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How we build and deliver
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Custom AI agent development for one real workflow
Tell us what your team does manually. We map the workflow, tools, permissions and approval steps, then build and test a practical agent.
Where an AI agent is worth building
We define the inputs, tool access, approval steps and success criteria before development.
Choose a workflow that depends on language, context and actions across your systems.
What can it do for your business?
Less repetitive work
Let agents collect information, prepare drafts and move data between tools. Keep your team focused on decisions that need their expertise.
Connected workflows
Connect your documents, CRM and APIs in a defined process, with permissions that match each task.
Results you can evaluate
Test on representative examples before rollout. Track quality and exceptions, with human approval for sensitive actions.
How we structure your AI agent
We separate the model, relevant context, tool access and workflow controls so each part can be tested and monitored.
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Reasoning engine
Select a model for the task and evaluate its outputs on representative examples. -
Memory management
Retrieve relevant information from approved documents and previous interactions, with access controls. -
Tool augmentation
Connect the agent to approved APIs and databases with limited permissions. -
Orchestration layer
Coordinate workflow steps, handle failures and add approval checkpoints for important actions.
Most companies have one common problem
Tasks that require too much judgment for old software, but are too repetitive for expensive talent. Our workflows solve this.
Collect information from agreed sources and prepare research summaries with source references for your team to review.
Monitor operational data, flag exceptions and prepare proposed updates, with approval rules for changes that affect the business.
Search technical documentation and run approved diagnostic checks to help your support team investigate issues.
How we deploy
Discovery and process mapping
Map the current process, its inputs, exceptions and expected output.
Agent persona and tool definition
Define the data sources, tools, permissions and approval rules available to the agent.
Prototype and evaluation (RAGAS)
Build a prototype and evaluate task completion, output quality and failure cases.
Deployment and monitoring
Deploy the workflow with logging, monitoring and human review for important decisions.
Frequently asked questions
Chatbots are designed for conversation; AI agents are designed for action. While a chatbot might explain how to book a flight, an agent will search for the flight, compare prices against your budget, and execute the booking via API.
We agree on hosting, access permissions, retention and model-provider data handling before development. Private deployments and masking can be included where the project requires them.
Absolutely. Our agents are built to be "tool-aware." They can be granted secure access to Salesforce, HubSpot, SAP, or custom internal databases to read and write data as needed.
The timeline depends on the workflow, data and integrations. We start with a defined first version and agree on milestones after discovery.
No AI system is error-free. We test representative tasks, validate outputs and limit tool permissions. Important actions can require human approval, with logs and exception handling for review.
Tell us what your team does manually
Share the workflow, tools and desired result. We’ll tell you whether an AI agent is a practical fit and what the first build should include.