Agents that do one job reliably
Marketing, sales, research, and support agents scoped narrowly enough to actually trust in production.
The useful version of an AI agent handles one narrow, repeatable task reliably - not a general-purpose assistant that needs constant correction. Scope is the entire game here; a broad mandate produces an agent nobody trusts enough to actually rely on.
We build agents around specific functions: marketing agents that execute repeatable campaign tasks with human review built in, sales agents that qualify and schedule with clear rules for when to hand off to a person, research agents that compress hours of competitive digging into minutes, and support agents that resolve the tickets that repeat constantly while escalating anything genuinely novel. Custom agents get built when none of these off-the-shelf patterns fit the actual workflow.
Every agent ships with guardrails and a defined escalation path - the failure mode we design against is an agent confidently doing the wrong thing, not an agent that occasionally says "I'm not sure, let me get a person."
Every AI Agents service, broken down
Agents that execute marketing tasks, not just suggest them
AI marketing agents scoped to one repeatable task at a time - drafting, monitoring, or flagging - with a human checkpoint before anything ships.
First-touch qualification, handled automatically
AI sales agents for lead qualification, follow-up, and scheduling, handed off to a human rep at the right moment rather than run end to end.
Hours of research, compressed to minutes
AI research agents for competitive analysis, market research, and account research, run at a depth and scale manual digging can't match.
Resolve the repeatable tickets, escalate the rest
AI customer support agents that close out the tickets that repeat constantly and hand off anything genuinely novel cleanly.
Built around your specific workflow, not a generic template
Custom AI agent development for internal or customer-facing workflows that off-the-shelf agent platforms don't fit.
Common questions
A basic automation follows a fixed if-this-then-that path with no judgment involved. An agent uses a language model to decide what to do within a scoped set of options - which lets it handle variation a rigid workflow can't, without the open-ended unpredictability of a general chat assistant.
Narrow enough that you could write down every input it will see and every action it's allowed to take. If that list is short, the agent will be reliable. If you can't write it down without hedging, the scope is still too broad and needs to be split into smaller pieces.
It depends on the task. A research or support agent pulling from existing documents needs those documents organised and accessible, but not a full data platform. A sales or marketing agent that needs to write to a CRM needs that integration working first - we check this during scoping rather than assuming it's ready.
It escalates to a person instead of guessing. Every agent we build ships with a defined escalation path, and the situations that trigger it are decided upfront, not discovered after something goes wrong in production.
No - any system built on a language model will occasionally get something wrong, and a vendor promising otherwise is overstating it. What we can guarantee is that the agent is scoped narrowly enough to keep errors rare and contained, and that it escalates rather than acts confidently when it's uncertain.
A single-task agent with a clear scope - one integration, one review workflow - usually takes four to eight weeks including a monitored pilot period. Multi-step agents or ones requiring several system integrations take longer, and we scope that specifically rather than quoting a blanket timeline.
Considering an AI agent for a specific task?
We scope it narrow first - a reliable agent that does one thing beats a broad one that needs constant supervision.
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