We don't sprinkle AI on your business. We rebuild the work, then deploy the agents.
Most AI projects stall because someone added AI to a process nobody had mapped. AgentivX works the way a forward deployed engineer does: we sit with your team, find out how the work really gets done, and build governed AI agents inside the systems you already run, with a measured baseline before we build and proof after.
Three reasons, and none of them is the model.
The SOP is not the real process.
The written version says six steps. The real one has fourteen, with loops nobody wrote down, and the key knowledge lives in one person's head.
The time hides between hand-offs.
Speeding up each step rarely helps. The days are lost waiting between people, teams, and systems, and that is where re-engineering pays.
A new tool nobody opens.
Asking your team to work in yet another app slows adoption. Agents should show up where people already work.
Map it. Measure it. Sort it. Build it in. Prove it.
- 01
Map the real process
We interview the people who do the work, review the history in your systems of record (CRM, accounting, HR, ticketing), and read the documents you already have. You get a map of how the work actually flows, loops and exceptions included.
- 02
Measure the baseline
Before we build anything, we measure cycle time, how often work gets sent back, how much goes straight through, accuracy, and cost per transaction. Those numbers become the scorecard.
- 03
Sort every step
Each step goes into one of four buckets: delete it, automate it with plain rules, give it to an AI agent, or keep a person on the decision.
- 04
Build inside your systems
Agents work in the tools your team already uses. Approvals arrive in Slack, Teams, or email. No migration and no new screen to learn.
- 05
Prove it
We report against the baseline at 90 days and again at six months, then pick the next workflow together.
Every step earns its place, or goes.
Example: paying a vendor invoice. One step from the process lands in each bucket.
Delete
Steps that only exist because of an old workaround.
Re-typing invoice details into a second tracking spreadsheet.
Plain code
If this, then that. No judgment, so no AI needed.
Matching the invoice to its purchase order and vendor record.
AI agent
Judgment, with enough history to learn from.
Coding each line item to the right account from past invoices, and flagging the odd ones.
Human decision
Money, hiring, signatures, and anything risky stays with a person.
Approving the payment.
Agents work where your team already works.
No migration and no retraining on a new app. Agents read and write in the systems you run today.
- Google Workspace
- Microsoft 365
- Slack
- Microsoft Teams
- HubSpot
- Salesforce
- QuickBooks Online
- ADP
- BambooHR
- Gusto
- Your ATS
- Your ticketing system
No system yet? We can set one up. That is what our AI operating systems are for.
AI that proposes. People who decide.
- Agents propose, people approve. Any action that changes your data or contacts a person waits for someone with the authority to make that change by hand.
- Approved changes run as the approver, with their permissions, so AI can never do more than the person who signed off.
- Rejected or expired proposals never run.
- Every proposal, decision, and change is logged, so you can see who approved what, and when.
Sheena Lindsey-Smith
The best forward deployed engineers bring four things at once. Most people bring one or two.
Knows how the work gets done
30 years in HR and workforce operations, 10 years in IT, and service as a U.S. Army veteran. She has built AI systems for HR, healthcare, real estate, and nonprofit teams.
Ships production software
AI products built and shipped, including AgentivX RealtyOS, ClinivX for healthcare, and the multi-tenant platform underneath them.
Judgment about AI
Knows which model fits each job, what to trust it with, and where a person must decide. Every system she builds keeps a human on the decisions that matter.
Talks to leadership
Turns process maps and numbers into the outcome each leader owns: cost and close time for finance, revenue for sales, faster hiring and day-one readiness for HR.
Start with one department. Prove it. Then scale.
Process X-Ray
About 2 weeks · one department
The real process map, your baseline numbers, the four-bucket sort, and the after map with the targets we will hold ourselves to. Credited toward the build.
FDE Sprint
About 8 weeks · one workflow
We re-engineer the workflow and build the agents inside your systems, with approvals and an audit trail. The baseline is locked before we start.
Embedded FDE
Monthly
Results reported against the baseline, the next workflow, model and cost tuning, and exception handling.
Prefer to start small? Book a $297 Setup Session.
Forward deployed AI, in plain terms.
- What is a forward deployed AI engineer?
- An engineer who works inside your business rather than from a distance. They learn how the work really gets done, re-engineer the process, build and run the AI agents that do it, and prove the results against a measured baseline.
- How is this different from buying an AI tool?
- A tool adds features to the process you already have. A forward deployed engagement fixes the process first, then puts agents into it, inside the systems you already use. That is why the results show up in cycle time and cost, not only in demos.
- Do we have to change our software?
- No. Agents work inside your current systems, and approvals come to you in Slack, Teams, or email. If you do not have a system of record yet, we can set one up for you.
- Will this replace our staff?
- The goal is to take repetitive work off your team so they can spend their time on higher-value work. People keep every decision that carries real risk, such as payments, hiring, and signatures.
- Which processes are a good fit?
- Work with many steps, hand-offs, and exceptions: invoices and collections, onboarding and compliance, client intake and document collection, quoting, recruiting follow-up, and service tickets.