Custom AI built on your data.

AI to automate the tedious manual work

The work your people should not be doing.

Let an AI agent do the reading, checking and drafting, and leave a person in charge of anything consequential.

• The quote request that eats an afternoon

  An AI drafted reply waiting in your inbox, with every number pulled from your own records.

  A request comes in. Someone reads it, checks stock, checks a delivery date, and writes back. Whereas an AI agent could instead do the reading and the checking, then leave a draft for a person to tweak and send. Anything that has to be exact, like the price or the availability, comes straight from your own system rather than from the AI, so every number can be confirmed.

• The paperwork nobody wants to retype

  Fields filled straight from PDFs and photos, with anything uncertain flagged instead of guessed.

  Invoices, purchase orders, packing slips and intake forms arrive as PDFs and photos, and somebody types them into QuickBooks, the ERP, or a spreadsheet by hand. However AI could read them and fill the fields instead. Before your business relies on the AI solution, we test it against 50 of your past documents and you see how many come out right.

• The same question, asked for the fourth time

  An AI assistant that answers questions using your own manuals and price sheets, and shows its source every time.

  The answers your team keeps asking for are already written down in manuals, price sheets, SOPs and old quotes. However an AI assistant could derive those answers faster and more accurately from those documents, and show where each answer came from. Before anyone relies on it, we test it against 50 real questions your team gets asked, and you see the score. You also get a simple way to rerun it and confirm the answers are still correct whenever prices or policies change.

AI to close the blind spots

The things you find out too late.

You can only ask about what you already suspect. The expensive surprises are the ones nobody thought to look for, so configure AI to watch on your behalf.

• The question only you can answer

  AI to derive answers using the system that already holds your data, with no spreadsheet in between.

  Which products moved slowest last quarter? Which customers have not ordered since spring? Typed in plain English, with answers derived from the system that already holds your sales or operations data, and a number or a tailored chart if you want one. No export, no pivot table, no waiting on the one person who knows Excel.

• The order about to slip

  AI to warn you days before the customer would notice, while there is still time to act.

  On-time delivery is the number your customers grade you on, and most firms only learn of the slip after the phone rings. However the signals are already in your data, and most of them can simply be counted: an order sitting past its usual dwell time, a supplier running behind its own average, a part with a history of waiting on inspection. AI computes those from your records and checks them against past months, so you see how often the flag would have been right. And where the pattern is subtler than what a written rule can express, an AI-managed machine learning model can do the watching instead.

• How much should we order?

  AI to flag what is about to run short and what is sitting too long, from stock and order history you already have.

  Too much inventory ties up cash, too little costs the sale, and the spreadsheet everyone argues over is somebody's best guess with a trend line on it. Often you do not need a forecasting model for this: current stock, open orders, lead times and what demand did at this point last year are already in your tables. You ask in plain English, the arithmetic is done in the query rather than by the AI so the numbers are deterministic, and the rule is scored against past months to see how often it would have been right. Where the pattern genuinely needs a model, AI builds and manages one.

AI to move the number that matters

Something custom, built to move it.

A decision somebody makes over and over, where a few points is worth real money. Point AI at the pattern already sitting in data you own.

• Win more of the quotes you bid

  AI to score every incoming request, so effort goes where the revenue is.

  Most shops bid everything and win a fraction, because nobody has time to sort the list. However your own quote history already says which jobs you win, at what margin, and which ones quietly waste a week of estimating. AI turns that history into a score for each new request, then checks the scoring against quotes you have already won and lost, so you see how well it sorts before you rely on it. And where those factors interact in ways a written rule cannot express, an AI-managed machine learning model can do the scoring instead.

• Stop paying the invoice that is wrong

  AI to surface the exceptions before payment, rather than during a much later audit.

  At volume, nobody checks every line. Duplicate billing, a rate that does not match the contract, a quantity that does not match the receipt: each is small, and together they are a real number. However AI could read incoming invoices against your own contracts and receipts, and surface only the ones that do not reconcile. An AI coding assistant writes that comparison as code rather than leaving it to a language model, so the arithmetic is deterministic and the same invoice always gets the same answer.

• Keep the customers you already have

  AI to name the accounts drifting away, while there is still time to call.

  Churn is rarely announced. Order frequency stretches, basket size shrinks, a product line quietly disappears from their orders. However each of those can simply be counted per account from your own transaction history. None of it is hard to spot on one account; it is hard because nobody has time to look at hundreds of them every week. AI does that every week and hands you the accounts that are slipping, with the numbers that triggered each one, so the call you make is an informed one. The rule is checked against accounts you have already lost, so you know how often it would have warned you while there was still time.

