# Build it. Run it. Make it intelligent.

> One team builds your AI and software, runs the cloud underneath, and operates it long term. No handoff gap between the builder and the operator.

Source: https://tachyonshift.com/build-and-run

The firm that builds your system and the firm that runs it are usually two firms. The handoff between them is where projects fail: documentation goes missing, the operator inherits code it did not write, and no one owns the outcome. TachyonShift is one team on both sides of that handoff, with AI engineering depth on top.

## What you get from one team that you used to buy from three

| What you need | Build-only consultancy | Cloud managed-service provider | AI lab services arm | TachyonShift |
| --- | --- | --- | --- | --- |
| Custom software and AI built for your process | Yes | No | Yes, on its own models | **Yes** |
| Operated 24/7 after launch | Hands off at delivery | Infrastructure only | Varies by contract | **Yes** |
| Cloud platform designed, secured, and run | Separate vendor | Yes | No | **Yes** |
| Any model provider | Varies | Not applicable | One model family | **Yes** |
| Human verification and audit trail on AI output | Varies | Not applicable | Varies | **Built into every system** |
| One team accountable for the result | No | No | No | **Yes** |

## We build

AI agents, workflow automation, and web and data applications, integrations, and the cloud environment they run in. Engineers work in your systems, demo every week, and ship on your real data. See [AI forward deployed engineering](/ai-forward-deployed-engineering).

## We run

**Cloud operations.** 24/7 monitoring, patching, incident response, and security hygiene on AWS, Azure, and GCP.

**Cost control.** We find the idle, oversized, and orphaned resources by ID, rank the savings by effort and impact, and make the changes.

**Migration.** We move production workloads to the cloud with one scheduled overnight cutover instead of weeks of outages. We have built the plan for two-site VMware environments moving to Azure.

**End-of-support risk.** We inventory what still runs on unsupported software, such as SQL Server 2016, and move it before an auditor or an attacker finds it first.

**AI operations.** We monitor accuracy, cost, and drift, run evaluation tests against every change, route work to the cheapest model that meets the quality bar, and swap models without rebuilding.

## How operation works

1. **Operating agreement.** Scope, response targets, and reporting are written down before launch.
2. **Runbooks from day one.** The system ships with the documentation to operate it, so you can take it over at any point.
3. **Monthly results review.** We report against the metrics agreed at the start, with denominators and dates.
4. **A roadmap, not a ticket queue.** Each quarter we propose what to improve next and why it pays back.

## The same pattern in marketing engineering

Our five-week marketing foundation builds the tracking, ad accounts, and key pages, then runs them monthly. Every ad, post, and link reports back to the quote, call, or ticket click it produced, and a weekly one-page report shows what changed. Build, then run, then improve.

## Start here

**[Request an AI Assessment](/assessment).** Written opportunity report within days. Or [book a discovery call](/discovery) to talk through a build, a migration, or an operating agreement.