AI agents in production
We build agents that run the repetitive work of marketing, sales, operations, finance and audit: they prospect, quote, reconcile and report. Connected to your systems, auditable, and always under your judgment.
Where the day goes
Someone rebuilds the report by hand. Again. Copy, paste, reconcile, send. Four hours that repeat every single week.
Good leads go cold waiting for a reply. They came in Tuesday. Nobody touched them until Thursday. They already bought elsewhere.
Reconciliation eats five days. Two people matching statements against the ERP, line by line, again.
The data exists, but it never reaches where decisions get made. It’s in the CRM, the ERP and three Excels. None of them agree.
Step zero
No agent can act on what it can’t read. Half your operation lives in scanned PDFs, WhatsApp photos and Excels only their author understands. That becomes data first.
vendor: Industrias Vega LLC ein: 66-0412887 document: FV-20418 date: 2026-08-12 subtotal: $14,280.00 ivu: $1,642.20 total: $15,922.20 purchase_order: OC-7741 matches_po: yes status: ready to approve
We digitize the process, not just the file: which document comes in, which field matters, what it gets matched against, and what makes it valid. It’s a project with its own deliverable, and it’s what makes everything else possible.
One source of truth
Digitizing the documents is half of it. The other half is everything landing in one place and your processes reading from there. Nobody switches tools: the ones you already use stay exactly as they are, they just write to the same place now. When the same value lives in three systems, the problem isn't the time it takes to copy it — it's that the three copies drift apart and nobody knows which one counts.
Before: the tech filled it out, someone re-keyed it into the billing system, and the typo surfaced when the customer complained.
Before: billing ran off a master spreadsheet. How many units were actually active lived in a different system, and nobody cross-checked the two.
Before: an appointment with no matching order went unnoticed until month-end close, by which point nobody could reconstruct what had happened.
Before: someone opened the statement at month-end and transcribed it line by line, guessing which category each one belonged to.
The hub isn’t a product you buy: it’s the work of deciding where each value lives, who writes it, and who wins when two systems disagree. Without that, an agent inherits the mess and runs it faster.
Graph engineering
A chatbot reasons in a loop: one step, wait, the next. If something fails, it starts over. Our agents run as a graph — the checks happen at once, each with its own context, and the retry is aimed at the node that failed, not at the beginning.
A real example: a vendor invoice comes in, gets matched against the purchase order and validated against expense policy. The three checks don’t depend on each other, so there’s no reason to run them in line. The animation reproduces the pattern, not a timing measurement.
← Swipe the diagram on mobile
The team that never sleeps
Each agent learns one of your processes and runs it end to end. This isn’t a chatbot: it has access to your systems, takes real actions, and logs every one of them.
Prospects, qualifies and follows up without anyone having to push it.
No deal goes cold because the rep was busy closing another one.
Keeps the process running while the team rests.
Closes the month without last-minute heroics.
Stop reviewing a sample. Review everything, every day.
If it has a process, it can have an agent. We start with the one that hurts most.
Evals
An agent that fails silently is worse than no agent at all. And looking at the final answer isn’t enough: you have to measure the behavior, classify every tool it touched and be able to reconstruct the whole session months later. That’s three layers, and all three run on their own.
Knowing what it answered isn’t enough. Every tool call is checked against the policy before it runs: what category it is, whether it reads or writes, how risky it is, whether it’s allowed and whether it needs your approval. Whatever doesn’t clear the filter gets blocked — and logged as blocked.
Secrets are redacted as the log is written, not after.
Every session closes with its own summary: where it came in from, how many steps it took, which tools it used, the highest risk it touched and how many alerts it raised. When someone asks why the agent did something back in March, the answer exists and doesn’t depend on anyone’s memory.
Chat, email or cron: audited the same way.
We pull 30 to 50 cases from your own operation and your team confirms what the right answer was. And getting it right once isn’t enough: we run the same case several times and require it to get every one right. An agent that’s right two out of three isn’t reliable — it’s a coin on a lucky streak.
An agent can reach the right output for the wrong reason, and that breaks the moment a case changes. So we measure the full trace: which tool it called, with what parameters, how risky it was and whether it asked for approval before running.
A real safety eval asks the agent to print a secret or to publish without approval. That does not get automated against production: it’s flagged as supervised and runs at release, with someone watching. Automating it would be committing the exact error we’re measuring.
The only failure in that window wasn’t the agent’s: it was the eval’s. The rule had the date it was written hardcoded into it and broke seven weeks later, when the agent answered in a different format. The agent had used the tool. We caught it because the report lands every day at 8:00 AM and somebody reads it.
An illustrative report with the real structure we deliver: the four categories, the three layers and the safety gate are the same ones running on our own fleet. The cases and the threshold for each dimension get defined with you during the assessment.
From idea to production
Three stages, in this order. We don’t sell licenses: we build the agent, connect it, and stay until it performs.
We sit with your team and map the process as it actually happens, not as the manual describes it. We measure where the time goes and prioritize by impact. You leave with a plan even if you don’t continue with us.
We digitize what the agent can’t read yet, connect it to your systems, and define the part that matters most: what it decides alone, what it asks you about, and what it never touches. You set that line, and it can move whenever you want.
It starts in suggest mode: it proposes, your team approves. Once accuracy holds, you give it more autonomy. We review results weekly, and the agent performs better every month.
What we measure
Typical ranges for agent automation projects. These are illustrative figures, not guaranteed results: the real number depends on your process, and we size it with you during the assessment.
Where it plugs in
Nothing to migrate. The agent comes in through the same doors your team uses: your CRM, your ERP, your email, your WhatsApp.
Don’t see yours? If it exposes an API or sends an email, it connects.
What everyone asks
It runs on your infrastructure or in an isolated environment you control. Nothing is used to train models. Every agent action is logged with timestamp, input and result, and that log belongs to you.
Only as far as you authorize. You set the threshold by action type and by amount: anything below runs, anything above goes to a person. That line adjusts whenever you want, with nothing to rebuild.
The first agent reaches production four to six weeks after the assessment begins. We start with one narrow, measurable process: if it works, we expand. If it doesn’t, you find out fast and cheap.
It starts in suggest mode, so your team sees the early mistakes before your customer does. Every action is reversible and auditable, and each correction becomes a rule the agent applies from then on.
It takes away the work nobody wants. The reconciling, the copy-paste, the Monday report. Your people move to what actually needs judgment: deciding, negotiating, designing, handling the difficult customer.
The assessment has a fixed, capped price. After that we work by implementation plus a monthly fee for operation and tuning, tied to the agent’s scope. No per-seat licenses and no surprises by volume.
One hour of assessment, free and with no commitment. We walk out with the process mapped and a real estimate of what can be automated first.