AI-native software studio

I don't pitch software.
I arrive with it running.

I build production applications, automation systems, and AI workflows — engineered by orchestrated fleets of AI agents under one operator's command. Research that takes a consultancy six weeks lands here in an afternoon, sourced and audited.

Fleet lattice 4 LAYERS · 400 NODES
Layers 4 Nodes 10 × 10 Active Mode PARALLEL
6 appsdesigned, built, and shipped to real devices
Hundredsof specialized agents coordinated in a single engagement
14 lanesof parallel research run simultaneously, then audited
23 yearsmilitary operations and logistics command experience

Capabilities

Four disciplines, one operating system.

Every engagement runs on the same machine: agent fleets doing the volume work, one operator deciding what matters, and a verification pass before anything reaches you.

AI development

Assistants that answer from your documents and cite the source — and say “not on record” instead of guessing. Retrieval systems, document intelligence, and model integration built to survive an expert poking at them.

  • Residual quantization over binary vectors
  • Grounded retrieval with source citation
  • Document + photo understanding
  • Never-guess answer contracts

App development

Native iOS and Android from one codebase, shipped through TestFlight to real devices — offline-capable, biometric-secured, and fast enough that a plant manager understands it in thirty seconds.

  • React Native / Expo, iOS + Android
  • Offline-first, voice, camera, biometrics
  • TestFlight and store delivery

Workflow automation

The paper, phone calls, and spreadsheets between two systems that nobody owns. I map the real process — not the one on the org chart — and automate the part that costs hours every day.

  • Process mapping and redesign
  • Integration in front of existing ERP
  • Field capture replacing paper

Agent orchestration

Fleets of specialized agents working in parallel lanes on one problem — research, code, design, verification — with an auditor that rejects any claim without a source before it reaches a human.

  • Parallel lane architecture
  • Adversarial verification pass
  • Scales from a handful to hundreds

Verified intelligence

Market, competitor, and operational research where every figure carries a source and a date. Anything unsourced is labeled an estimate or struck. I never hand you a number I can't defend.

  • Sourced-or-struck standard
  • Conflicts surfaced, not smoothed
  • Explicit unknowns section

Working demos

Most engagements start with software already built. I study the operation, find the problem worth solving, and put a functioning app in your hand before the first invoice exists.

  • Problem identified from evidence
  • Demo built on public data
  • Production scope follows proof

Method

How hundreds of agents stay honest.

Scale is easy. Trustworthy scale is the hard part. Every engagement I run moves through the same five stages, in this order, because the order is what keeps volume from turning into noise.

STAGE 01

Dispatch

The problem is decomposed into independent lanes. Each agent gets one domain, explicit rules, and a hard boundary against the others.

STAGE 02

Parallel run

Lanes execute simultaneously — research, code, design, data. Work that would run for weeks in sequence finishes in a single sitting.

STAGE 03

Audit

A dedicated agent researches nothing. It reviews everything: strikes unsourced claims, marks conflicts unresolved rather than picking a winner.

STAGE 04

Build

Findings become software. Typechecked, device-tested, bundled — a working artifact, not a specification document.

STAGE 05

Deliver

You get the app in your hand, the evidence file behind it, and a written list of what could not be confirmed.

Under the hood

Retrieval built for scale, not for demos.

An assistant is only as good as what it can find. Mine run on compressed vector indexes — residual quantization layered over binary embeddings — so a search across decades of manuals, drawings, and service records returns in milliseconds on hardware you already own, and keeps returning when the corpus grows by an order of magnitude.

The compression is the reason it stays fast. The citation contract is the reason you can trust what comes back.

Index Residual quantization over binary vectors — coarse codebook plus residual refinement, Hamming-distance first pass
Footprint Order-of-magnitude smaller than dense float indexes — large corpora fit in memory instead of a cluster
Retrieval Grounded generation with mandatory source attribution and an explicit “not on record” path
Deployment On-device, on-premise, or hosted — offline capable where the work happens without signal
Verification Adversarial audit pass before any output reaches a human decision-maker

Engagement pattern

From cold start to working app.

A 118-employee manufacturer, five acquired brands, one week.

