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AI inbox organizer: maps every Gmail sender, classifies them with Claude, then builds the labels and filters so new mail arrives pre-sorted.
- Claude
- Gmail API
- Google sign-in
S-05 — Service · AI agents
Most AI projects stall between a clever demo and something the team relies on every day. I build agents and LLM integrations that plug into your actual systems (inbox, CRM, database, documents) with structured outputs, guardrails and logging, so you can trust them with real work.
Diagnosis
It works, but it's manual, inconsistent and invisible to the rest of the business.
The demo impressed, then stalled on data access, reliability and edge cases.
Answers change from run to run, and there's no way to check what the model did.
Token spend is a mystery until the invoice arrives.
Scope
S-05.1
Sort emails, tickets, leads or documents and send each one to the right place, the way Sortie does for Gmail.
S-05.2
Turn PDFs, emails and forms into clean, structured records in your CRM or database.
S-05.3
First drafts of replies, proposals and reports in your voice, with a person approving before anything goes out.
S-05.4
Agents that search, look up records and take actions through APIs, with clear limits on what they're allowed to do.
S-05.5
LLM features built into your SaaS with evaluation and cost controls. Axis, for example, runs on the Anthropic API.
S-05.6
Test sets, logging and cost tracking, so you know how well it works and what it costs.
Method
I treat an AI agent like any other part of the building: it needs a clear job, defined inputs and outputs, and a way to fail safely. We decide together where the model decides and where a person approves. Then I build it and measure it against real examples from your business.
I map how work actually moves today: every tool, handoff, spreadsheet and inbox. You get the map in writing before anything is built.
A scoped plan: what gets automated, what stays human, what it connects to and what it will cost to run. You sign off before I build.
Delivered in stages your team can test with real data, with error handling and logging from day one rather than bolted on later.
Documentation and a recorded walkthrough, so your team can run it and change it. Your accounts, your data, your system.
Evidence

AI inbox organizer: maps every Gmail sender, classifies them with Claude, then builds the labels and filters so new mail arrives pre-sorted.

Multi-tenant SaaS operations platform for VA service businesses — team, clients and communications on one control surface.
Questions
Whichever fits the task. I build most agents so the model can be swapped, and choose based on quality on your real examples, speed, cost and data policy.
Anthropic and OpenAI don't train on business API data by default. I'll walk you through their data policies and set up the integration so only the data the task needs is sent.
It can, within limits you set. I usually start with the agent proposing actions and a person approving them, then automate the steps that prove reliable.
By grounding it in your data, asking for structured outputs, validating them in code and testing against real examples before launch. When it isn't sure, it's designed to say so and escalate.
For internal workflows, usually less than people expect, but it depends on volume and model. I estimate token costs from your real data during scoping and add cost tracking so there are no surprises.
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