AI Automation Consulting
We build the AI systems that let a business take on more without adding headcount, then run them for you. Choose your city below.
AI automation consulting means finding the repetitive work inside your operation, building the systems that take it over, and keeping those systems running. The version worth paying for ends in working software wired into the tools you already use, not a slide deck you are left to implement. We work with businesses in Los Angeles, Houston, Miami, New York and Chicago, and the engagement is the same everywhere: audit the workflow costing you the most, build the system that removes it, then run it for you from there.
Locations
For LA's lean, fast-moving operations: agencies, studios, clinics, e-commerce, logistics. We remove the recurring manual work quietly eating your team's hours.
For Houston's operations-heavy industries: energy, manufacturing, logistics, healthcare. The more process-heavy the business, the more manual hours there are to recover.
For Miami's fast-moving, deal-driven market: trade and logistics, real estate, hospitality, finance. We remove the manual back-office work that caps how much a lean team can handle.
For NYC's high-value firms: finance, legal, media, agencies. Where skilled labor is this expensive, automating the manual work carries some of the strongest returns anywhere.
For Chicago's diversified base: manufacturing, logistics and rail, finance, professional services. Different industries, same recurring manual work quietly consuming skilled hours.
What is different about each city is the work that dominates there, not the way the engagement runs. The audit, the build and the running of it are the same in all five. What changes is which workflow comes out of the audit first, because that follows the local economy and the paperwork it generates.
| City | The operations that dominate | What the manual work arrives as | Where a first build usually starts |
|---|---|---|---|
| Houston, TX | Energy and oilfield services, petrochemicals, manufacturing, port and marine logistics, healthcare | Field tickets, bills of lading, inspection and compliance reports, payer paperwork | Document intake, meaning whatever arrives on paper or as a scan and has to end up inside a system |
| Chicago, IL | Manufacturing, freight and rail, finance, professional services | Rate confirmations, bills of lading, supplier invoices, reconciliation files | Matching records across systems that were never built to talk to each other |
| Los Angeles, CA | Agencies, studios and production, consumer brands, clinics, e-commerce and logistics | Briefs, order and returns data, patient and client intake forms, scheduling | Intake and scheduling, where how fast you answer decides whether the work lands |
| New York, NY | Finance, legal, media, agencies | Contracts, client intake, diligence packets, billing detail | Reading and extraction, because an hour of skilled time costs more here than anywhere else on this list |
| Miami, FL | Trade and logistics, real estate, hospitality, finance | Customs and shipping paperwork, leases, booking and guest records, often in two languages | Document work that arrives in English and Spanish and has to come out in one structured form |
Two things follow from that table. The first is that the useful question to put to any firm is not whether they sit in your city, it is whether they have built for the kind of document your work arrives as. That is what decides how long the build takes and whether it still works in six months. The second is that order matters more than breadth: the workflow worth building first is the one that recurs most often and eats the most skilled time, and that is a different workflow in a refinery's back office than it is in a law firm's intake.
The city pages carry the local detail, including the state rules that apply to automated decisions where there are any. Texas and Illinois both regulate parts of this, and they do it differently: the Houston page covers the Texas Responsible AI Governance Act and the state privacy act, and the Chicago page covers the Illinois biometric rules, which is the one most likely to catch a system that touches employee records.
If your business is not in one of those five cities, we take the work anyway, and for most builds your address changes very little about how it goes. The five cities above are where we have written the most down, not the edge of what we cover. A manufacturer in Richardson or Carrollton is a Dallas and Fort Worth build, and it runs exactly like a Houston one. Illinois outside Chicago works the same way, as does the rest of Texas and the towns around the other four cities.
The reason distance matters so little is that there is nothing to install. The systems run on our infrastructure and connect to your software over the internet, so nobody has to be in the building to deploy a change or restart something at seven in the morning. A business two states away gets the same system and the same report on what that system produced, from the same people keeping it running.
What does decide how a build goes is what your operation already runs on. A company on mainstream cloud software with a usable API is a shorter build than one on an older on-premise system that was never meant to be connected to anything, and that gap is worth far more than the distance between you and whoever is building it. Ask about integration before you ask about the address.
