actions within a workflow. The right approach depends on the task and the level of control it requires.
Agents work on top of the CRM, ERP, helpdesk, databases and document stores you already run, through their existing APIs. Share the systems and access arrangements so the integration can be assessed, including which records it can read and which changes need approval. If a system has no API, we say so during the assessment.
It is designed on the assumption that it sometimes will. Permissions limit what it can touch, anything consequential goes to a person first, and every run leaves a trace you can investigate. These controls reduce exposure; they do not make every output correct.
Whichever fits the workflow and the budget, including OpenAI, Anthropic and Gemini models. We build behind an abstraction so the model is a decision you can revisit rather than a dependency you are stuck with.
A typical production engagement runs around 10 to 14 weeks, depending on integrations, data readiness and approval requirements. A working prototype on your own data usually arrives around weeks five to seven.
Monitoring, incident handling and updates when models, source information or integrations change are agreed as part of the engagement. Support is provided during business hours, with response targets for each priority level set in the agreed scope. Explore ongoing platform and application management.
Start here
Tell Us the Workflow. We Will Tell You If an Agent Belongs There.
Bring one process, the systems it uses and the outcome your team needs. That is enough to discuss whether an agent fits, and often enough for us to say it is not worth automating yet.