No introduction, no brief, no access to their systems. Fourteen research agents swept the company in parallel — products, operations, workforce, technology, leadership, regulatory environment — while an auditor struck every claim that arrived without a source and flagged one entire source as unreliable.

The evidence pointed at one problem: an aftermarket parts business running on a phone line, a fifteen-year-old catalog, and the memory of employees who were leaving. Two applications were designed, built, and device-tested against it — a knowledge engine holding every machine the company ever built, and a configure-price-quote tool priced from their own published contract data.

Elapsed time from first search to working software on a phone: seven days.

42machine models recovered and catalogued, including lines absent from the company's own website
13verified factory list prices located in public contract records
0figures published without a source and access date

Products

Built here. Running now.

Two products born out of that engagement, each deployable to any equipment manufacturer and white-labeled under your brand.

ShopVault Proof of concept Buildsheet ▸
All Aquatech X-Vac O'Brien
Aquatech
B-10
Combination sewer cleaner
Current
X-Vac
X-15
Hydro excavator
Current
X-Vac
X-9
Manual-documented only
Legacy
UltraVac
Stationary loaders
Folded into successor line
Legacy
MachinesAskCapture
● Shipping

ShopVault

Every machine your company ever built — current lines and models discontinued decades ago — in one searchable registry. An assistant answers from your documents and cites them. Voice capture records what your veterans know before they retire.

BuildSheet Proof of concept ◂ Shopvault
Build & quote — instant pricing
Base unit
Aquatech B-10
$338,432
Add-ons
Wireless remote$5,800
Debris tank vibrator$3,890
Front reel + 180° boom$51,600
Hydraulic pump-off$5,375
Quote total
$348,122
Share quote
● Shipping

BuildSheet

Your rep configures the unit standing beside the customer. Tap the add-ons, watch the total move, share an itemized quote from the driveway. A week-long quote cycle collapses into a conversation.

How it starts

You see working software before you spend anything.

No discovery retainer, no six-week assessment, no statement of work written before anyone understands the problem. The order is deliberately backwards from how consultancies do it.

01 — Study

I research your operation

Agent fleets sweep your market, competitors, published records, and public documents. You get a sourced report naming the problems the evidence actually supports — ranked, with confidence levels and an explicit list of what could not be confirmed.

Days, not weeks. No cost to you.
02 — Prove

I build the demo

You pick the problem worth solving. I build a working application against it and put it in your hand — running on a phone, not sketched on a slide. You poke at it, break it, and decide whether it's real.

Free. That's the whole pitch.
03 — Build

We scope the real thing

Your data loaded, your brand on it, your systems connected, your team on it through TestFlight or the stores. Fixed-scope project or retained capacity — whichever fits how you buy.

This is the part you pay for.

Who this is for

Operations where the knowledge lives in people.

The pattern repeats across industries: decades of institutional knowledge, a workforce turning over, and critical processes still running on paper, phone calls, and spreadsheets nobody owns.

Equipment manufacturers — legacy model support, parts & service Industrial & fabrication — shop-floor knowledge capture Distribution & fleets — proof of delivery, driver workflows Utilities & co-ops — field crews, outage & meter operations Construction & heavy civil — daily reports, equipment logs Dealer groups — service status, customer communication Logistics & cross-border — shipment visibility, dock scheduling Agriculture & processing — grower portals, scale tickets
23 years, United States Air ForceRetired Master Sergeant
Space Systems CommandLogistics Management Specialist — launch and test range systems
Six shipped mobile applicationsDelivered to real devices through AI-assisted development

Who you're working with

An operational architect, not another dev shop.

Twenty-three years of military logistics teaches you to read a broken process the way other people read a sentence. Launch and range operations don't forgive hand-waving: every part has a source, every claim has a record, and the mission either works or it doesn't.

That discipline is what makes agent fleets safe to deploy at scale. The agents produce the volume. I decide what's true, what matters, and what gets built. Your problem is process pain — and process pain is my native language.

Start here

Show me the pain point.
I'll show up with the fix.

Every engagement opens the same way: I study the operation, find the problem the evidence actually supports, and put working software in your hand. Not a proposal, not a deck — something you can hold and break. You'll know within days whether it's worth going further.

SHADOW.N.PROCESSOR@GMAIL.COM