One operator. One AI team. Infinite solutions.
Wolfberg LLC is an AI operating-model company.
In 38 days, the model built the company itself — along with a cloud-native infrastructure, a team of AI employees, an AI pipeline that refactors legacy code to cloud-native, and a brain that carries context as code across every session. All proof the AI operating-model delivers.
Work with us and you're engaging an operator who runs an AI team on your highest-stakes problems, not a consultancy renting you hours. The operating model is a discipline, a human conductor plus a brain that carries context as code, and it's the one part the market can't sell you. So we do better than run it for you: we teach you to run it yourself, and to train the next ones.
Twenty-seven years of building digital infrastructure for defense, intelligence, and commercial enterprises is the root the discipline grew from. AI is the substrate it runs on today, utilizing modern cloud services architectures.
Live numbers · last compiled 2026-06-10
One operator. 183 active hours.
What the discipline produces when one operator runs it. Not a team of fifty. One conductor and a brain.
89,362
lines of code
Keystone, Refactory, the brain, this site.
1,600
pages of documentation
Operator runbooks, architecture references, AI employee specs.
150
memory files
Three Claude instances sharing persistent context.
786
commits
Branches keep landing. Main moves every day.
One brain to rule them all
The operating model, made visible
This isn't a diagram of how Wolfberg works. It's the working discipline itself, mapped: the operator, the AI employees, the shared-context brain they think in, the doctrine and work orders that route the work. The wiring is the discipline, how each part references the others so the whole coheres. Every vendor will sell you a node on this graph. Nobody sells you the wiring. That's what we teach. Drag a node, watch what fires. This is what Wolfberg runs on right now.
Archetypal shape, live count. Wolfberg's brain holds 150 memory files today, and grows nightly as sessions land.
One pattern, four places
The same loop, everywhere it runs.
Someone directs the work. A specialist does it. Someone else checks it independently. The result is signed off and becomes the record everything downstream runs on. Then it repeats: in how the company runs, in the pipeline we sell, in the runtime we deploy, and in what we teach. The loop is the discipline. Learn the loop and you can point it at anything.
Loop 1
The Company — how Wolfberg runs
Loop 2
Refactory — what Wolfberg sells
Loop 3
Keystone — what Wolfberg deploys
Loop 4
Curriculum — what Wolfberg teaches
Click any loop to enlarge
The Thesis
You're not ready for what's coming.
Disrupt your perspective.
Most answers fail in the same shape.
Bolt a familiar abstraction onto a new substrate. Cloud is just a hypervisor and storage. AI is just chatbots and a model. The substrate is different; the answer treats it as the same.
Lift-and-shift was never cloud-native — it was someone else's data center with a markup. Chatbots dropped into legacy workflows aren't AI-native either — just a faster autocomplete bolted onto a system that wasn't designed to operate this way.
The substrate doesn't care. It rewards the work designed for it.
Why now
Watch the field right now. Everyone is selling a piece of the operating model: the memory layer, the orchestration framework, the governance control plane, the agentic client that owns your context. Every one of them hands the same hard part back to you, the integrating discipline that makes the pieces cohere, run by a human who is accountable for the result.
That discipline is the only thing not for sale. So we don't sell you a part. We teach you to run the whole.
One conviction
Infrastructure is the game — and almost nobody's watching it.
Every choice you make about digital infrastructure shapes everything built on top of it. And nobody notices infrastructure until it breaks — same as the physical world. Nobody thinks about the power grid until the lights go out, the water main until it bursts, the bridge until it falls. Digital infrastructure is no different: invisible right up until it's the only thing anyone's talking about — and by then you're not improving it, you're surviving it.
Twenty-seven years taught me one thing above the rest: build it right the first time and the whole game changes — not incrementally, categorically. The cost curve bends. It scales without heroics. It doesn't fall over at 2am. And you stop paying the compounding tax on a foundation that was wrong from the start — because most of the cost of bad infrastructure isn't the infrastructure, it's everything downstream that inherited the mistake, usually without ever knowing that's what it's paying for.
Two myths keep people bolting an old foundation onto new ground and calling it modern:
- › "Vendor lock-in" is just the result of making a decision and sticking to it. You don't buy a Toyota, a Nissan, and a Ford at once to avoid being locked into one.
- › "Cloud agnostic" sounds like prudence — but can run anywhere and needs to run anywhere are different things.
