Automation that pays for itself
We find the process that is costing you hours, build the system that removes it, and measure the result against the number it started at. The engagement begins with a conversation about your work, not a specification.
Four recent client engagements, and the figure that moved in each. The case studies further down carry the full context.
Services
What we build
The work falls into four services, and most engagements use more than one. Which one you start with comes out of the first conversation rather than a decision you have to make now.
AI agents and LLM systems
Your team reads, sorts, and answers the same documents and questions every day, and the volume grows faster than the headcount you can add.
A system that reads the documents, answers the questions, and routes what needs a person, with every answer traceable to the source it came from.
Workflow automation
A process your business depends on runs on people copying data between tools, and it breaks quietly whenever the person who knows it is away.
The process runs on a schedule, and a person reviews the exceptions instead of assembling the whole thing by hand every week.
AI strategy
You have a list of things AI could do for the business and no reliable way to tell which one pays for itself first, or whether any of them do.
A ranked shortlist that says what each process costs you today, what automating it would take, and what to do first.
Data and backend engineering
The automation you want sits on top of data that is slow, scattered across systems, or does not agree with itself from one report to the next.
A data layer that is fast and consistent enough that the systems built on top of it can be trusted without a manual check.
How an engagement runs
Diagnose, design, build, measure
The same four steps apply whichever service you start with. The last one is the part most automation projects skip, and it is the only one that tells you whether the work was worth doing.
Diagnose
Design
Build
Measure
Case studies
What this looks like in practice
Every number below is the one the work produced, including the ones that are estimates and are labelled as such.
Political intelligence and advertising
Turning FCC filings into roughly $77M of tracked political ad spend
A four-stage LLM pipeline that turns raw FCC contract filings into queryable weekly ad spend, with every dollar traceable to its source document.
Digital agencies and product development
From $500 to $50 a month: rebuilding an agency automation stack
Migrating a digital agency off a hosted no-code platform onto self-hosted n8n and Python cut costs 90% and responses from 3 minutes to under 20 seconds.
Legal services
From 5 hours to under 2 minutes: a legal firm's lead report
Re-engineering a manual weekly lead report with n8n and AI-powered validation cut it from 5 hours to under 2 minutes for more than 12 executives.
ROI calculator
What would automating this task save you?
Enter what the task costs you today. The estimate updates as you change the inputs, and every assumption behind it is listed below.
Prefer to talk through your own numbers? Get in touch
hours per week
Estimate based on conservative assumptions: 48 working weeks, your selected automation share, fully loaded labor cost. Figures in USD.
FAQ
Questions worth asking first
The ones that come up before an engagement starts, answered directly. The full set covers pricing, technology and data privacy.
What is AI automation, in plain terms?
It is software that does a repetitive job your team currently does by hand, where part of that job involves reading or judging something. Ordinary automation moves data between systems on fixed rules. AI automation handles the steps where the rule is hard to write down: sorting a message by what it is about, pulling figures out of a document that has no consistent layout, or answering a question from a body of knowledge. The two are usually combined in one system.
How do we know if a process is a good candidate?
Three things: it repeats on a schedule, it consumes hours somebody could count, and an error in it costs something. A process that runs twice a year is a poor candidate no matter how tedious it is. If you want a number before talking to anyone, the ROI calculator on this site estimates the annual cost of a repetitive task from four inputs.
Who actually does the work?
I do. Datasmarts is one person, Jesus Martinez, and the person you talk to on the first call is the person who writes the code. There is no account manager layer and no handoff to a delivery team you have not met. The trade-off is capacity: I take on a small number of engagements at a time, so timelines are real rather than optimistic.
How does pricing work?
In two stages. The assessment is priced on its own and produces a ranked shortlist with a cost against each candidate process. If a build follows, it is quoted as fixed scope from that shortlist, so you are approving a specific piece of work at a known price rather than opening an hourly meter. Get in touch with the process you have in mind and you get a number for the assessment.
What if the assessment says we should not build anything?
Then it says so, and you have still got the thing you paid for. Some processes run too rarely to justify the work, some are about to change and would be automated twice, and some are cheaper to fix by removing a step than by automating it. Knowing which of those you have is worth more than a build nobody uses.
Where does our data go?
Onto infrastructure you control, by default. Pipelines read from the systems your data already lives in and write results back to a store in your accounts. Where a language model is involved, the relevant text is sent to that model provider for the duration of the call, and which provider that is becomes an explicit decision rather than an assumption.
Tell us which process is costing you the most
That is the whole first conversation. If the process turns out not to be worth automating, we will say so, and that answer costs less than finding it out halfway through a build.