For Enterprise Teams Already Running AI
Every prompt you send
is a signal about
where value lives
in your business.
Claude, Copilot, and GPT see what your teams are working on. The frontier labs have a structural incentive to follow that signal. The companies winning the next decade are not the ones who deployed AI first. They are the ones who built the operating layer they own.
89%
Of enterprise AI spend goes to closed models. Open-source alternatives run 1.4x faster and cost 40% less. Locally, 100x less. That gap is a CFO problem.
30 days
From assessment to first owned AI system running in production on your infrastructure. Not a vendor platform. Yours.
100%
Of code, data, model weights, and infrastructure owned by you. Cancel anytime. The system keeps running. Your competitor cannot follow you there.
The Real Risk
You are not just using AI.
You are training your competition.
The frontier labs see which verticals their customers are building in. They see the use cases generating the most value. They have a product roadmap and a structural incentive to follow that signal into your market. This is not paranoia. It already happened.
It Already Happened
Claude Code launched after Cursor demonstrated traction on Anthropic's platform. Claude Design launched after Figma showed the value of AI-assisted design. The pattern is consistent: watch where value compounds, then move there.
Every prompt your team sends to a frontier model is a data point about where value lives in your operation. The enterprises building sovereign AI infrastructure now are not doing it for ideological reasons. They are doing it because the alternative is funding their own disruption.
Exposure 01
Your operational data is your competitive moat. Right now it is leaving your environment.
Trade secrets, customer workflows, pricing logic, operational playbooks. Every API call passes through infrastructure you do not own, to a company that is actively building vertical products in your category. You are paying them to study you.
Exposure 02
The open-source arbitrage is real. Ignoring it is a governance failure.
Open-source models: 1.4x faster. 40% cheaper. Locally, 100x cheaper. 89% of enterprise spend still goes to closed models.
Comparable performance at a fraction of the cost. Running locally, your data never leaves your environment. Your CFO and board have an obligation to at least evaluate this. The model is commoditizing. Continuing to pay frontier prices is not a technology decision. It is a governance failure.
Exposure 03
No governance layer means nobody knows what your AI is actually doing.
Citizen-built AI workflows touching production data with no audit trail, no defined owner, no rollback procedure. When it breaks, nobody knows who is responsible. The CTO's question is coming: "Who owns what these AI systems are doing to our data?" You need a defensible answer before it becomes a crisis.
Intelligence Sovereignty
Privacy is keeping data secret.
Sovereignty is something harder.
Privacy means an external party cannot see your data. Sovereignty means no external party can analyze your operational intelligence to build against you. The distinction matters. You can have privacy and still be losing the sovereignty fight every single day.
What Sovereignty Means
Your infrastructure. Your model weights. Your data. No external dependency on any of it.
The US government's approach to AI infrastructure: own the hardware, own the model weights, own the data. Not because the frontier labs are untrustworthy. Because the structural incentives cannot be managed by contract. Enterprises are arriving at the same conclusion.
Model-Agnostic by Design
Build the operating layer once. Swap a better engine in without rebuilding.
We build on your infrastructure with your API keys. The operating architecture is model-agnostic. When a better model ships, you upgrade without rebuilding. When a provider raises prices or changes terms, you have options. That is what infrastructure ownership means.
AI Compounds Headcount
AI-heavy companies grew headcount 10% and entry-level hiring 12%. The outcome is more people doing higher-value work.
RAMP and Revelio Labs studied 21,559 US firms. Companies with deep AI adoption grew faster and hired more. The fear that AI replaces teams is backwards. The companies that own their AI infrastructure are growing their teams. The ones renting access to it are not compounding.
What's Missing
The model is the engine.
You still need the vehicle.
The model debate is over. Every company has access to capable AI. The constraint has moved. It is the four-layer operating architecture between a capable model and a workflow your business actually runs on, measured in dollars, owned by you, and compounding every month.
Layer 01
Workflow Architecture
Patching AI into an existing process produces marginal gains. Redesigning that process from a blank sheet for an AI-first world produces step-change ones. Every workflow we redesign has one KPI. If that number does not move, neither does the next invoice.
Layer 02
Governance
A named owner for every production workflow. A defined failure protocol. An audit log on every AI action touching a real record. Not a tool. Not a committee. A 6-page operating document and one Salesforce field that makes your AI operation defensible from day one.
Layer 03
Measured Uplift
Every workflow gets a KPI tied to your P&L: hours recovered, cycle time cut, error rate reduced, output volume increased. You receive a written results report every month. If the number is not moving in the direction it should, we fix it before you see the next report.
Layer 04
Adoption Architecture
Your team does not open a new app. Agents do the work. Humans receive the output in tools they already use and approve or override in one click. Adoption is a design requirement built into every workflow from the start. It is not a change management program added after launch.
Your data is your competitive moat. Stop sending it to companies with a structural incentive to compete with you.
