Question 1

What's the difference between an AI agent and a chatbot?

Accepted Answer

A chatbot answers questions. An AI agent takes action, it can look up an order, update a record, trigger a workflow, or complete a multi-step task across your systems, then escalate what it shouldn't handle. We build both; the difference is whether you need answers or outcomes.

Question 2

How long does it take to build an AI agent?

Accepted Answer

Most agents go live in 2–6 weeks. A focused single-workflow agent with one or two integrations is 2–3 weeks; an orchestrated multi-agent system spanning several systems is 4–6 weeks. We ship a working version early so you give feedback before launch.

Question 3

Why do so many AI agent pilots never reach production?

Accepted Answer

Usually because the agent was never connected to the systems where work happens, was never evaluated against real cases, or had no guardrails or handoff path. We build for production from day one, integrations, evaluation, allowlists, and escalation, so the agent ships instead of stalling.

Question 4

Can the agent actually take actions, or just answer?

Accepted Answer

Take actions, within strict guardrails. We build scoped write access so the agent can update a record, process a refund, book a slot, or move data between systems, with an allowlist defining what it can do freely, what needs confirmation, and what always goes to a human.

Question 5

Will it integrate with the tools we already use?

Accepted Answer

Yes, CRMs (HubSpot, Salesforce), helpdesks (Zendesk, Intercom), billing, scheduling, Slack, and custom internal APIs. Integration depth is exactly what separates an agent that completes work from one that only describes it.

Question 6

How do you stop the agent from doing something wrong?

Accepted Answer

Three layers: grounded retrieval so answers come from your real data, an action allowlist so the agent can only do what it's authorized to, and a pre-launch evaluation suite that scores action-correctness on real cases. Anything outside the agent's authority escalates to a human with full context.

Question 7

Which AI model do you build with?

Accepted Answer

Whichever fits your accuracy, latency, and cost requirements, GPT, Claude, or Gemini. We have no vendor allegiance; the engineering and evaluation around the model matter more than the model name.

Question 8

What about our data security?

Accepted Answer

Encryption in transit and at rest, scoped access controls, and architecture that keeps your data out of model training. For regulated industries we design to your compliance requirements from day one.

Question 9

Do we need a finished spec to start?

Accepted Answer

No. Most engagements start from a workflow that's painful and a rough idea. Discovery turns that into a scoped roadmap, a realistic estimate, and an honest pass/no-pass on whether an agent is the right fix.

AI agents that take action and reach production, not another pilot that stalls.

Agents that do the work, not just talk about it.

Customer support agents

Sales and lead-qualification agents

Operations and back-office agents

Internal copilots and multi-agent workflows

Production-first, with guardrails from day one.

Discover

Build

Deploy and grow

Built for teams with real volume.

Connected to your real systems.

Proof from production.

Related field notes and proof.

Why AI agent pilots stall

AI chatbot development

Phobia: 14k conversations a week

Things teams ask us first.

Explore connected service pages.

Ready to ship an agent that actually works?