One focused workflow
Basic
A focused agent for one main use case, automation, or primary data source or integration.
- Single integration or automation
- Agent build and testing
- Deployment to the agreed environment
- Documentation and handover
How We Work
From AI idea to production agent.
We move from a business workflow to a production AI agent connected to your systems, data, and approval rules. First a free call, then paid Discovery, a fixed-scope build, and an explicit operating model after launch.
Process
A clear path from first call to operations.
01Free 30-minute call
First, we check if there is a real opportunity.
This is a practical fit call. We talk through your business, the workflow you want to improve, the systems involved, and whether an AI agent is the right tool for the job before deciding if it makes sense to go deeper.
What we look for
- a repeated workflow that costs time, money, or attention;
- business data that already exists but is hard to use;
- a clear team or process that would benefit from an agent;
- enough access, ownership, and urgency to make implementation realistic.
You get
- an honest recommendation;
- a clear next step;
- a decision on whether Discovery is worth doing.
If the problem is not a good fit for AI, we will say so.
02AI Strategy & Audit
Before we build, we map the work.
Discovery is a paid engagement where we audit your workflow, data, integrations, and business value, then define what should be built and what it takes to launch. This is where vague AI ambition becomes an implementation plan.
If you sign the build agreement within 60 days of the final Discovery Report, 100% of the Discovery fee is credited once toward the Build Fee. Discovery can also show that an AI agent is not the right next step — a valid outcome before committing to development.
Typical Discovery outputs
- Discovery Report;
- AI Opportunity Map;
- Architecture Recommendation;
- Implementation Roadmap;
- estimated scope, timeline, and investment range.
You get
- clarity on whether the agent is worth building;
- a practical technical direction;
- enough detail to make a go/no-go decision.
03Fixed-scope AI agent build
Once the scope is clear, we build.
The build phase is governed by a written scope of work. It defines the workflow, integrations, data sources, user roles, deliverables, acceptance criteria, timeline, and fixed price.
Agent complexity is scoped in three levels. The right tier depends on how many systems the agent touches, how much workflow logic it needs, and how much production reliability the business requires.
Connected workflows
Standard
A multi-step agent that coordinates across several systems with workflow logic, error handling, and operational handoffs.
- Several integrations
- Workflow design and prompt engineering
- Error handling and retry logic
- Deployment and monitoring setup
- Documentation and handover
Full custom build
Complex
A custom AI system with deeper architecture, multiple integrations, business logic, data pipelines, monitoring, and production reliability requirements.
- Multiple integrations and custom code
- Custom architecture and data-pipeline design
- Error handling, retry logic, and logging
- Deployment and monitoring setup
- Technical documentation and SOP
04Launch, handoff, or AI Operations
After launch, ownership is explicit.
When the agent is built, tested, accepted, and deployed, we complete launch and decide how the system will be operated.
You can take full ownership through handoff, or keep Madency involved through an AI Operations Retainer.
Full handoff
Best when your team has the technical ownership, operational discipline, and capacity to maintain the agent internally.
- project code handed over after acceptance and full payment;
- architecture documentation;
- deployment and update runbook;
- configuration notes;
- prompt and retrieval overview;
- monitoring setup or monitoring instructions;
- known limitations and operational assumptions;
- knowledge transfer session;
- a 60-day warranty for covered defects.
AI Operations retainer
Best when you want Madency to stay close to the system and keep improving it month after month.
- quality review of real outputs;
- monitoring of failures, data sync, and API issues;
- prompt and retrieval improvements;
- incident response;
- usage analysis;
- monthly reporting;
- error and usage review;
- recommendations for new features, data sources, or agents.
FAQ
Questions teams usually ask before starting.
Still have questions? Book a free call and we will answer them directly.
Bring us the workflow. We will help you decide what is worth building.
If your team spends too much time searching, checking, routing, reporting, or moving data between systems, an AI agent may be the right next step.
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