• The customers who should be buying more

  AI to name the accounts with an obvious gap, and what to offer them.

  Your best customers rarely buy everything you sell, and nobody has time to work out which gap belongs to which account. However your own order history already shows what customers like them buy. AI compares each account against the ones that resemble it and hands your team a short list: this customer buys two of the three things their peers buy, and here is the third. It is checked against what accounts actually went on to order, so you know how often the suggestion would have been right before anyone picks up the phone.

How it goes

Three steps, and you can stop after the first.

1. 1

   A conversation, free

   You describe the job. I tell you whether AI is the right tool for it, and if something off the shelf already does it, I point you there instead.

2. 2

   Scope, price, and the test, in writing

   One job, one number, and an agreed definition of what working means, including which examples the result gets scored against. Nothing starts before you have all three.

3. 3

   Built, scored, handed over

   Running on your own data and inside the applications your team already uses, with the score in writing and a session so your people can operate it without me.

One scoped build is $5,000 fixed. Larger or less defined work runs at $250 per hour with fixed-bid pricing once the scope is clear, and continuing work on a retainer from $3,500 per month. Full rates are on the engagements section of the main page.

Questions

What owners and operators ask first.

• Can a business without a data team actually use AI?

  Yes, if you have data and a specific job to do. What has changed is cost: work that used to require a data team and a quarter of engineering time now takes weeks, because AI-assisted development compresses the build. What has not changed is that AI applied to a vague goal wastes money. The businesses that get value start with one concrete, repetitive task, not with a strategy.

• What does custom AI cost?

  One scoped build is $5,000 fixed: we agree the use case and the price in writing before any work starts, and you get it running on your own data with a handover session. Larger or less defined work runs at $250 per hour with fixed-bid pricing once the scope is clear, and continuing work runs on a retainer from $3,500 per month. There is no hourly meter on a fixed-price build and no retainer to sign.

• What is the smallest way to start?

  A conversation, which is free. If after that you want a considered answer rather than a verbal one, the smallest paid step is a $250 working session: one hour on your actual problem, followed by a written page covering what I would build, roughly what it should cost, and what to do instead if building it is the wrong move. It is credited in full against any engagement you book within thirty days.

• How do I know it actually worked?

  Because we agree the test before the build. Whatever gets built is scored against examples it never saw while being built, using the measure we settled on in advance, and you get that result in writing before handover. If the score does not clear the bar we set, you hear that from me rather than six months into relying on it.

• What data do we need before we start?

  Data that already exists somewhere retrievable, and one job you want done. A point-of-sale or ERP export, a folder of PDFs, an inbox, a QuickBooks file, sensor logs, or a spreadsheet somebody maintains by hand. You do not need a warehouse, a cloud contract, or a clean schema. If your data turns out to be too thin to support what you want, I will tell you during scoping rather than after you have paid for a build.

• What if an off-the-shelf tool already does this?

  Then I will tell you that and point you at it. A meaningful share of these requests are better served by a product that already exists for a monthly fee than by anything custom. Custom earns its keep when the decision is yours alone, when the data lives in systems no vendor integrates with, or when the advantage comes precisely from doing it differently than your competitors. Finding that out in a scoping call costs you nothing.

• Does this replace people?

  The work it takes over is the part nobody wants: the sorting, the checking, the retyping, the watching. The judgment stays with your team and they get the time back to exercise it. Every one of these builds leaves a person in the loop on anything consequential, because a model acting unsupervised on a decision that matters is a liability rather than an asset.

• Will it work inside the systems we already use?

  That is usually the point. A model living in a notebook nobody opens changes nothing. The output belongs where the decision is made: a flag in the order screen, a score on the quote list, a line in the report your team already reads. Where an application can be integrated with, I integrate; where it cannot, the result lands somewhere your team already looks.

• How long does it take?

  Weeks, not quarters. A scoped build is typically a few weeks from the first call to something running on your data. I build with AI coding assistants, which is a large part of why that timeline is realistic rather than optimistic.

• Do I need to be in Austin to work with you?

  No. Delivery is remote and most of this work does not require anyone in the room. I am based in Cedar Park in the Austin metro and spend part of the year in the Knoxville area, so on-site time is easy in either region and available elsewhere when a project genuinely calls for it.