When several systems are involved
A use case does not hang on a single tool. Data sits in CRM, ERP, email, documents or other applications and needs to be brought together sensibly.
AI agents
AI agents that fit your processes
Not every AI use case can be solved with a standard tool. When several systems, your own data sources, individual rules or recurring workflows come together, AI agents can be the right path. Aviando builds agents and automations that fit into your existing way of working.
AI Agent
Perception
Semantic understanding
Decision-making
Action
Learning
Governance
Multi-agent
Overview
Why AI agents are becoming relevant
AI becomes valuable in the company when it does not just answer but can help within a process. For that it needs context, access to relevant information and clear rules about what it is allowed to do.
Many AI tools only help with individual tasks - they write texts, summarise content or answer questions, but they do not automatically solve a whole workflow. In mid-sized companies in particular, the relevant information is also spread across several systems, for example Salesforce, ERP, Google Workspace, Slack, email, documents and databases.
AI agents start exactly there: they connect models, data sources, APIs and automations into a flow that supports a concrete task. That is not a gimmick but practical relief for recurring work.
Overview
When an AI agent makes sense
When standard features do not fit
An existing AI tool covers the core process only partly. A specific logic, an individual workflow or an integration into existing systems is missing.
When recurring tasks tie up a lot of time
Teams search for information, transfer data, create summaries or prepare decisions manually on a regular basis.
When control remains important
An agent should not simply work freely. It needs clear limits, approvals, logging and human review at important points.
When cost and model choice are relevant
Not every task needs the same model. Agents can be designed so that suitable models, cost logic and data flows are deliberately chosen.
In detail
Our approach
01
The process first, then the agent
We do not start with the question of which agent we can build. We first clarify which task should be improved, who is involved and where effort arises today.
02
Understand the system landscape
An agent is only as good as its context. That is why we review which systems, data sources, interfaces and permissions are relevant.
03
Scope agents clearly
Good agents have a defined mandate. They know which information they may use, which tools are available and when a human has to decide.
04
Combine automation sensibly
Not everything needs generative AI. Often the best solution comes from classic automation, API integration, retrieval, rules and AI support.
05
Pilot and improve
We build a first usable agent, test it with real tasks and improve it based on feedback, quality and impact.
Overview
How AI agents can work
Use cases
Typical use cases
CRM assistance
An agent summarises account information, checks open activities, spots missing data and prepares next steps for sales or service.
Proposal preparation
Information from CRM, documents and past projects is brought together so teams get to a structured proposal draft faster.
Knowledge agent
Internal documents, policies, project knowledge or FAQs become searchable and usable, without employees having to search for long.
Service support
Tickets, customer history, knowledge articles and internal rules are brought together so support teams can react faster.
Meeting and follow-up agent
Call notes, transcripts or CRM data are turned into tasks, summaries and follow-ups.
Data review and data maintenance
An agent spots missing information, duplicates, incomplete fields or unusual patterns and prepares corrections.
Marketing operations
Briefings, content variants, campaign information and performance data are prepared in a structured way so teams can move on faster.
Internal workflow automation
Recurring tasks across tools are connected. For example check a request, retrieve data, create a summary, obtain approval and trigger the next action.
Ready-made agents
Agents we have already built
Some of these use cases already exist as a ready-made agent, each with its own product page covering how it works, its approval logic and pricing.
Lead Signals AgentContinuously scans more than 1,200 sources and matches every find against your ICP.Sales Signals AgentMonitors flagged accounts and only files qualified events on the account.Visit Capture AgentTurns a voice note after the meeting into a complete visit report in Salesforce.Appointment PlannerAnalyses the account history before the meeting and files the meeting preparation on the appointment.Data Enrichment AgentCompletes leads, contacts and accounts from public sources without overwriting existing values.Order Processing AgentReads incoming order documents and creates the order header and line items; uncertain runs go to the review queue.Marketing Assets AgentTurns a short briefing into a landing page, email and form in the look of your brand.TicketforceBrings an AI development team into your org: from requirement to finished feature.
In detail
Possible technical building blocks
AI models
Depending on the task, different models can make sense. Not every process needs the most powerful model. Often what counts is the right balance of quality, speed, cost and data protection.
Retrieval and knowledge sources
Documents, web pages, databases or knowledge articles can be connected so an agent does not just use general knowledge but receives relevant company context.
APIs and integrations
Custom agents create impact when they can talk to existing systems. These include Salesforce, ERP, ticketing systems, collaboration tools, databases or your own applications.
Workflow orchestration
Tools such as n8n can help make steps visible, controllable and integratable. That way AI functions can be connected with existing automations.
Human in the loop
Not every action should be carried out automatically. Critical steps can be handed to people for review, approval or adjustment.
Logging and monitoring
Agents should stay traceable. Logs, error analysis and usage data help safeguard quality and identify room for improvement.
Impact
What this improves
AI agents can take the load off where standard tools stay too generic.
- Teams spend less time on search, transfer, summarising and preparation. Information becomes usable faster. Recurring work runs in a more structured way. Processes across systems become more traceable. Departments receive solutions that sit closer to their actual work.
- The result is not yet another isolated tool. It is an agent embedded in existing workflows.
Effort and relief over time
Break-even StartMonth 1Month 2Month 3
Relief, cumulative Effort, cumulative
Services
Our services
Use case review
We analyse whether an agent makes sense for your process or whether Agentforce, a platform, an existing tool or classic automation fits better.
Process and system analysis
We review which systems, data sources, roles, interfaces and work steps are relevant. From that emerges a clear target picture for the agent.
Agent design
We define task, limits, tools, data access, user interaction, approvals and success criteria.
Technical implementation
Aviando builds the agent and connects it with suitable models, data sources, APIs and workflows.
Pilot and testing
A limited pilot shows whether the agent helps in everyday work. Feedback, error patterns and usage are used to improve the solution.
Enablement and handover
Teams learn how the agent is used, reviewed and developed further. Admins or technical owners receive the necessary documentation.
Optimisation and operations
After launch we support quality, monitoring, adjustments, new data sources and extensions.
Related topics
More related content
AI strategy
When the right use case is not yet clear, a structured prioritisation helps before development.
Learn moreAI adoption & enablement
AI agents only create impact when teams know how to work with them and review results.
Learn moreSalesforce Agentforce & AI
When the use case sits directly in Salesforce, Agentforce can be the more suitable entry point.
Learn moreSalesforce integration
Agents often need access to data and systems. Integration creates the basis for that.
Learn moreAI for sales
Many agent use cases arise in sales, for example with preparation, follow-ups or CRM data maintenance.
Learn moreAI for service
Service processes benefit from knowledge access, ticket context and controlled reply preparation.
Learn more Intro call
Do you have an AI use case that cannot really be solved with standard tools?
Chris Uhrig, CTO & Co-Founder of Aviando, looks at your process, your systems and possible AI agent approaches with you. Together you clarify whether an individual agent makes sense, which data is needed and what a first pilot can look like.
Chris Uhrig
CTO & Co-Founder