AI where it actually saves you time.
AI voice agents, AI website chat, AI email agents, and custom AI tools trained on your data. Built with Claude and OpenAI. Every system has a scoped job, real data, and a human handoff. Live in weeks, measured every month.
AI care advisor, AI voice for inbound, AI product intake, AI lead classification, and a natural-language question layer on live business data. All running for real clients today.
AI works when it's trained on your business and pointed at a specific, repetitive task. Not a chatbot for its own sake. Not "let's throw ChatGPT at it." A real system with a scoped job, tied to your data, with a human hand-off when it should escalate.
We build AI voice agents, AI chat, AI email agents, and custom AI tools trained on your data. Every system is measured against the real hours or dollars it saves. If it doesn't save real hours or dollars, we don't build it.
Six things, in the order they happen.
- 01Find the workflow AI is actually good at. Not every workflow is an AI candidate. We look at what your team does today and identify where AI would meaningfully save time (or where it would just be a demo).
- 02Scope + design the system. What data does the AI need? What actions can it take? What does escalation look like? What are the guardrails? We write it down before we build anything.
- 03Build with Claude, OpenAI, or the right model. We're Claude Code and OpenAI developers. We pick the model that fits the job (reasoning quality, latency, cost) and build the integration around your systems.
- 04Train on your data. Your product catalog, your customer conversations, your knowledge base, your call transcripts. The AI is only useful if it knows your business, not the general internet.
- 05Wire in human handoff. Every AI system has an escalation path to a real human, with context passed through. The AI knows when to say 'let me get someone.'
- 06Ship, measure, iterate. Live in weeks, not months. We measure the hours saved (or the leads captured, or the calls handled) every month and tune the system against real usage.
Five very different AI systems. All in production today.
AI is not one thing. A voice agent for inbound calls is a different problem than a decision engine that generates quotes from a conversation. We've built both. And chat, and classification, and a natural-language question engine on live business data. The pattern that runs through all of them: scoped job, trained on real data, human handoff when it matters.
Public AI chat at seniorcompanion.care. Families describe their situation in plain English. The AI asks about care needs, location, budget, and preferences, then hands off to a real caregiver-matching specialist when the search gets complex.
Natural-language question layer on top of Google Ads, GA4, call tracking, CRM, and revenue data. Ask 'how did we do last week' and get a real answer with cited data. Live in production across every Allegro Brands client engagement.
AI classifies scraped leads by audience (residential vs. commercial vs. mixed), enriches from LinkedIn, and routes qualified contacts into email campaigns. Has processed 38,000+ contacts to date.
A client's team creates and edits WooCommerce products from a Mission Control interface, powered by a voice-driven intake that captures product details from a phone or SMS. Replaces the manual back-and-forth between the field team and the ops team.
A construction supplier's system generates quotes from customer conversations, using AI trained on their historical estimates and product library. Estimates that used to take days now take minutes.
Businesses whose team is drowning in repetitive work. Answering the same question dozens of times a day. Filling out the same forms. Qualifying leads by hand. Producing the same kind of quote from a different customer.
Not for businesses looking to "add AI" for its own sake. Not for teams that want a chatbot on the site just to have one. If you can point at a specific hour of your team's week that you'd like back, AI is probably the answer. If you can't, we'll tell you that honestly on the call.
Live in weeks. Getting better every month.
Scope is fixed-fee. Build is phased. Ongoing retainer covers tuning against real usage.
Scope + design
One or two workflows identified. Data requirements mapped. Escalation path drawn. Fixed-fee scoping.
Build + train
Model selected. Integration built with your systems. Trained on your data. Human-handoff path wired in.
Measure + tune
Monthly retainer covers monitoring, tuning against real usage, and small improvements as the workflow evolves.
Full technical breakdown of everything we build with is on the custom software stack page.
AI Automations FAQ.
How is this different from just using ChatGPT or an off-the-shelf chatbot?
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How much does an AI system cost to build?
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How long does it take to build?
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What happens when the AI can't answer or gets it wrong?
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Which AI model do you use?
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Will the AI have access to my company data?
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Free 30-minute AI consultation.
Tell us what your team does that shouldn't be manual. We'll point at the one or two places AI would actually help, tell you what it takes to build, and be honest if it isn't the right answer yet.