Being nearby genuinely helps in one case. If the work still starts with paper somebody has to physically handle, a filing room to digitize or a plant floor to walk, then part of that job has to happen in the room. We will tell you so at the audit rather than after you have signed something. Everything that arrives as a file, an email, a form or a record is the same job from anywhere.
An AI automation consultant finds where your operation loses hours to repetitive work, builds the systems that take that work over, and wires them into the software you already run. The job has three parts, and the third is the one most firms skip.
Audit. Map where the time and money leak, and put a number on each one. This is the part that decides whether anything should be built at all. A task that eats twenty minutes a month is not worth a system, and the audit is what tells you which is which before anything gets built.
Build. Build the thing that removes that specific work. Not a platform and not a roadmap: a system that reads the documents, moves the records, drafts the reply, and puts the result where your team already looks for it.
Run. Keep it running once it is live. Anything wired into a live operation needs someone watching it when a vendor changes an API or a form grows a new field. We run what we build, on our infrastructure, so the hours you recovered stay recovered.
The work that comes out of the audit tends to follow the local economy, which is why the city pages set out different examples. What a business automation consultant in Los Angeles ends up automating for a production company or a consumer brand looks different from the finance, legal and real-estate paperwork an AI automation consultant in New York spends most of its time on. The same goes for the field tickets, bills of lading and intake forms behind AI automation and AI consulting services in Houston, and for the freight, manufacturing and reconciliation work that business automation consultants in Chicago are usually called in for. AI automation consulting in Miami adds one thing the other four do not, which is that the documents arrive in two languages.
Choose an AI automation consultant on four questions, and settle all four before you sign anything. Can they show you a system they actually built, and what it returned? Will they quantify the opportunity before you commit to a build? Do they build, or do they only advise? And once it is live, who keeps it running?
The second of those four has public evidence behind it. S&P Global Market Intelligence's 2025 survey of more than 1,000 enterprises across North America and Europe found that 42% of companies had abandoned most of their AI initiatives, against 17% the year before, and that organizations were discarding an average of 46% of their proofs of concept before those reached production. Work that was never sized against a number it had to pay back is the work that gets abandoned, which is why the opportunity gets quantified here before anyone agrees to build anything.
That last question is the one that separates the options, and it is the one nobody volunteers. A roadmap has no owner. A script left behind by a contractor who has moved on has no owner either. Here is how the usual approaches compare.
| Approach | What you actually get | Who it suits | Who keeps it running |
|---|---|---|---|
| BLACKSIG: audit, build, run | An audit that puts a number on the opportunity, then the working system, wired into the tools you already use | An owner who wants the work gone and does not want to staff for it | We do, on our infrastructure |
| An in-house automation hire | Someone on payroll who can build and maintain internal tools | Businesses with enough recurring build work to keep a role busy | Your hire, until they leave |
| A strategy or management consultancy | A roadmap, an opportunity assessment, a prioritized list | Leadership teams who need a plan everyone can agree on first | Nobody. Building it is a separate project |
| An RPA or traditional automation integrator | Scripted, rule-based workflows driving software you already own | High-volume processes with stable, structured inputs and no judgement calls | You, or a support contract with them |
| No-code tools your own team wires up | Simple connections between apps you already pay for | Light, low-stakes workflows a non-technical person can own | Whoever built it, in whatever time they have spare |
None of these is the wrong answer in every case. The mistake is picking one without asking the fourth question, which is how businesses end up with an automation nobody has looked at since the person who built it left.
That table compares categories rather than firms, and at some point you have to compare firms. We keep a named list for four of the five cities, with what each option is for and where we fit: the best AI automation consultants in Houston, in Miami, in Los Angeles and in Chicago. If your work is home services or small-batch manufacturing rather than a city shortlist, the same treatment by trade is in the home services list and the one for small manufacturers.
The system drops to maintenance rather than switching off. The arrangement that keeps a build improving is a running one, and ending it does not turn anything off: the system moves to maintenance, which covers keeping it alive rather than developing it further, and is set by how many systems are running and what they cost to operate. The data and the records the system has written stay where they already are, inside your own software.
