AI that reads the record and does the work.
This is the build-AI pillar at NavoTech, the counterpart to our SEO and AI Search work on the found-by-AI side. It runs from a plain-spoken readiness check through to assistants grounded in your own content, automated reading-and-acting work, agents that carry a multi-step task across your tools, and AI features wired into the app you already run. The modern scribe reads your record and acts within clear limits, while your people stay in the loop on the decisions that carry weight. The model is a swappable part, not a cage. You own the prompts, the code, and the data. Each kind of build has its own page below.
> Start with a pilot on one real task,
> then scale what actually worked.
Practical AI that does real work, not a demo that impresses once.
There is a lot of noise around AI right now, and most of it is about possibility rather than the actual job. We are interested in the opposite: AI put to work on tasks your team does every day, measured against whether it makes those tasks faster, cheaper, or more consistent. That work takes a few shapes, from a readiness check that says where AI actually pays off, to assistants, automation, agents, and features built into your app, and each has its own page below. If a packaged tool already does the job for you, we will say so. We would rather scope the right thing than sell the most ambitious one.
Think of these systems as a modern scribe. A scribe reads the record, understands what is being asked, drafts a response, and flags anything it is unsure of for a person to decide. That is what good AI does here. It reads your content and your data, it acts within clear limits, it shows its sources, and it hands off to a human the moment a decision carries real weight. Grounding it in your own content makes it far more reliable, though no amount of grounding makes it infallible, so the handoff is built in rather than hoped for.
The principle underneath all of it is ownership. The prompts, the application code, and the data are yours, in your accounts and your repositories, documented for your team. The model is a swappable part behind a clean interface, changeable without rebuilding what sits around it. You should never be locked into us or into a single vendor to keep using what we built.
What we hold to
Every engagement runs on the same handful of commitments, whatever the project.
- You own the prompts, the code, and the data
- No vendor lock-in; the model is a swappable part
- Humans stay in the loop on decisions that matter
- The system cites its sources and hands off cleanly
- Honest scoping, and a candid "not yet" when that is true
Pilot on one real task, then scale what worked.
We start small on purpose. A focused pilot on a task you actually care about proves whether AI helps before anyone commits to a program. What earns its keep gets scaled. What does not gets dropped without ceremony.
// the scribe drafts, the human decides, the record stands
- Honest scopingWe look at the work you want help with and decide, with you, whether AI is the right tool at all. Sometimes the answer is a packaged product, a process change, or "not yet." When custom AI is the right call, we name the single task to start with and what proving it would look like.
- A small pilotWe build one thing on one real task: a single assistant, one automated workflow, or one feature. It runs against your actual content and your actual cases, not a tidy demo, so the result tells you something true about how it will behave in production.
- Evaluation against a real barWe define what good looks like before we build, then test against it: accuracy, citation quality, how often it correctly hands off, and how much volume runs cleanly without a person. You get to watch quality rather than take it on faith.
- Guardrails and handoffWe write down what the system is allowed to do, what it must refuse, what gets logged, and exactly when it passes control to a human. The decisions that carry weight stay with your people. The system does the reading and drafting around them.
- Scale and hand overWhat works gets widened to more cases and more of the team, with documentation in your repositories and accounts. The prompts, code, and data are yours. If you want to keep building in-house from here, you can.
Five ways to put AI to work, each its own page.
The pillar breaks into five kinds of build. Most engagements start with one of them on a single real task. Open any page for the detail, the honest limits, and what an engagement looks like.
Not sure which you need? Start with AI Strategy & Readiness for a straight read on where AI actually pays off, or tell us the problem and we will point you to the right one.
// one pillar, five tablets, the same commitments under each
Frequently asked questions
Related work and reading
The other pillars sit alongside this one, and two of our Insights pieces go deeper on the thinking behind grounded assistants and when automation actually pays off.
Have a repetitive task you suspect AI could handle?
Tell us the one task that eats your team's time. We will give you a straight read on whether AI fits, what a small pilot would look like, and whether the honest answer is "not yet."