Custom AI agents that move work from discovery to action
We design agents for defined business tasks: finding relevant information, applying your rules, preparing an output and sending it to the right place. See our public examples, then talk to us about a workflow of your own.
What we mean by an AI agent
An agent is more than a chat window. It follows a defined objective across steps, uses approved data and tools, and produces an action or draft that can be checked. The useful design question is not “where can we add AI?” but “which repeatable decision or handoff should become faster and more consistent?” Explore our broader AI automation approach.
AI agents in action: live examples
Explore the public experiences behind our discovery and distribution work. External channels open in a new tab.
Web-based discovery agent
Smart Shopping Deals is our example of a web-based agent that finds Amazon deals for the day and presents them for shoppers.
WhatsApp Channel Agent
See an example of deal updates delivered through a WhatsApp channel as part of a channel-focused agent experience.
Telegram Distribution Agent
Explore a Telegram channel that illustrates distribution of shopping updates beyond a website.
These are live external destinations; their content and availability may change. They illustrate the kind of workflow we can build, not a claim that every client project uses the same tools.
How we build a useful agent
Every engagement starts with your goal and the boundaries the agent must respect.
Define the job
Agree on the task, source data, output, success criteria and decisions that still need human approval.
Connect the workflow
Integrate permitted websites, APIs and business tools; choose rules or model assistance only where each adds value.
Test and operate
Check real examples and failure cases, then add logs, rate limits, review points and monitoring before scaling up.
Agents we can scope for your business
Start with a narrow pilot and expand when the result is reliable.
Research and discovery
Find, filter and organize relevant public information or approved internal knowledge for a team to review. See our data collection service.
Content and channel operations
Prepare updates, route them for approval and publish or distribute through supported platforms where permissions allow.
Internal workflow assistants
Read documents, draft summaries, flag exceptions and hand off work inside your existing systems. See more AI use cases.
Built with boundaries
We scope data permissions, platform terms, privacy requirements and human oversight before automating collection or publication. An agent should expose what it did and when it needs help, not silently guess its way through an exception. For production operation, our DevOps, AIOps and MLOps capabilities support deployment, observability and change control. If the first use case is unclear, start with an AI workflow assessment; for more project context, see selected work.
Common questions
Can you build an agent for a workflow other than shopping deals?
Yes. The examples show capabilities, not a fixed product. We start by mapping your goal, data, systems and review requirements.
Will the agent run without human review?
That depends on the risk of the action. We recommend approval steps for customer-facing, regulated or otherwise consequential outputs.
Can an agent use WhatsApp, Telegram or other channels?
Potentially, where suitable APIs, account permissions and platform terms support the intended workflow. We check feasibility during scoping.
Have a repeatable task an agent could handle?
Tell us the trigger, data sources, decisions and desired output. We will help identify a practical pilot and the safeguards it needs.
Talk to us about an AI agent