Lead research and qualification agent
Scope, integrations, controls and acceptance criteria are defined for the actual workflow before scaling.
Service
AI Agents & Automation
PlanckCyber designs and builds AI agents and intelligent automation for organizations that want software to perform defined work across business processes. These systems can interpret information, use tools, update business applications, coordinate multi-step tasks and route exceptions to people. The emphasis is not on “autonomy” as a slogan; it is on a bounded workflow with clear permissions, observability, validation and measurable results. Projects can range from a single agent handling a repetitive process to coordinated multi-agent systems that connect CRM, support, document, research or back-office workflows. PlanckCyber can start with a known automation target, prove a risky step before committing to a larger build, or help identify the right workflow when the opportunity is not yet clear.
Problems We Solve
When this service fits.
- Repetitive manual work consumes skilled time
- Work moves slowly across multiple systems and handoffs
- Teams copy information between email, CRM, documents and internal tools
- Routine research or follow-up is inconsistent
- Existing automation breaks when inputs require interpretation
What We Can Deliver
Built around the requirement.
- AI agent architecture and implementation
- Tool/API integrations and orchestration
- Human-in-the-loop review paths
- Testing and evaluation harnesses
- Auditability and operational controls
- Deployment, monitoring and improvement plan
Use Cases
Concrete examples.
CRM follow-up and administration automation
Scope, integrations, controls and acceptance criteria are defined for the actual workflow before scaling.
Customer-support resolution workflow
Scope, integrations, controls and acceptance criteria are defined for the actual workflow before scaling.
Document intake and routing agent
Scope, integrations, controls and acceptance criteria are defined for the actual workflow before scaling.
Multi-agent research and operations workflow
Scope, integrations, controls and acceptance criteria are defined for the actual workflow before scaling.
How an Engagement Can Start
Use the smallest responsible starting point.
Direct build
When the workflow and desired outcome are already understood.
Focused prototype
When model behavior, data access or integration needs to be proven first.
Workflow discovery
When several automation opportunities compete for attention.
Process
From definition to evidence.
- Define workflow and boundaries
- Map tools, data and permissions
- Prototype the uncertain parts
- Build and integrate
- Evaluate, release and monitor
Data, Integration & Security
Constraints are design inputs.
- Least-privilege tool access
- Reversible actions and approvals
- Model evaluation and failure modes
- API reliability and rate limits
- Sensitive-data handling and retention
Related Solutions
See the service applied to a business problem.
AI Sales Automation
Sales teams lose selling time to research, CRM administration, qualification and follow-up.
Explore solutionAI Customer Service
Support teams face repetitive questions, fragmented knowledge and inconsistent routing while customers expect fast answers.
Explore solutionAI Workflow Automation
Business processes slow down when people repeatedly move information, interpret inputs and coordinate work across disconnected systems.
Explore solutionFAQ
Questions buyers ask.
What is an AI agent?
An AI agent is software that can interpret context, decide among allowed actions and use approved tools to complete parts of a task. Production agents still need defined boundaries, permissions and evaluation.
When should we use an agent instead of traditional automation?
Traditional rules are often better for deterministic steps. Agents become useful when a workflow requires interpreting language, documents or changing context. Many reliable systems combine both.
Can agents work with our existing CRM or business software?
Often, yes, when those systems expose suitable APIs or controlled browser/workflow interfaces. Integration feasibility is checked before the build is scaled.
Start with the problem
Have a problem AI might solve?
You do not need a specification. Tell us what you are trying to improve.