Combine them and you get a cloud built on top of the cloud to pretend you're not using the cloud — costing more than the on-prem it replaced. Cool soundbite. Multiplied time, scope, and cost. The tell is legacy patterns in cloud clothing:
- ✕ EC2 instances pretending to be servers
- ✕ RDS pretending to be Oracle
- ✕ Kubernetes running 24/7 for workloads with eight hours of traffic
- ✕ Always-on compute for event-driven work
- ✕ SaaS pricing for software you should own outright
None of this is hard, and none of it is new. Every service I build on has been generally available for years — Lambda since 2014, DynamoDB on-demand since 2018, Step Functions since 2016. There's no secret. It's just AWS used the way it was designed to be used.
Keystone is what building it right looks like: serverless by construction, idle-cost-zero, cloud-native correct on day one. See the platform →
Point the operating model at the infrastructure problem and it produced Keystone. Point it at legacy modernization and it produced Refactory. The products aren't the pitch — they're proof the model does what it claims.
The Evidence
Most still aren't using cloud — and now AI — correctly.
- › Not at scale, where the budgets should make it pay off.
- › Not the consultancies billing $400/hr to migrate.
- › Not the SaaS vendors charging $2,000/mo for what should cost $20.
29% of cloud spend is wasted in 2026, and 84% of organizations can't say where it's going — reversing five years of progress. The cause is lift-and-shift: refactored apps run 25–30% lower TCO, lifted ones run 15–20% higher, and 38% of migrations still take that path — inheriting a debt that compounds.
Same script, one layer up.
- › Not the labs racing the next frontier model.
- › Not the consultancies billing the same $400/hr to "AI-enable" your codebase.
- › Not the SaaS bolting a sidebar chatbot at $200/seat/mo onto a model call that costs three cents.
Fewer than 3 in 10 organizations see significant ROI from gen AI, just 23% from agents, and 81% hit production failures from AI-generated code. The wrapper fixes none of it — 77% of AI failures trace to strategy and governance, not the model.
Architecture that matters at a million users can be built correctly on day one. Almost nobody does — and at the pilot stage it's worse: 95% of enterprise AI pilots ship zero measurable return. The model isn't the problem. The substrate around it is — Context as Code, process discipline, cloud-native infrastructure, cross-instance protocols, cadence. The whole operating model has to compound from day one. Almost nobody thinks about it, let alone engineers all of it.
These aren't model failures. They're discipline failures, and the integrating discipline is the part nobody is selling. That's the gap we teach you to close.
Sources →
Sources · Cloud · Flexera 2026 State of the Cloud (29% wasted, 84% struggle managing, 76% >$5M/mo) · Auvik Cloud Migration 2026 (38% lift-and-shift) · Datastackhub Cloud TCO 2025–2026 (25–30% lower refactored, 15–20% higher lift-and-shift)
Sources · AI · MIT NANDA State of AI in Business, July 2025 (95% pilot zero-return) · Deloitte State of AI in Enterprise 2026 (29% gen AI ROI, 23% agents ROI, 79% face challenges) · GlobeNewswire, May 19 2026 (81% prod failures from AI-generated code) · Gartner April 2026 / RAND 2025 (28% AI infra deliver promised return; 80% fail to deliver value) · Folio3 analysis of 140 implementations (77% non-model failures) · Dharmadhikari, April 2026 (wrapper SaaS pricing)
The Solutions
The economics of enterprise IT changed by an order of magnitude. Most operating models haven't caught up.
Legacy modernization — the most expensive line item in enterprise IT, the one that resisted compression for thirty years — just moved by orders of magnitude. Refactory's first pilot ported 1,003 lines of production Java to a cloud-native target topology for $3.46 in Claude API spend and 2.9 hours of operator oversight. Traditional consulting quotes the same scope at nine to fourteen thousand dollars. And the Verifier instance caught two structural defects the Migrator missed — engineering quality isn't the trade.
The question is whether the operating model your company runs on has absorbed that shift. Most haven't — still pricing engineering the way it was priced before AI got competent at it, still treating modernization as a quarter-by-quarter line item instead of a problem that finally yields.
Wolfberg's has. We run on it, we teach it, and we ship the engineering it produces.
The receipts
This is what the discipline produces, and what you'd learn to reproduce.
Keystone is evidence, not the offer. The AI-employee operating system the discipline produced in 41 hours, on $40 of API, when one operator pointed it at engineering work. Built and load-tested. The numbers below are not projections. They're what running this way looks like.
41
hours
Built solo. Production-deployed.
$40
in API costs
Total Claude spend across the build.
7
AI roles, not people
Seven roles, running as software on AWS. Not seven hires.
1M
users
What the architecture scales to with 4 code changes.
