The system that runs your business is a decade old and cannot be paused. AI has to arrive beside it — reading it, writing to it, failing safely against it. That is the work we do.
We work at every layer under the model: the data engineering that feeds it, the system architecture it runs on, the agent harnessing that keeps it inside its job, and the security around software that acts on its own.
The model is bought. The four layers beneath it are built, and they are where a transformation succeeds or stalls.
A model that performs in a notebook and a model that survives Monday morning are different engineering problems. Four failures sit under the model, and a project hits them in this order.
The model is fine. The data is in six systems, three of them legacy, none of them contracted. No pipeline, no retrieval path, and no owner for what a field means. The pilot ran on an extract someone built by hand. Production cannot.
Ten questions a day and ten thousand are different systems. Throughput, p99 latency, capacity, cost per request and failure behavior get discovered after launch, on the customer.
An agent that calls tools is a new class of principal. Most estates hand it a service account and hope. No authority model, no least privilege on tool access, and no answer for a prompt that is itself the attack.
The system of record is a decade old, the business runs on it, and it cannot be paused. Everything above has to be true while it keeps running.
Most AI work fails at a seam between layers. A team that works at one layer hands you the seam.
Six steps, in the order they ship. Each one answers a failure above.
What runs the business today, what it costs, where the data actually lives, and what may not be touched.
You keep · an estate mapContracts, pipelines, retrieval and context. The data layer the system will read, built once and owned.
You keep · data contracts and a pipeline specThe target architecture beside the legacy one: services, boundaries, capacity, latency and cost budgets set before the build.
You keep · a migration architecture noteAgent design, tool and authority models, evaluation harnesses, and a human checkpoint anywhere the action is irreversible.
You keep · an authority model and an eval harnessLeast privilege on every tool, a threat model for software that acts, model provenance, and keys you hold.
You keep · a threat model and least-privilege grantsLoad-tested against real traffic, observability on, cost per decision known, and a rollback to the legacy path that stays available.
You keep · load-test results and a rollback planNothing is paused, and there is always a path back. That sentence is only true because of step six.
Each layer names a discipline you already have a budget line for.
Pipelines, data contracts, retrieval and context, and migration off legacy stores.
Buys it Head of Data
Agent design, tool and authority models, evaluation harnesses, human checkpoints.
Buys it VP Eng · Platform
The target architecture beside the legacy one: serving, capacity, latency budgets, cost per request, observability.
Buys it Platform · SRE · CTO
Threat models for systems that act, least privilege on tool access, identity for software, model provenance.
Buys it CISO · AppSec
Brand rules written as a machine-readable spec, with every asset generated from it.
Buys it Founder · CMO
The first four are engineering disciplines before they are AI disciplines. A data platform that has to hold ten times its traffic is the same work with the model left out.
You buy a running system in production, not a report and a headcount. The engagement ends with something deployed, load-tested and handed over, with the interfaces documented and your data exportable on demand.
In rank order. Cut from the bottom when the page is tight, never reordered. Each one is stated with its mechanism, because this market's claims are exhausted and only mechanisms still carry information.
Data engineering, system architecture, agent harnessing and security in one team. A transformation crosses all four, and every seam between them is where AI work fails.
Heavy traffic, heavy data, reliability written into a contract, and scale that arrives without warning. Capacity, latency and cost per request are budgeted before the build and load-tested before launch.
Least privilege on every tool an agent can reach, a threat model for software that acts, model provenance, and keys you hold.
Documented interfaces, portable data, standard stacks in your infrastructure. Replace us and everything keeps running.
The name is not decoration. Open interfaces, portable data, standard stacks in your infrastructure, and an export guarantee in the engagement. Replace us and everything keeps running.
Heavy traffic, heavy data, reliability written into a contract, and scale that arrives without warning. Those four are why the work underneath is the same and the expensive decision is different in each one.
Volume, correctness and audit pressure at the same time. The expensive decision is what an automated system may do without a person, on anything that moves money.
Retail, POS and syndicated sources, each with its own definition of the same word. The expensive decision is which definition wins, and who owns it.
Data and regulation, and a system of record that is not going anywhere. The expensive decision is where the boundary sits, and whether a compliance team can read it.
Catalog scale, seasonal peaks, and a latency number that converts into revenue. The expensive decision is what happens when the peak is larger than the forecast.
Documents, and revenue measured in hours. The expensive decision is which half of the work is repeatable, and who signs the output.
Vertical SaaS, entertainment and media, and regulated or data-heavy operations. The same four properties underneath, and the same transformation work.
This market's claims are exhausted, so we publish mechanisms instead. What follows is the whole of it, including the parts that are not flattering.
We are not a frontier lab. The model layer is bought, and often it should be.
Every performance figure we could put on this page would be a guess, so there are none. We publish the load-test methodology first, and results only from your system.
So there are no logos and no case studies here. We will walk you through an architecture instead, which tells you more than a testimonial.
SOC 2, PCI and GDPR are design targets. No audit is complete, so we write aligned and never certified. EU AI Act work is readiness.
If an action is irreversible, a person is on it, and we will tell you which person.
Nothing is paused is a sentence about step six, not a slogan.
Nothing is paused. The new architecture runs beside the old one and takes one workflow at a time. Step six of the path leaves a rollback to the legacy route in place, and it keeps working after cutover.
They are five layers of one system, not five businesses. We start with the layer you called about, and the work tells us which adjacent one comes next.
Most work at one layer and hand you the seam. The failures are at the seams: data that was never contracted, an architecture that was never sized, an agent holding a service account.
Throughput, p99 latency, capacity and cost per request are budgeted before the build and load-tested before launch. We will show you the method. We will not quote you a number we have not measured on your system.
It will. Anything irreversible sits behind a named human checkpoint, every tool grant is least privilege, and a run can be replayed from its inputs.
Not published ones. We do not put a client's name on a page without written permission, and none is on file. We will walk you through the architecture instead.
No. Products are commercially licensed and client work is under contract. Open describes how the systems behave: documented interfaces, portable data, export on demand.
Standard stacks in your infrastructure, documented interfaces, and an export guarantee written into the engagement.
Often the right people for the estate. Ask them for the authority model on the agent and the cost per request at peak.
Sometimes, but it is usually the wrong project. The ones that work start from a decision you are already making by hand, repeatedly, with a cost attached when it is wrong. Name one of those and we will tell you whether it is worth automating.
Describe what is stuck: the workflow, the system it runs against, and what happens if it stops. We will come back with an architecture, a timeline and the first commit.