Agentic AI

Agentic AI development for systems that already have work to do

The demo is never the hard part. The hard part is an agent that reaches your real data, knows which actions it may take alone, and still behaves the week after a model upgrade. 31 pages below, grouped by the job you are trying to get done.

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Get found by AI

When someone asks ChatGPT who does this, does your name come up?

Answer engines read structure, not adjectives. We rebuild pages so the claim, the evidence, and the entity are all machine-readable, then track whether the citations actually land.

Sell with AI

Who answers your buyers at 2am, and what are they allowed to promise?

Sales, support, and voice agents wired into the systems that hold the truth -- inventory, pricing, availability -- with escalation paths for everything they should not decide alone.

Run operations with AI

Which recurring hour of your team’s week is pure lookup and retyping?

Agentic workflows for the work that is repetitive but not simple: triage, document handling, routing, follow-up. Built per industry, because the exceptions are where these fail.

Connect AI to your stack

Your model is capable. Can it actually reach your data?

The plumbing layer: retrieval over your own database, tool definitions your systems can trust, and integrations into the CRM, ERP, and store where the records already live.

Common questions

What is the difference between an AI integration and an agentic workflow?

An integration answers when asked. An agent decides what to do next, calls tools in sequence, and keeps going until the task is finished or it hits a guardrail. Integrations are a solved problem; agents are where the engineering is.

How long does an agent build take?

A single-purpose agent against systems that already have clean APIs is typically four to six weeks. Most of that is not the model -- it is defining what the agent may do unsupervised, and building the evaluation set that proves it still behaves after a model upgrade.

Do you work with Claude, GPT, or both?

Both, and the choice is usually made per task rather than per project. We build on the provider SDKs directly so the model is a swappable dependency, not an architectural commitment.

What happens when the agent gets something wrong?

That is a design input, not an edge case. Every build defines the actions an agent may take alone, the actions that need a human approval step, and the logging that lets you reconstruct any decision after the fact.

Tell us what the agent needs to be right about

That question decides the architecture. Send it over and we will tell you what it takes to build.

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something together.

Whether it's a migration, a new build, or an SEO challenge — the Social Animal team would love to hear from you.

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