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AI automation services

AI agents that do the work, with a person on the approve button

We build autonomous AI agents for business processes: order handling, lead routing, content pipelines and monitoring, each with real tools, approval gates and an audit log.

GPT-4oGeminin8nTool useApproval gatesFixed-fee proposals
What's included
  • Process and tool mapping
  • Reasoning layer
  • Tool integrations
  • Human-in-the-loop approvals
  • Logging and failure handling
How it starts

A free 30-minute strategy call, then a fixed-fee proposal. No retainer required to get a quote.

The problem

When answering questions isn't enough

The expensive work is multi-step: check the order, look up the policy, decide, update three systems, tell the customer. That is where a chatbot stops and an agent starts.

A refund request touches Shopify, the payment provider, the inventory sheet and the customer, in order
Inbound leads sit for hours because qualifying them means reading, researching and deciding who gets them
Content ideas pile up because turning a source into a draft, a brief and a schedule is five jobs
Someone checks dashboards each morning for problems a script could have flagged at 3 a.m.
Fully autonomous tools feel risky because nobody can see what they did or stop them

Our approach

Agents with tools, boundaries and a paper trail

An AI agent differs from an assistant in one way that matters: it acts. Given a goal and a set of tools, it plans the steps, calls the tools, reads the results and continues until the job is done or a person is needed. We use GPT-4o or Gemini as the reasoning layer, with a defined toolset underneath: Shopify Admin API calls, Gmail sends, Notion updates, Sheets writes, HTTP requests to your systems, and n8n workflows for the plumbing.

The design work is deciding where the boundaries sit. Which actions can the agent take alone (tag an order, draft a reply, log an alert) and which need approval (issue a refund, email a customer, publish a post)? Approval gates put that decision in front of a person in Slack, email or a dashboard, with the agent's reasoning attached. Every step is logged, so any run can be replayed to see what was read, decided and executed.

Our AI Content Engine is this pattern in production: it monitors AI-industry news, generates LinkedIn and social drafts, and queues them for review before anything publishes. THS Automation's Autopilot agent runs scheduled tasks the same way, from one dashboard. Both are Next.js apps we built and operate, and the same architecture, adapted to your tools and approval rules, is what we deliver.

What's included

  • Process and tool mapping — The workflow documented step by step, then each tool the agent may call defined with its inputs and outputs.
  • Reasoning layer — GPT-4o or Gemini with a structured prompt, task decomposition and clear stop conditions, so runs don't wander.
  • Tool integrations — Typed functions for Shopify, Gmail, Notion, Sheets and your own APIs, with input validation before anything executes.
  • Human-in-the-loop approvals — Gates that pause the agent and request sign-off for high-impact actions, with the context attached.
  • Logging and failure handling — Every plan, call and result stored per run; retries, timeouts and a clear alert when the agent gives up instead of looping.
  • Cost controls and triggers — Per-run token budgets, cheaper models for simple steps, spend alerts, and runs started by webhooks, schedules or Shopify events.

Process

How we work

Discovery

A free 30-minute call to pick one process that is frequent, well understood and painful enough to justify an agent.

Boundary spec

We write out the tools, the autonomous actions, the approval points and the failure paths; you sign off before code.

Shadow runs

The agent runs on real inputs without executing, proposing actions we compare against what your team actually did.

Supervised launch

Approvals start tight and loosen as the log shows reliable decisions; monthly reviews cover runs, costs and failures.

Benefits

What an agent changes

Whole tasks completed

The agent carries a job across systems instead of leaving the next step in someone's queue.

Judgment on messy inputs

Where a rule-based flow breaks, an agent reads the email, checks the record and picks a path.

Control you can dial

Approval gates set how autonomous each action is, and change without a rebuild.

Nothing happens silently

Logs and replays make every action explainable to a manager, an auditor or a customer.

Fit

Who this is for

  • Ecommerce operators handling refunds, exchanges and supplier updates by hand
  • B2B teams with inbound leads that need research and routing before a rep sees them
  • Founders publishing content who want drafts queued for review, not auto-posted
  • Businesses that tried Zapier-style automations and hit the limits of fixed rules

Technologies & platforms

OpenAI GPT-4oGoogle GeminiGroqn8nNodeNext.jsTypeScriptShopify APIsNotion APIGmail API

Common use cases

Order exception agent

Reads a problem order, checks stock and policy, drafts the customer email and proposes the fix for one-click approval.

Lead qualification and routing

Enriches a new lead, scores it against your criteria, and assigns it to the right person with a summary.

Content pipeline

Monitors sources, drafts posts and briefs, and fills a review queue, the way our AI Content Engine does.

Monitoring and alerting

Watches inventory, ad spend or uptime, investigates anomalies and posts a diagnosis rather than a raw alert.

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FAQ

Frequently asked questions

What can an agent do that a workflow automation can't?
A workflow automation follows a fixed path: if this, then that. An agent is given a goal and a toolset and works out the path at run time, which matters when inputs vary. A refund email might describe a wrong size, a damaged item or a missing parcel, each needing different checks. The agent reads the message, decides which tools to call, and adapts to what it finds. We still use plain automation wherever the path is fixed.
How do you keep an agent from doing something harmful?
By limiting what it can do and checking before it does it. Tools are explicit functions with validated inputs, so the agent cannot invent an action outside its toolset. High-impact actions sit behind approval gates: the agent proposes, a person confirms. Shadow mode during the build compares its decisions with your team's before it executes anything. Budgets cap runaway loops, and every run is logged so problems are visible and, where possible, reversible.
Where does our data go when an agent runs?
The agent runs on infrastructure you own, typically a Node service or n8n instance in your cloud account. Tool calls go directly to your systems through their APIs. The model provider, OpenAI or Google, receives the prompts and the data needed to reason on each step; under their API terms that data isn't used for training. Logs stay in your database, and personal data can be redacted before it reaches the model.
What drives the cost of building and running an agent?
Build cost scales with the number of tools, the complexity of the decision logic and how much shadow testing the process needs before you'd trust it. Running cost is mainly model usage, which depends on how many steps each run takes and how much context it reads; we route simple steps to cheaper, faster models and reserve GPT-4o for reasoning. After a free strategy call you receive a fixed-fee proposal with a running-cost estimate attached.
How long does it take to get an agent into production?
A single-process agent with three to five tools and approval gates usually takes two to three two-week sprints, including shadow runs on real data. The shadow phase is where most of the time goes, and it's the part we won't skip, because it turns 'it worked in the demo' into 'it works on Tuesday's strange order'. A second process on the same toolset goes faster.
Who owns it, and what maintenance does it need?
You own the code, prompts, tool definitions and logs, deployed in your accounts, with documentation and Loom walkthroughs. Agents need more ongoing attention than static automations: APIs change, model versions update, and new edge cases appear in the logs. A monthly review, either by your team using our runbook or through a retainer with us, keeps the failure rate low and lets you widen autonomy as trust builds.

Next step

Pick one process. We'll show you where an agent fits.

Book a free strategy call and we'll map the steps, the tools and the approval points, then send a fixed-fee proposal.

  • Free 30-minute strategy call — no sales script
  • Fixed-fee proposal within a few days of the call
  • You own everything we build: code, store, prompts, data
  • Remote-first, working across US, UK, EU and AU time zones

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