How we work
Agents build your deployment. People stand behind it.
From the first conversation to live operations, the pattern is the same: AI does the work, your experts and ours review what ships, and you own what gets built.
01Where you start
Kate is the front door to building your organization as code
In Console, you ask Kate for work in plain language. She answers what she can and hands the rest to the right specialist agent. Its questions and results come back to you in the same conversation.
Asking for a new workflow works the same way. You describe it in your own words, and what gets built becomes part of your organization as code. It is versioned like any code, and your people review every change.
From request to live
- Kate hands the request to the AI forward-deployed engineer
- It writes a one-page brief of what it will build and which steps need your sign-off
- Once you approve the brief, it proposes the change for review and test-runs it on your real records first
- Nothing goes live without a person's approval
Before you sign
See the speed first.
We build a custom workflow demo on sample data so you can watch an agent do the actual work in minutes. No data of yours required to start, and nothing touches your systems until you decide to move forward.
Book a demo02The build
Meet your AI forward-deployed engineer (AI FDE)
When you sign, a crew of agents goes to work on your engagement: discovery over your SOPs and data, drafts of your operating model, a benchmark calibrated from your real scenarios, and the coordination in between. Everything they produce is a draft for human review. Nothing reaches your deployment unsigned.
Discovery
Read the operation.
Agents work through your SOPs, transcripts, spreadsheets, and exports, and turn what they find into typed proposals for your operating model.
agents do the workCalibration
Grade the work.
An eval agent builds the benchmark from your real scenarios, calibrates it, and watches for drift once you are live.
agents calibrate · humans labelCoordination
Keep it moving.
A conversational agent works alongside our engineers on your engagement, keeping the build moving and the people informed.
agents assist · humans directThe write gate
One way in.
There is exactly one path that writes to your deployment, and everything on it lands as a pull request (PR) that a person reviews and signs.
humans approveBuild
Agents draft the operating model, scaffold the benchmark, and open the PRs. Every artifact is a draft for a person to review. You are not receiving autonomous output yet.
Gate: the operating model is merged, the first benchmark cases pass five runs out of five, and the test plan is signed off by your team and ours.
Evals
Your team runs the system against your real workflows, with daily check-ins. The agents shift from authoring to validating, and every miss you flag becomes a benchmark case before it can recur.
Gate: every scenario on the signed-off list passes, and you give the explicit go-live sign-off.
Live
Agents make real decisions in production. The engagement shifts to stewardship: drift watch, anomaly response, and a weekly cadence on operational health.
Each stage ends on evidence, not on a date. The benchmark discipline behind the gates is OrgBench.
03Your experts hold the pen
Your operating model has an IDE
The operating model is not locked inside our engineering team. It has its own IDE, and the engine that validates your edits live in the browser is the same engine that enforces the model in production. What is valid in the editor is what runs. Edits gather into a change set you can diff line by line, try on staging, and ship as a pull request, with version history and snapshot rollback underneath. It is included in every engagement.
You can also just describe the change. The IDE agent turns plain language into a drafted edit, the way a coding assistant drafts code, and its proposal clears exactly the same validation, review, and gates a person's edit does. Your experts hold the pen either way: nothing ships unreviewed.
If a duty refund claim sits unactioned for five days, escalate it to a supervisor.
escalation_policy
-"on_timeout": "none"
+"on_timeout": "notify_supervisor"
+"timeout_days": 5
04Throughout
A forward deployed engineer owns your engagement
Every deployment includes a forward deployed engineer who builds your operating model, configures your integrations, and creates your workflows. Not self-serve-and-pray. We are committed to making it work for your team. Your data stays yours, and it is never used to train models for anyone else.
The AI FDE works alongside that engineer from day one, in the tools you already use: automating the drafts and validating the output, while the engineer reviews what ships and answers for the result.
Dana Reyes 9:02 AM
We keep overpaying carriers, a few lanes bill above our contracted rates. Can the agents catch these before we pay?
Authentica FDE APP 9:02 AM
On it. I'll pull the last 90 days of carrier invoices, check each line against your rate cards, and flag the overcharges. Want me to draft the audit check too?
Dana Reyes 9:04 AM
Yes please, and make sure nothing gets disputed without us signing off.
Authentica FDE APP 9:08 AM
Done. I added a freight rate check to your operating model and wrote gold cases from your real invoices. It's in staging as a PR, gated so no dispute goes to a carrier until your team approves.
PR · add freight_rate_checkstaging · 12 gold cases passing · needs your review ✅ 2🎯 1
One workflow, one measurable result, then you decide.
We scope tight on purpose. If we do not earn workflow two, we do not get it. Start with a workflow demo on sample data and see an agent do your kind of work first.