Stationed deployment work in context

// AI DEPLOYMENT FIRM

// BUILDWHATMATTERS[FASTER]

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EMBEDDED ENGINEERSWORKING SYSTEMSIN THE REAL WORKFLOWCAPABILITY TRANSFERDAYS, NOT QUARTERS

// WHERE OUR SYSTEMS LAND

Where our systems land

GTM & pipeline
Internal ops
Vendor & supplier data
Hiring & evaluation
Agent infrastructure
Data & enrichment pipelines
Support workflows
Reporting

AI doesn't fail because the models are bad. It fails because it never reaches the work.

Position gap in deployment work

The gap is rarely model quality. It is ownership, data paths, and whether the system has a place in the work people already do.

No owner after launch
No path to the real data
No place in the existing workflow
Nobody's job to keep it alive
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The problems we go after

Agent-run go-to-market experiments across a portfolio problem image

VENTURE STUDIO / FDE EMBED

Agent-run go-to-market experiments across a portfolio

Agent-run go-to-market experiments

Turning a manual vendor-sourcing process into a searchable system problem image

CONSUMER / SYSTEM BUILD

Turning a manual vendor-sourcing process into a searchable system

Searchable vendor-sourcing system

Hiring pipeline for AI-native engineers, run as a product problem image

INTERNAL / EVALUATION SYSTEM

Hiring pipeline for AI-native engineers, run as a product

Roles-and-applications pipeline

Getting an ops team off a stack it couldn't control problem image

OPERATIONS / PLATFORM MIGRATION

Getting an ops team off a stack it couldn't control

Migration onto owned infrastructure

What an embedded team does differently

Deployment, not advice, is the unit of work: systems only matter when they survive contact with the work.

01

We work inside your stack

Same repos, same data, same review process.

02

We build the boring layer

Enrichment, permissions, scheduling, evaluation; the parts that decide whether the interesting part works.

03

We hand back the keys

The system is owned, documented, and operated by your team when we leave.

AI doesn't fail because the models are bad. It fails because it never reaches the work.

// STATIONED / POINT OF VIEW

What we actually do

See what we've deployed

A focused build against one workflow, with a working system in the environment where the work happens.

embedbuildland
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Agentic systems with the permissions, review steps, and failure behavior required for real operations.

agentsreviewship
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The structured data layer that makes useful automation possible and keeps it current.

sourcesenrichroute
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Evidence-based evaluation for judgment-heavy processes where a demo is not a result.

baselinescoredecide
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Build days and training where your team ships against its own workflows and stack.

learnmakeown
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Move working automations onto infrastructure your team controls and can extend.

inventorymigrateoperate
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Services system in context
Structure method in context

A method, not a pilot

The work has a shape because the handoff is part of the build. Five stages keep the team close to the workflow and make ownership visible from the first week.

5STAGES FROM EMBED TO TRANSFER
1WORKFLOW AT A TIME

// DEPLOYMENTS / SYSTEMS IN THE WORK

Systems that earned their place

Agent-run go-to-market experiments across a portfolio case image

VENTURE STUDIO / FDE EMBED

Agent-run go-to-market experiments across a portfolio

Problem: Founders had channel ideas but no way to run enough experiments to find out which ones worked, and no shared infrastructure between companies.

What we built: A GTM system that handled the mechanical parts of an experiment end to end — audience definition, data collection, enrichment, scoring, message generation, and tracking — so a founder could go from a hypothesis to a live test the same week.

WHAT CHANGED

The unit of work moved from campaign to experiment: a founder could take a hypothesis to a live test in the same week, and what survived became shared infrastructure instead of one person's process.

WHAT WE LEARNED

Most of the value wasn't the generation step everyone expects. It was the enrichment and scoring underneath it — the boring part decided whether the interesting part worked.

View detail
Turning a manual vendor-sourcing process into a searchable system case image

CONSUMER / SYSTEM BUILD

Turning a manual vendor-sourcing process into a searchable system

Problem: The knowledge existed but wasn't queryable, so the same research happened repeatedly and new team members couldn't self-serve.

What we built: A structured directory with the sourcing and enrichment pipeline behind it, in the place the team already worked, so adding a vendor was a side effect of normal work rather than an extra chore.

WHAT CHANGED

Vendor questions became a lookup instead of a research project.

WHAT WE LEARNED

When a workflow depends on one person's memory, the deployment risk isn't the model — it's whether the system captures knowledge as a byproduct of work people already do.

View detail
Hiring pipeline for AI-native engineers, run as a product case image

INTERNAL / EVALUATION SYSTEM

Hiring pipeline for AI-native engineers, run as a product

Problem: Resumes are a weak signal for this, and "AI-heavy" resumes are now trivially easy to generate.

What we built: A roles-and-applications pipeline wired to our own operating stack, plus a structured evaluation approach that scores real work instead of claims — including interviews where candidates are expected to use AI.

WHAT CHANGED

Evaluation moved from impression to evidence, and the same infrastructure now supports client hiring conversations.

WHAT WE LEARNED

Every internal system we build is a rehearsal for a client deployment. This one became a reusable pattern for evaluating any judgment-heavy process.

View detail
Getting an ops team off a stack it couldn't control case image

OPERATIONS / PLATFORM MIGRATION

Getting an ops team off a stack it couldn't control

Problem: The workflows worked but were locked in — no ownership of the data, no way to test changes safely, no path to extend them.

What we built: A full inventory of what the platform was doing, an exporter that preserved fidelity, and a migration onto infrastructure the team owns, with the same workflows running.

WHAT CHANGED

The team kept its automations and gained the ability to change them.

WHAT WE LEARNED

"Owning your data" sounds like a strategy slide until an outage or a pricing change makes it an operational one.

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01 / 04

How a deployment actually runs

Sit with the people doing the job and find the workflow worth attacking first.

Read the full method
01Embed
02Build
03Land
04Prove
05Transfer

// NEXT DEPLOYMENT

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+Have a workflow that should already be automated?Start there↗Want to see how we deploy?Read the method