Lock-in is the right question to put to anybody in this market, and "you own everything" is not automatically the better answer to it. A system handed over in full is yours, and it is also yours to keep alive, which sounds like a formality until you see what actually breaks one. It is almost never the model. It is a vendor shipping a new API version, a supplier form growing a field, a login expiring on a Friday. So ask two things rather than one: what you keep if the arrangement ends, and who is accountable for the thing continuing to work while it has not.
Our answer is the second half of that, and the trade is worth stating plainly rather than leaving in a contract. We run what we build, on our infrastructure, which is why there is nothing to install and why a change can go out without anybody being in your building. If your plan is for your own IT team to own and operate the system afterwards, we are the wrong shape for you, and a firm that builds it, hands over the keys and steps away will suit you better.
What stays with you either way is the work the system has already done. The fields it extracted, the records it created, the documents it filed: all of that sits in the software you already use, because a system that cannot write into the record your team actually looks at is one more place to type. That is also the thing to ask about during a demo. Find out where the output lands before you look at the interface.
What AI automation consulting costs is set by the workflow rather than by the firm, which is why nobody worth hiring quotes you before looking at one. Three things move the number further than anything else: how much of the work software can reach at all, what state the systems it has to connect to are in, and what keeping it alive is going to take every month once it is live. We do not publish a rate, because a number quoted before anyone has looked at your operation is a guess with a dollar sign in front of it.
The reachable share is the first thing to settle. A workflow where a person reads something, decides something and types something is mostly reachable. A workflow where the decision is the job, and the typing is incidental, is mostly not. Scoping the first one is a short conversation and scoping the second one is most of the engagement, and that difference shows up in what it costs long before any code does.
The second is integration, and it is the one that surprises people. Connecting to mainstream cloud software with a documented API is routine. Connecting to an on-premise system from 2009 that was never meant to talk to anything means building the way in before you build the thing, and that work is real whoever does it. Get somebody to look at what the system has to plug into before you take a number seriously.
The third is what happens after go-live, and it is the cost most quotes leave out entirely. A live system has a running bill: the model usage, the tools it has to write into, and somebody watching it when a vendor changes an API. We set out what sits on that bill in how to reduce operational costs with AI. A build quote with no running cost beside it is half a number.
| What moves the cost | Cheaper when | More expensive when |
|---|---|---|
| How the work arrives | Already inside a system, as records or files | On paper, or as scans somebody has to handle first |
| What it has to connect to | Mainstream cloud software with a documented API | Older on-premise software with no supported way in |
| How settled the process is | One agreed way of doing it that everyone follows | Four people doing it four different ways |
| The exceptions | A handful, and somebody can list them | A long tail nobody has ever written down |
| What finished looks like | The system can complete the job and file it | A person has to approve every single output |
| Who keeps it running | We do, on our infrastructure, as part of the arrangement | Your team, who then need the documentation and the training to do it |
When you compare two quotes, read down that table rather than down the price. Two firms looking at the same workflow can land a long way apart because one of them has assumed the clean version of every row and the other has asked. The audit is what turns those assumptions into answers, and it is why we quantify the opportunity before anyone agrees to build anything.
Hiring someone in-house is cheaper past a certain volume, and the arithmetic is worth doing before you talk to anyone. A hire makes sense when you have enough recurring build work to keep somebody busy all year, and enough systems already running to justify a full-time person maintaining them. Below that line you are paying a salary to have somebody wait for work.
The number to run it against is the loaded cost, not the salary. In its Employer Costs for Employee Compensation release for March 2026, the U.S. Bureau of Labor Statistics put private-industry compensation at an average of $46.60 per hour worked: $32.60 in wages and salaries and $14.01 in benefits. A salary line understates what a hire costs by roughly a third once the employer has paid for all of it, which is the same reason a manual workflow costs more than people assume.
Run the same calculation on the work itself before you decide either way. We set out the whole method, including the monthly figure, in how much time and money AI automation actually saves.
