Every operation we walk into has the same job hiding in it: a person reading a document and typing what it says into a system. Invoices, BOLs, POs, emails, voicemails, photos from the field. That job is now automatable — reliably, affordably, and if it has to be, entirely on your own hardware. That's the AI we build for Houston businesses. The rest is mostly demos. (When the paperwork in question is inbound leads and follow-ups, that's our customer intake & outreach practice.)
None of these need a moonshot. Most of them need a model, a review queue, and some plumbing.
Somebody's whole job is reading a document and typing it into a system. That job can now be checked by a person instead of done by one.
The PO arrives as a PDF, gets keyed into the ERP, then keyed again into a portal. Every retype is another chance to be wrong.
The answer exists — in a 2019 email thread, a shared drive, or the head of the one person who has been here twenty years. Nobody can search any of it.
Contracts, compliance, or common sense say your documents stay in-house. That rules out the SaaS tools — not AI itself.
Copy-paste into a chat window proved the concept. Wiring it into your real systems, with controls and an audit trail, is the part that pays.
The board wants an answer and vendors want a signature — before anyone has checked whether the data can support any of it. Start there instead.
Calls and voicemails stack up overnight and get triaged at eight — by which time the customer has already called somebody else.
Site photos, a handwritten ticket, and a text thread. Someone at the office turns that into the record days later, from memory.
Ten things we build, from document extraction to phone calls to forecasting. Every one ships with validation rules, a human veto where it matters, and documentation your team can run without us.
LLMs reading the paperwork your team currently retypes.
Self-hosted open-weight models for data that can't leave the building.
Copilots and answers wired into the ERP, WMS, and CRM you already run.
Automation where the model does the reading and a human keeps the veto.
Software that carries a whole task through, not one prompt at a time.
The calls, voicemails, and meetings nobody has time to write up.
An assistant on your site or portal that answers from your documents, not the internet.
The camera roll your crews fill up, turned into records.
The part of AI that isn't an LLM — ordinary models on your own history.
What Lean Six Sigma is to process, this is to your data.
Some of the most useful weeks we spend on this produce a policy, a training session, or a number — not software. These are engagements in their own right.
A short written policy your staff can actually follow: what may be pasted into a public chatbot, what may not, which tools are approved, and who to ask.
Which AI tools your team is already using, on what data, under whose terms. It is usually a longer list than management expects.
A working session for your team on the AI in the licences you already pay for, with prompts and checklists for their actual jobs rather than a generic webinar.
An honest read on the AI product a vendor is quoting: what it does, what it costs at your volume, and how much of it is a feature you already own.
Two or three approaches run against your own documents and scored on the same sample, so the decision is made on measured accuracy instead of a demo.
Accuracy tracked, spend capped and alerted, model versions pinned, and a migration plan for the day your provider retires the one you are on.
Any of these can be bought on its own. None of them obliges you to build anything with us afterwards — and if the honest finding is that you already own the tool you need, that is what the report will say.
The same handful of patterns, pointed at the paperwork a particular business actually drowns in. These are the Houston operations where the payback shows up fastest.
BOLs, PODs, rate confirmations, and status emails all day long. Extraction covers the partners who will never do EDI, and the exceptions still reach a person.
EDI & B2B logistics integration →Purchase orders and acknowledgements keyed by hand, spec sheets nobody can search, and a quoting process that lives in one person's memory.
System integration →Photos, timesheets, and daily reports from the field turned into records — and voicemail from the job site turned into a work order before it is forgotten.
Mobile apps for field crews →Inspection paperwork, panel programs, and deficiency lists read into records that produce an NFPA 72 report on demand instead of a folder of scanned PDFs.
Fire alarm programming & records →Contracts and engagement letters summarized, intake forms read, and follow-ups drafted from calls — with a person approving anything a client will see.
Customer intake & outreach →Field tickets, JSAs, and vendor invoices matched against the contract, and daily reports assembled from the documents the crews already send in.
Data entry & analysis →Nothing here needs a platform contract or a year of discovery. The first useful result usually lands in weeks.
An hour on what your team retypes, what it costs you in time, and which of it a model can read reliably. You get a ranked list either way.
Two or three approaches scored against a real batch of your own documents, so the accuracy number you plan around came from your paperwork, not a vendor's.
One queue, live, with validation rules and a review screen. Your team approves every call the model isn't sure about, and we watch the error rate together.
The pilot either beat the baseline or it didn't. If it did, the next document type is cheap. If it didn't, you spent weeks instead of a three-year commitment.
Source code in your repository, prompts and validation rules in plain files you can read, API keys in accounts billed to you, and documentation written for whoever inherits it. If we deployed models on your own hardware, they keep running whether or not we are still involved — and if you would rather someone else maintain it, none of it is locked to us. That is the same deal as our custom software work.
