Leveled AI

Service

Pipelines That Fail Loudly Instead of Silently

Most of what makes a company feel automated is invisible. Data moving between tools on time, in the right shape, with somebody told when it does not. This is the least glamorous work we do and it usually decides whether anything else holds together.

Getting systems to talk that were never meant to

Your CRM, your billing platform, the industry software you are stuck with, the spreadsheet the operations lead guards, and the tool one department bought without telling anyone. We build the connections between them, through APIs where they exist and through exports or scraping where they do not. The test is simple. A record created in one place shows up correctly everywhere else it belongs, and no person had to copy it.

Pipelines that tell you when they break

Every data pipeline breaks eventually. A vendor renames a field, an export changes format, a credential expires at 2am on a Sunday. The difference between a small problem and a quarter of bad numbers is whether the pipeline notices and says something. We build in retries, validation on the way in, and alerts that land in a channel a human actually reads. Silent failure is the expensive kind.

Agents with a job description

An AI agent is useful when it has a narrow job, real tools, and limits on what it can do without asking. We build them that way: one defined task, access to exactly the systems it needs, logging on every action it takes, and a human approval step wherever a mistake would cost real money. The agents that go wrong in public are almost always the ones handed broad permissions and a vague instruction.

A living map of your market

The same pipelines that move your internal data can watch the outside. Competitor pricing pages, product reviews, job postings, public filings, the forums where your customers complain. We pull it on a schedule, normalize it into one shape, and put it somewhere you can query, so a question like which competitors changed pricing this quarter takes a minute instead of an afternoon of open tabs.

What you actually get

  • Working integrations between the tools you already pay for
  • Validation and alerting, so a failure surfaces the same day
  • AI agents scoped to one job, with logging and approval steps
  • Scheduled competitive data collection you can actually query
  • Everything running in your accounts, under your credentials
  • Runbooks for the two or three things that will need a human

Common Questions

Before you book a call

Is scraping legal?
It depends on the source, the data, and the terms you agreed to. We stick to publicly accessible information, respect rate limits and robots directives, and stay out of anything behind a login you do not control. Where a proper API exists we use it instead. If a source looks like a problem, we tell you rather than quietly building it.
Where does this run?
In your accounts. Your cloud provider, your database, your API keys. We build and deploy it there, and you can revoke our access the day we finish without anything stopping.
What happens when something breaks after handover?
You have the alerting and the runbooks, so your team sees the failure and knows the first few things to check. If you want us available for ongoing support we can arrange that separately. The system is built so you are not forced into it.
Can you work alongside our existing engineers?
Yes, and it usually goes better that way. We follow your repository conventions, open pull requests like any other contributor, and hand off with documentation written for the people who will maintain it.

Let's find out if this fits

A 30-minute call, no charge. If we can help, we will show you how. If we cannot, you still leave knowing what to do next.