108
AWS resources (baseline)
Baseline Keystone. What a fresh operator's first apply ships. Production today: 151 after about five weeks of feature work.
100
seconds to deploy
From terraform apply to a working /health on a clean AWS account. The DeployReplay below is the captured run.
$0.20
per tenant
New tenant spin-up cost on a fresh account. About twenty cents.
2
operator instances live
wolfberg-pm and wolfberg-llc running on the same substrate today. Multi-tenant by construction.
173 requests per second sustained at p95 317ms is roughly the load profile of a mid-market SaaS at peak. Most platforms hit that with auto-scaling clusters and a 24/7 SRE team.
Keystone holds it with a single Lambda concurrency setting. No warm pool. No failures.
At 100,000 users, Keystone runs $10K/month. Salesforce Service Cloud + Agentforce at the same scale: $29M/month. ServiceNow Pro Plus with AI: $13M/month. Same workflows. Different planet. Idle cost is literally zero — pay only for invocations. And building it conventionally — a 3–4 engineer team over 4–6 months — would cost $250K–$500K loaded. This cost $40 in API.
Clean teardown — no orphan IAM, no dangling network, no cost leak. The only artifact is the AWS-mandated KMS 30-day pending-delete window.
That's the discipline the Curriculum teaches you to reproduce.
Anatomy of a 100.6s up + 60.7s down deploy
Watch 108 AWS resources come up — and back down.
A real terraform apply followed by terraform destroy against a clean AWS account. 108 is the baseline Keystone, what comes up on a fresh operator's first apply. Every line below was captured from the actual run. Nothing is reenacted.
And again, under recovery — on 2026-05-19, a destroy-script category error began emptying a stack-managed S3 bucket holding 37 business documents. The model caught the run mid-execution and recovered every file. Zero data lost. Zero lines of remediation code. Read the case study →
By surface
Where the work lives.
The operating model produces output across four surfaces — two products, the operator brain that runs them, and the company itself. Numbers below are live where the producer can measure them; em-dashes where a metric does not apply or is not tracked today.
Product
Keystone
AI-employee operating system.
- Lines of code
- 42,870
- Pages of documentation
- 197
- Memory files
- 113
- Commits
- 190
- Active hours
- ~6
- API costs (known)
- $40 build
- Production AI roles
- 7
- User load ceiling
- 1M
Product
Refactory
Six AI agents. Legacy to cloud-native, on contract.
- Lines of code
- 18,525
- Pages of documentation
- 58
- Memory files
- —
- Commits
- 29
- Active hours
- ~7
- API costs (known)
- $3.46 pilot
- Production AI roles
- 6
- User load ceiling
- —
Operator brain
Capstone
The brain Wolfberg runs on.
- Lines of code
- —
- Pages of documentation
- 921
- Memory files
- 37
- Commits
- —
- Active hours
- ~98
- API costs (known)
- —
- Production AI roles
- 3
- User load ceiling
- —
The company
Wolfberg LLC
The company that runs on the model.
- Lines of code
- 27,967
- Pages of documentation
- 424
- Memory files
- —
- Commits
- 567
- Active hours
- ~3
- API costs (known)
- —
- Production AI roles
- 0
- User load ceiling
- —
Pre-attribution
Baseline hours
Operator time logged in the productivity table before per-session transcripts started carrying product attribution (cutover: 2026-05-05). Real work, attributable in the aggregate, not per surface — surfaced here so the math adds up.
~70 hrs
How these numbers are computed →
Live rows recompile nightly from session deltas, repos, and the brain. Static rows (API costs, Production AI roles, user load ceiling) are known receipts where they exist. Production AI roles counts AI agents scoped to that surface; Wolfberg LLC runs on Keystone + Capstone, so its AI workforce rolls up there rather than duplicating here. The Wolfberg LLC row counts site code, ops automation, and IaC for the company itself, not the Keystone runtime which is its own column. The four product tiles plus the pre-attribution baseline tile reconcile to the aggregate active-hours figure shown elsewhere on the page.
This didn't come from theory. See where it came from →
Or get the formal deck.
The machinery, named
How the work gets done.
The model has named components: one human, three AI instances, a shared context system, and session protocols that carry state across work sessions. Architecture is public; brain contents are private.
The conductor
Berg
Sets direction. Gates decisions. The human in the loop. The 27 years of pattern-recognition that produced the model in the first place.
Strategic synthesis
Capstone
AI brain. Frames decisions. Drafts directives. Holds the long-horizon view across sessions.
Engineering execution
Code
AI instance. Builds, deploys, executes against directives. The hands on the keyboard.
