What an Automation
Infrastructure
Engagement Looks Like
We build automations using Claude, custom integrations, and the tooling that fits your situation. Before we write any code, we spend time understanding your operations, your data, and how your team actually works. The technology is the easy part. Getting it right for your specific situation is where the work is.
We figure out whether automation is actually the right answer first.
Not every manual process should be automated. Some are manual because the volume is low and the cost of a mistake is high. Some are manual because the underlying data is inconsistent and automation would just propagate errors faster. We would rather tell you that upfront than build something that creates more problems than it solves.
When automation is the right answer, we need to understand your systems before we design anything. That means knowing what tools you use, how your data is structured, what the handoffs look like between people and systems, and where the real friction is. We map that before we write a line of code.
A workflow that runs automatically is only better than a manual one if it was doing the right thing to begin with. We spend the first phase making sure it was.
We use Claude for anything involving unstructured text or document processing, and custom integrations where a third-party tool does not expose the right interface. The stack depends on what your situation requires, not on what we prefer.
Four phases, in order.
Every engagement follows the same structure. The duration of each phase depends on what we are automating. What stays fixed is the sequence: we do not build before we understand, and we do not hand off before everything is documented.
We map your current workflows. That means talking to the people who actually do the work, walking through the tools they use, and tracing where data moves between systems. We are looking for three things: where the real bottleneck is, whether the data quality can support automation, and what failure modes we need to design for. The output is a written summary of what exists today and a clear recommendation for what to build and why.
We produce a written design document covering the data flow, the tools involved, the error handling approach, and what the automation does when it encounters something unexpected. You review it. We discuss it. We both agree on it before any code is written. This is also when we flag any data or systems issues that will affect the build, so there are no surprises mid-project.
We build in layers. You see working components early, not just at the end. Where possible, we run the automation alongside the manual process first, compare results against real data, and only cut over when we are confident it is working correctly. We do not hand you something and say "we think it works." We show you it working on your actual data before we call it done.
We document everything: how it works, what it connects to, what healthy operation looks like, and what to do when something fails. Your team gets a walkthrough before we close out. Then we stay available for 60 days after handoff for questions, issues, and adjustments. The goal is that your team can maintain and modify this independently. That is a requirement we build toward from the start, not an afterthought.
The kinds of automation we build.
Most automation work falls into a few categories. Engagements typically focus on one or two of these rather than trying to cover everything at once.
Ingesting files or feeds, extracting structured data from unstructured content, transforming and normalizing records, and loading them into the system that needs them. PDF extraction, form processing, API-to-database sync. We use Claude for anything that requires understanding the content of a document, not just parsing its structure.
Connecting the tools your team already uses so information moves between them without manual steps. CRMs, project management tools, communication platforms, ERPs. Built so the logic is visible, testable, and your team can adjust rules without needing a developer for every change.
Using Claude to extract structured data from documents, classify records, summarize large volumes of content, or generate first drafts of standard outputs. Built with clear input controls, output validation, and human review steps where the stakes require it. We do not wire an LLM directly to a production system without guardrails.
Replacing the manual process of pulling data, formatting it, and sending it. Scheduled jobs that generate and distribute reports on your cadence, dashboards that surface what your team needs without someone building a query, and alerts that fire when something crosses a threshold rather than when someone happens to check.
Request intake, conditional routing based on rules, notifications to the right people at the right time, and a record of what was approved, by whom, and when. From expense approvals to content review to vendor onboarding. Processes that currently live in email threads or shared spreadsheets, given a structure that is auditable and repeatable.
Automation that runs in response to something happening: a record is updated, a file arrives, a threshold is crossed, a user completes an action. The system responds without anyone having to notice and decide what to do. Scheduled jobs for recurring tasks. Webhook-based triggers for real-time responses. Built to handle failures gracefully, not just the happy path.
Everything we build belongs to you.
The code, the configurations, the infrastructure access, and the documentation. You are not dependent on us to keep it running. We build it so your team can maintain, adjust, and extend it without us.
- All workflow configurations, custom code, and integration files committed to your version control
- Architecture documentation covering data flow, system connections, and what depends on what
- Runbooks for your team: how to monitor it, what to do when it fails, and how to adjust rules or thresholds
- A test suite so you know if something breaks, and clear instructions for how to run it
- A walkthrough session with your team covering how it works, not just how to use it
- 60-day support window after handoff for questions, issues, and minor adjustments
- A written record of every significant decision we made and why, so the next person who touches it has context
- Guidance on what to watch for and what normal operation looks like, so your team knows when something is wrong
What makes engagements shorter or longer.
Timeline estimates during scoping are based on what we know at that point. These are the factors that most consistently move things in one direction or the other.
Conditions that keep timelines tight
- Your data is already in one place, consistently structured, and accessible via an API or direct connection
- The process we are automating is documented. Someone has written down the steps, the exceptions, and the edge cases.
- The tools involved have stable APIs. We are not working around undocumented behavior or rate limits with no documentation.
- Your team is available for a weekly check-in and can give feedback on working components within a day or two
Conditions that extend them
- Data lives across multiple systems with inconsistent naming, formats, or quality. Cleaning and normalizing it is part of the scope.
- The process exists mostly in people's heads. Mapping it takes time and often reveals more complexity than expected.
- Integrations require coordinating with third-party vendors, legacy systems without APIs, or partner companies on their own timelines
- Compliance or legal review is required before certain data can go into a model or leave your environment
See what this could look like for your situation.
The best next step is a 45-minute discovery call. We will ask about your current workflows, your tools, and what you are trying to solve. You will leave with a clear picture of whether automation makes sense and what the scope would involve.