It won't fix a broken process — automating a mess just gives you a faster mess, which is why we sometimes recommend a process improvement pass before any model gets involved. It won't be right 100% of the time, which is why everything we ship has validation rules and a human veto on the calls that matter. And it won't replace your team: the goal is that people stop retyping and start reviewing.
It also isn't a strategy. "We need AI" is not a project; "stop hand-keying 400 invoices a month" is. We scope the second kind — small, measurable, and honest about the cases where the right answer is a boring script rather than a model.
One document type, one queue, a few weeks. A measured pilot beats a platform contract you're stuck with for three years.
AI Builder and Power Automate can carry the first workload inside the Power Platform licences you already pay for.
Review screens, queues, and audit trails are custom software — we build that side too, so the model isn't an orphan.
BOLs, PODs, rate confirmations, and status emails all day long — 3PLs and distributors are where this earns its keep first.
Model-agnostic on purpose. Which one runs a given workload is a configuration choice you can change later, not an architecture you're married to.
No — it rules out the SaaS tools, which is a different thing. We deploy open-weight models like Llama and Qwen on your own hardware via Ollama: no per-token fees, no data crossing your network boundary, no vendor terms to renegotiate. We run local models in our own operation, so this is a setup we maintain daily, not a slide.
Start with a pilot: one document type or one queue, fixed scope, a few weeks, and a measured baseline so you can judge it honestly. That is a fraction of what a platform contract costs, and it tells you whether the bigger build is worth doing before you commit to it.
A document-extraction pilot typically shows real results in two to four weeks. The model is rarely the slow part — the plumbing into your systems, the validation rules, and the review workflow are where the engineering lives, and that is ordinary software work with ordinary timelines.
No. Practical AI in 2026 is systems integration, not research — the models are built; the work is wiring them safely into your processes. What you keep is normal software: readable code, documentation, and monitoring your existing people or a modest support arrangement can run.
Old systems are our home turf. AI usually sits alongside the legacy core rather than inside it — reading the documents, files, and emails around it, and talking to it through the database, an API layer we add, or the same interfaces your staff use today. No rip-and-replace required.
Real, and managed like any other failure mode. We constrain the model to extraction and drafting rather than open-ended answers, validate output against your business rules, route low-confidence cases to a person, and measure accuracy over time. Nothing we ship answers your customers unsupervised on day one.
Whichever one wins on your workload. We build against an abstraction rather than a vendor, so a model is a configuration choice you can change later: Claude and GPT through their APIs or Azure OpenAI, and Llama- or Qwen-class open-weight models when the work has to stay on your hardware. Where it matters, we score two or three against the same sample of your own documents and let the numbers pick.
Yes, and it is one of the fastest wins for a business that misses calls after hours. The realistic version takes the caller's details, answers what it can from your own information, and files a ticket or work order with everything captured — rather than pretending to be a person. Recordings and voicemail get transcribed, summarized, and attached to the right customer, in English or Spanish.
We do, with the boring parts done properly: it answers from your catalog, policies, and manuals with the source cited, it can look up a real order status instead of guessing, it has explicit limits on what it may quote or promise, and it hands off to a person with the transcript attached the moment it is out of depth. A bot that invents a price is worse than no bot.
Often not for the general assistant work — if you own the licences, use them, and we will happily train your team on them instead of selling you a second tool. What Copilot does not do is read your inbound documents into your ERP, answer from systems it cannot reach, or run a queue with validation and an audit trail. That gap is the part worth building.
We size the model to the job rather than defaulting to the largest one, cache and batch where the workload allows, and put a hard monthly cap with alerting on every API key we set up. You get the per-document cost measured during the pilot, so the monthly bill is something you approved in advance rather than discovered. Self-hosted models remove the per-token line entirely.
Not in anything we have built. The pattern that works is that the model does the reading and the typing while your people do the checking and the exceptions — the same headcount handling more volume, with the tedious part gone. If a vendor tells you a model will run a queue unsupervised, ask them who signs off on the mistakes.
Yes. We are based in Houston and are glad to sit in your conference room anywhere in the Greater Houston area — Sugar Land, Katy, The Woodlands, Conroe, Pearland — and we also run these engagements remotely for clients across Texas and the rest of the US. Document automation in particular rarely needs anyone on site.
We'll tell you whether AI can read it reliably, what a pilot costs, and when the honest answer is a plain script.
Call 832-598-8234 or email msco@stoneagesoftware.com. Houston, Texas — serving Houston, The Woodlands, Conroe, Sugar Land, Katy, Pearland, and the Greater Houston metro.
We read your message, an engineer replies with questions or a straight answer, and if it's worth a call we book one. No drip campaign, no handoff to sales.
