Deploy Agents That Work While Your Team Focuses on What Matters.

Purpose-built AI agents for research, content, lead qualification, support triage, and operational workflows - not chatbots. Autonomous systems with decision logic.

AI agents are not chatbots. They're autonomous systems that take inputs, make decisions, execute tasks, and hand off to humans only when they need to. The difference in output is the difference between a FAQ bot and a system that qualifies 200 leads overnight.

KICKFLIP has built 90+ internal tools, products, and agents inside this studio: research agents, content engines, QA systems, and brand compliance monitors. We've run them in production. We've seen them break. We know how to build them so they don't.

Why It Matters

Agents don't replace your team. They remove the ceiling on what your team can accomplish.

  • Run 24/7 without fatigue, context-switching errors, or staffing costs

  • Qualify and respond to leads within seconds of inquiry

  • Produce first drafts, summaries, research briefs, and reports at scale

  • Monitor data, flag anomalies, and trigger workflows without human polling

  • Scale from handling 10 tasks a day to 10,000 without headcount change

--NE

What You Get

  • Agent Scope Definition - Full specification of tasks, triggers, decision logic, and escalation rules

  • Agent Build - Configured and trained agent on custom LLM stack (Claude, GPT-4, or open-source)

  • Stack Integration - Native or webhook connections to your CRM, email, Slack, database, or API

  • Decision Logic Design - Documented flow showing autonomous versus escalated actions at every step

  • QA and Adversarial Testing - Real-world and edge-case testing before go-live

  • Monitoring Setup - Dashboard and alert configuration for post-launch agent behavior

  • Documentation and Handoff - Full written specification and recorded walkthrough

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Our Typical Process

01.

Define Agent Scope

We map the exact tasks you want the agent to handle: inputs, outputs, decision points, escalation rules, and handoff conditions. Scope drives everything.

01.

Define Agent Scope

We map the exact tasks you want the agent to handle: inputs, outputs, decision points, escalation rules, and handoff conditions. Scope drives everything.

01.

Define Agent Scope

We map the exact tasks you want the agent to handle: inputs, outputs, decision points, escalation rules, and handoff conditions. Scope drives everything.

02.

Design Decision Logic

We design the agent's decision flow: what it does autonomously, what it escalates, what triggers human review, and how it handles edge cases without breaking.

02.

Design Decision Logic

We design the agent's decision flow: what it does autonomously, what it escalates, what triggers human review, and how it handles edge cases without breaking.

02.

Design Decision Logic

We design the agent's decision flow: what it does autonomously, what it escalates, what triggers human review, and how it handles edge cases without breaking.

03.

Build and Configure

We build the agent on custom LLM integrations using Claude, GPT-4, or open-source models depending on your requirements, data sensitivity, and budget.

03.

Build and Configure

We build the agent on custom LLM integrations using Claude, GPT-4, or open-source models depending on your requirements, data sensitivity, and budget.

03.

Build and Configure

We build the agent on custom LLM integrations using Claude, GPT-4, or open-source models depending on your requirements, data sensitivity, and budget.

04.

Integrate With Your Stack

We connect the agent to your CRM, email, Slack, database, or any API your workflow depends on - using native integrations or webhooks where native isn't available.

04.

Integrate With Your Stack

We connect the agent to your CRM, email, Slack, database, or any API your workflow depends on - using native integrations or webhooks where native isn't available.

04.

Integrate With Your Stack

We connect the agent to your CRM, email, Slack, database, or any API your workflow depends on - using native integrations or webhooks where native isn't available.

05.

Test, Launch, Monitor

Agents run through structured QA before going live: real-world scenarios, adversarial inputs, and failure recovery testing. Post-launch monitoring catches drift and edge cases in production.

05.

Test, Launch, Monitor

Agents run through structured QA before going live: real-world scenarios, adversarial inputs, and failure recovery testing. Post-launch monitoring catches drift and edge cases in production.

05.

Test, Launch, Monitor

Agents run through structured QA before going live: real-world scenarios, adversarial inputs, and failure recovery testing. Post-launch monitoring catches drift and edge cases in production.

Related projects

Why KICKFLIP?

We didn't learn to build agents for clients. We built them for ourselves first. Research agents that surface competitive intelligence before a project kicks off. Content engines that maintain brand voice at scale. QA systems that catch inconsistencies before anything ships. That's 90+ internal builds across 13+ years of production work.

When we deploy agents for your business, we bring the knowledge of someone who has run them in production, not the knowledge of someone who configured a demo. The difference shows up in reliability, edge case handling, and how fast issues resolve.

Hear It From Our Partners

--SE

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Agents Are Ready. Is Your Business?

Purpose-built AI agents for research, content, lead qualification, support triage, and operational workflows - not chatbots. Autonomous systems with decision logic.

Agents Are Ready. Is Your Business?

Purpose-built AI agents for research, content, lead qualification, support triage, and operational workflows - not chatbots. Autonomous systems with decision logic.

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Purpose-built AI agents for research, content, lead qualification, support triage, and operational workflows - not chatbots. Autonomous systems with decision logic.

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