Visual execution
Design
AI instance. Brand, decks, visual artifacts. Parallel to Code, different surface.
Substrate
Shared context system
Lets the four working surfaces coordinate without stepping on each other. Memory files, canonical pages, the operator brain.
Substrate
Session protocols
Carry state from one work session to the next. Deltas, quick-loads, end-of-day consolidation. Continuity is engineered, not assumed.
The thing competitors can't copy isn't a component. It's how they work together, and that "how" is the discipline. We don't keep it behind glass. We teach it. Day 4 of the Curriculum is how to build this brain.
The brain at the center. See the brain Wolfberg runs on → · Learn to build your own →
What comes out of running this way
Wolfberg is the operating model.
The way of working is the asset. Senior Advisory is the substrate the model grew from. Curriculum is the model packaged for transfer. Refactory and Keystone are what falls out of the AI pipeline when the model is pointed at engineering work. The model is the differentiator; the products are evidence it works.
Click to enlarge
The front door is Senior Advisory: bring the operator and the AI team in on your highest-stakes work. In engagement form, the operating model answers four buyer questions.
The engagement
We work ourselves out of a job.
Most firms sell dependency: the longer you need them, the better they do. We sell the opposite. The flagship engagement is train-the-trainer, and it's done when your people can run the operating model without us and teach the next ones themselves. Senior Advisory is the guide. The whole arc runs inside it.
01 · Show
See it running.
We show you the model on the company it already runs, then map it to yours. No slideware. The thing itself.
02 · Deploy
Stood up on your cloud.
Your platform built as modern cloud architecture, a Keystone tenant on your own account. A phase of the work, not a license.
03 · Teach
Your people become the trainers.
The Curriculum installs the operating model in your team. They don't just learn to run it, they learn to teach the next ones.
04 · Refactory
Clear the legacy in the way.
Where old systems block the path, Refactory converts them to the cloud-native shape the rest of the arc runs on. It assists. It isn't the point.
You finish with operators who run the model and train the next ones. We're built to become unnecessary. That's the product.
Each piece also stands alone: a Senior Advisory engagement, a Keystone deploy, a Curriculum cohort, a Refactory pilot. Train-the-trainer is the flagship that bundles all four into one arc.
Why us
Where we sit. Why it's defensible.
Plenty of players claim AI-native operations. Almost nobody runs their own company on what they sell. The picture below shows where the rest of the market is — and where we sit.
Competitive positioning — scored against published criteria; illustrative, not measured. (Tier-1 judgment, Y-axis proxy-scored.)
X — vertical / operator-specific
- ›X1 — vertical product (not a horizontal tool)
- ›X2 — workflow / per-tenant / outcome pricing (not per-seat or hourly)
- ›X3 — operator go-to-market (sells to operators of a business, not builders)
Y — runs their own company on it
- ›Y1 — dogfoods with evidence (actually operates on the product, provable)
- ›Y2 — the product IS the operation (not a side demo)
- ›Y3 — operator-led (the people running it are operators, not just vendors)
Tier-1 = rubric-applied judgment, not measured data. Y-axis is proxy-scored (real internal ops are private). Tier-2 citations (2–3 per dot) are the planned fast-follow — when they ship, the appendix becomes externally auditable.
A competitor can copy the products. They can't copy the integrating discipline, and they can't acquire a 27-year defense, intelligence, and commercial trust network. The platforms sell you parts. We teach you the discipline that makes parts into a company, and we prove it by running ours on it. The differentiator isn't the software. It's the distance from the product to the operator, and our willingness to close it for you.
When to call
- › Your cloud bill scales linearly with your customer count
- › Your roadmap is measured in quarters because every change requires a re-platform
- › You're paying $130/user/mo for ServiceNow, $290/user/mo for Salesforce, or $400+/mo for vertical SaaS
- › Your team built it the way they knew, not the way the cloud was designed
- › Your infrastructure costs more idle than it does at peak
If that's you, there are two moves: bring us in on it, or learn to run it yourself. Either way, it starts with a conversation.
From colleagues across 27 years
"Berg is a fire-and-forget missile."
Doug Jones · Leidos colleague · SVP, Defense Sector CTO"Infrastructure has never been more critical than it is right now — anyone can ship an app in two hours, but the infrastructure underneath is what separates a demo from a business. Berg has been ahead of this for a decade. I'd trust him with anything critical."
Kevin Fogarty · Leidos colleague · SVP, Intel Sector CTO
Infrastructure was the first problem we pointed the model at. It won't be the last.
Let's talk.
DMs open. Email open. Always down for great convos over great wine.
Start the conversation