Who can see what?
A system that retrieves across the whole company will eventually show someone a document they shouldn't see. So security says no, and the pilot stays a pilot.
PRACTICE 02 · INTELLIGENCE : AI AUTOMATION
Get AI out of the pilot and into the operating model.
Most AI projects stall between demo and production. The model works. What's missing is who can see what, who approves what, and how anyone knows it's paying off. We build that layer.
WHY PILOTS STALL
The model is rarely the problem.
Most people assume AI projects stall on capability. In practice they stall on three questions nobody designed answers for.
Who answers for it?
When an agent sends, signs, approves, or spends, someone is accountable. If that person isn't defined, legal says no.
Is it working?
Without a baseline and a measure, nobody can prove the system saves time or money. So finance says no.
Answer those three, and production stops being a debate.
WHEN PEOPLE CALL
Usually it's one of these.
The pilot worked, and nothing happened next.
The demo impressed people. Six months later it's still a demo.
Security or legal keeps saying no.
Usually with good reason: nobody can show what the system sees, or who's responsible when it acts.
Your agents are about to act, not just answer.
Sending email, updating records, approving spend. The stakes changed, and the controls haven't.
Nobody can say what AI is saving you.
Lots of tools, lots of usage, and no number you'd put in front of a board.
Recognize one of these? Book a call
DELIVERABLES
Three layers between a model and production.
01 /
Workflow economics
WHAT IT IS
A map of where work actually happens, which steps AI should take on, and a baseline for each one, so value is measured, not estimated.
WHAT IT PREVENTS
Automating the wrong thing, then being unable to prove anything paid off.
02 /
Permission-aware knowledge systems
WHAT IT IS
Retrieval over your private data that respects the access rights people already have, connected to the systems where work gets done.
WHAT IT PREVENTS
The assistant that shows an intern the board deck.
03 /
Authority, approval & monitoring
WHAT IT IS
Defined limits on what each agent can do, human approval where decisions are consequential, and monitoring that catches drift after launch.
WHAT IT PREVENTS
An agent acting outside its authority, with no record of why.
DELIVERY
Clarity at every gate.
Nothing reaches production until you've approved how it behaves, what it sees, and how it will be measured.
01
Map
We document the workflows, data, and decisions in scope, set a baseline for each, and rank candidates by value and risk.
APPROVAL GATE
You approve the shortlist and the success measures.
02
Build
We connect the system to your data and tools, with permissions, approval steps, and logging designed in from the start.
APPROVAL GATE
You approve its behavior on your own test cases before it touches live work.
03
Run
A controlled rollout with monitoring live from day one, then a readout against the original baseline.
APPROVAL GATE
You decide whether to scale, adjust, or stop, based on numbers.
Guardrails, built in.
Each system sees only the data its users are already allowed to see, keeps nothing longer than it needs, and sends consequential decisions to a human. Every model evaluation and approval is logged, so you can show an auditor what happened and why.
ONE ARCHITECTURE
An agent's authority has to come from somewhere.
Legal Engineering
Governance defines which entity an agent acts for and what it's authorized to commit to. We design that before we write the first workflow.
RWA Tokenization
Automated compliance checks, investor eligibility, and reporting run on the same permission and audit model.
Capital Formation
A measured, auditable AI operation is an asset in diligence. An unexplained one is a risk.
Built on your infrastructure, not ours. Private retrieval, model gateways, workflow orchestration, and observability, deployed inside your environment.
WORK
We build the controls we recommend.
Agent compliance middleware
Open-source middleware that checks every agent action against encoded corporate-law rules before it executes, and writes each decision to a hash-chained evidence log.
Book a call about a similar systemThis platform
Our client portal keeps consultation records private to the signed-in account. Booking confirmations are verified with Cal.com before they appear in the portal. Agreement signing and payment remain demonstrations.
Book a call about a similar systemAgentCorp
Gives AI agents their own Delaware Series LLC, so authority and liability have a legal home before the agent acts.
Book a call about a similar systemGoverned intelligence operations
A private knowledge system for regulated operations, combining permission-aware retrieval with human approval gates. A representative design, not a reported client outcome.
Book a call about a similar systemCommon questions
YOUR NEXT INFLECTION POINT
Your pilot proved it can work. Now prove it's working.
Thirty minutes is usually enough to find what's keeping it out of production.
Book a structuring call Founder-led · Free · No obligation