An AI automation consultant is the wrong call in three situations, and an audit is what surfaces them before anyone builds anything.
The first is a process that is not settled. If how the work gets done is still changing month to month, automating it freezes a version you are about to abandon. Settle the process by hand first, then automate whatever survives.
The second is volume. Savings scale with how often the task recurs and how expensive the person doing it is, so a quarterly job that takes one person an hour is not a build, however irritating it is to that person.
The third is that something off the shelf already does it. Plenty of industries have mature software covering the exact workflow you are looking at, and if that exists for yours, buy it. It will be cheaper and better supported than anything built from scratch.
One more is specific to us. If you want to own and operate the system with your own team, we are the wrong shape: we run what we build, and that is the arrangement rather than an add-on to it. A firm that builds, hands over the keys and steps away will suit you better.
Finds where a business loses time and money to manual, repetitive work, then builds the AI systems that take it over. BLACKSIG audits the opportunity and puts a number on it, builds the working system, and then runs it for you from there, rather than handing you a recommendation you have to go and implement.
We build. Most AI consulting stops at an audit or a roadmap, and most build shops cannot tell you what is worth building in the first place. We do both, and then we keep the system running for you on our infrastructure.
RPA follows fixed rules against structured, predictable inputs: it clicks the same buttons in the same order every time, and it breaks when the input changes shape. AI automation handles the messy inputs that defeat rules, such as documents in different formats, free text, and requests phrased a hundred different ways. Most real operations need some of both, which is covered in AI automation versus traditional automation.
If it recurs, follows a pattern a person could describe, and eats skilled time, usually yes. The best candidates are document and data workflows: extracting fields from paperwork, moving records between systems that do not talk to each other, compiling the same report every week. Judgement calls, pricing and relationships stay with your people.
Yes. The five cities above are where we have the most depth, not the edge of what we cover: a business in Richardson, Carrollton or Fort Worth is a Dallas and Fort Worth build and runs exactly like a Houston one, and the same goes for Illinois outside Chicago and the towns around the other four. The systems run on our infrastructure and connect to your software over the internet, so there is nothing to install and nobody has to be in the building to deploy a change. What matters more is what your operation runs on, because the system has to connect to the tools you already use. The one case where being nearby genuinely helps is work that still starts with paper somebody has to physically handle.
The system drops to maintenance rather than switching off. Ending the arrangement does not turn anything off: the system moves to maintenance, which covers keeping it alive rather than developing it further, and is set by how many systems are running and what they cost to operate. The data and the records the system has written stay inside your own software, where they already were. Ask any firm in this market two questions rather than one: what you keep if the arrangement ends, and who is accountable for the system continuing to work while it has not. A system handed over in full is yours and is also yours to keep alive, and what breaks one is almost never the model. It is a vendor shipping a new API version, a supplier form growing a field, a login expiring. We run what we build, on our infrastructure, so if your plan is for your own IT team to own and operate it afterwards we are the wrong shape for you.
The workflow sets the cost, not the firm, so there is no rate card here and a firm that hands you one before looking at your operation is pricing a guess. Three things move it most: how much of the work software can reach, what state the systems it has to connect to are in, and what the system costs to run every month once it is live. A workflow already sitting in mainstream cloud software with a documented API is cheaper than the same workflow arriving on paper or locked in an older on-premise system. Ask any firm for the running cost alongside the build cost, because a build quote with no running cost beside it is half a number.
It depends on the scope, so we agree the timeline in the roadmap before anything is built. A first system is scoped to one workflow rather than a whole department, which keeps it small enough to reach live work without a long programme. The system we built for the research lab at Universidad Maimonides has been running on our infrastructure ever since.
Proof
Wherever you are, the work is the same: find the work worth automating, build the system, and keep it running for you. See it built and shipped in the Universidad Maimonides case study: a manual data workflow automated, with 15 hours a week recovered.
We map where your operation loses hours to manual work, put a number on each one, and turn that into a roadmap of what to build and in what order. Our engineering team builds it, it lives on our infrastructure, and we own the result from there.