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The answer is in somebody’s inbox. Finding it takes an hour.
Search your own email and files, with a link back to the exact message it came from.
AI automation & consulting · Clarksville, TN · working nationwide
AI automation services that do the work, not the demo.
We find the hours your team loses to searching, sorting and retyping, then build AI that handles them. A person stays in charge of anything that sends, files or pays. One principal, start to finish.
Free scoping conversation. Replies typically within one business day.
What you
walk away with
MAP
Where the hours actually go, written in your words.
BUILD
One working automation on your real data. Not a mockup.
GATES
A person approves anything that sends, files or pays.
LOG
Every action recorded, so you can check what it did.
Sound familiar?
Problem to what we build
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Every request gets retyped into two or three systems.
Intake captured once and filed everywhere it needs to go.
03
New leads wait until someone has a free minute.
Enquiries sorted and a reply drafted for you. A person hits send.
04
Your team answers the same questions every day.
Answers drawn from your documents, not from the open internet.
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Someone builds the same report by hand every Monday.
The numbers pulled together on a schedule and waiting for you.
06
You tried ChatGPT and it made something up.
Answers that cite their source, and say so when they do not know.
Proof · Mid-size law firm
17,000+
emails, now answerable by search
BEFORECase-relevant correspondence buried in 17,000+ emails across attorneys. Finding the right thread meant searching mailboxes by hand.
AFTERCase-relevant correspondence resolves by search rather than by manual review. Results return with thread citations attorneys can open and verify.
See all the work →How it goes
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01 · SCOPE
A free conversation about where the time goes. You leave knowing whether this is worth doing.
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02 · BUILD
One automation on your real data first, so you judge it on your work, not a demo.
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03 · VERIFY
Checked against real cases, with review gates on every step that acts.
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04 · SHIP
Live in your tools, with a log of everything it does and a person you can call.
01 · SCOPE
Where AI automation actually pays off
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Applied AI past the demo stage. Retrieval over private corpora with citations back to source, classification at scale, and multi-agent infrastructure with persistent memory, review gates, and audit-grade logging. The unglamorous parts are the ones that decide whether AI survives contact with production.
Retrieval is the most common starting point. An organization already holds the answer somewhere across contracts, email, case files, or documentation, and the cost is not that the answer is missing but that finding it takes a person an hour. Retrieval over a private corpus turns that into a query, and citations back to the source thread are what make the result usable rather than merely plausible.
Classification at scale is the second. Sorting, routing, and tiering a volume of inbound that a person cannot keep up with, where the cost of a miss is a real deadline. Third is standing work: multi-step systems with persistent memory that carry a task forward across days instead of starting cold every time, which is what an AI agent actually is once the marketing is stripped off.
Not sure yet which of these you need? Then work out where AI fits first, and build the part that earns it.
02 · RIGOR
Why most AI projects stall after the demo
The unglamorous parts decide whether AI survives contact with production. A demo has to succeed once on an input somebody picked. A system running unattended has to behave on inputs nobody anticipated, and has to be answerable afterwards for what it did and why.
Three things carry that weight. Verification gates built as first-class agents rather than as a check bolted on at the end, so the step that decides and the step that acts are separate and a person can sit between them. Audit-grade logging of every action, because a system whose behavior cannot be reconstructed cannot be trusted with anything consequential. And citations, so a retrieved answer can be opened and checked against the source rather than believed.
This is also the honest limit of the practice. Where an answer has to be right and cannot be verified, the correct design keeps a person in the loop rather than claiming a confidence the system does not have. Scope decides the price more than anything else, which is why what AI automation costs is answered with the drivers rather than a number.
03 · EVIDENCE
Systems already running
The problem and the build for each of these sit on systems already in production, alongside the rest of the practices.
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Internal AI agent platform (HDS)
One principal runs several workstreams in parallel, because review and verification are automated rather than queued behind a person. The agents that built this site are the same ones used in client work.
Common questions
How is this different from a chatbot?
A chatbot answers questions. Automation does work: it reads the inbox, sorts it, drafts the reply, updates the system and stops for approval before anything goes out. The parts that make it safe to run unattended are the ones a demo never shows.
Can it work over our own documents and email?
That is the usual starting point. Retrieval runs over data you control and every answer links back to its source document or thread, so a person can open the original and check it. An answer you cannot trace is not usable for work that carries consequences.
What stops it from acting on something it got wrong?
Review gates. The step that decides is separate from the step that acts, and a person approves anything that sends, files or pays. Every action is logged, so what the system did can always be reconstructed afterwards.
What do AI automation services cost?
It depends on how many systems the work touches and how much of it can run without a person. The cost page walks through what drives the number, and the first scoping conversation is free.
Do you use this yourselves or only sell it?
HDS runs its own multi-agent platform for delivery, with persistent memory, code review and verification gates as first-class agents, and audit-grade logging of every action. The agents that built this site are the same ones used in client work, which means the failure modes described here were found the hard way rather than read about.
Next Step · Direct Principal Scoping
What would you hand off first?
Start with a free scoping conversation with Mike Hyams, the person who builds and supports the work.