ShooflyAI builds on your infrastructure with your API keys. You own the model weights, the workflows, and the data. Always.
Book the Assessment
How It Works
Assessment to production.
In 30 days.
We start with a paid assessment. You get a written report with ranked opportunities and hard ROI projections per workflow. If the numbers work, we build. If they do not, you keep the report and we walk. No obligation, no pitch dressed up as discovery.
Week One
AI Operating Assessment
A 60 to 90 minute working session with your operators. We map your highest-volume workflows, identify where human time is going to coordination and handoffs AI can handle, and flag any governance gaps requiring immediate attention. NDA signed before we see a single record. Written report delivered within 48 hours: four to six ranked automation opportunities, projected labor recovery per workflow, a governance gap analysis, and a prioritized build roadmap you own regardless of what you do next.
$6,000. Credited in full if you move forward. Keep the report if the ROI is not there.
Month One
First System Live and Governance Deployed
We build against the top-ranked workflow from your assessment. The AI system goes live in your environment on your infrastructure with your API keys. In parallel, the governance framework ships: named owners, failure protocol, audit logging. Your team sees automated output within 30 days. No 6-month runway. No pilot that never closes into production.
Month Two Onward
Monthly KPI Reports. Continuous Build.
Month two establishes your baseline. Month three delivers your first KPI report: hours recovered, errors reduced, cycle times cut, documented in writing. We build continuously against your roadmap and extend governance as new workflows go live. You own everything throughout. Your data never leaves your environment. Cancel at any time. The system keeps running.
The Difference
Renting AI vs.
owning it.
The gap is not capability. Every company has access to capable models. The gap is infrastructure ownership. Here is what it actually means in practice.
|
Typical Enterprise AI Rollout |
ShooflyAI Operating Layer |
| Starting point |
Tools deployed, usage tracked, outcomes not measured |
Assessment maps where hours are going and what fixing each workflow returns |
| Governance model |
No defined owner per workflow. Liability grows with usage. |
Named owner, failure protocol, and audit log deployed in week one |
| Time to first outcome |
6 to 12 months, frequently longer for enterprise orgs |
First automated workflow live within 30 days of assessment |
| How ROI is measured |
Adoption rates, feature usage, satisfaction surveys |
Dollars recovered per workflow, reported in writing every month |
| What you own |
Access to a vendor platform. Cancel and lose everything. |
100% of code, data, and infrastructure. Cancel and keep it all. |
| Data handling |
Data passes through vendor systems, may train shared models |
Runs on your infrastructure. Your data stays in your environment. |
| Model dependency |
Locked to one provider's pricing and roadmap |
Model-agnostic. Swap a better engine in without rebuilding |
| Adoption approach |
Training program. New tools to learn. Behavior change required. |
Agents do the work. Humans approve the exception. No new software. |
FAQ
Questions from teams
already running AI.
We already have Claude Code deployed. Is it too late to add governance?
No. The governance framework we deploy is not a re-architecture. It is a structured accountability layer added on top of what you already have: a lightweight intake checklist for new workflows, a named owner assigned to each production workflow, a failure protocol, and an audit log field in the system of record. The whole framework ships in one to two weeks. The earlier you deploy it, the less untangling there is to do later.
How do you identify which workflows to prioritize?
The assessment does that work. We run a 60 to 90 minute working session with your operators and map your highest-volume, highest-friction workflows against two variables: projected labor recovery and build complexity. The output is a ranked table with projected annual value and time-to-impact for each opportunity. You leave with a prioritized roadmap you own and can act on with us or with anyone else.
We have a vendor relationship with Anthropic or Microsoft already. Does that conflict?
No. We are model-agnostic. If your enterprise agreement gives you access to Claude or Copilot, we build the operating layer on top of those models. We are not a model company. We build the workflow architecture, governance, adoption design, and measurement layer that makes whatever model you are already paying for produce outcomes instead of activity. Your existing vendor relationships stay in place.
How is this different from what our internal AI team is building?
Internal teams build what they have time and capacity to build. We deploy a complete operating layer in 30 days because we have already built the components: workflow architecture, governance frameworks, adoption design, and measurement. Your internal team stays focused on proprietary capabilities and roadmap. We handle the operational infrastructure that makes their work compound instead of staying in pilot indefinitely.
Our data has to stay in our environment. Can you accommodate that?
Yes. We build on your infrastructure by default. Your data does not pass through our servers or any third-party AI provider's systems unless that is a configuration you choose. For organizations with strict data governance requirements, we also support fully local model deployment using open-source models that now perform at or near parity with hosted APIs at significantly lower cost.
The Offer
One session.
One report.
You know exactly what to fix first.
AI Operating Assessment
A 60 to 90 minute working session with your operators. We map where human time is going to work AI can and should be handling. Written report within 48 hours. Four to six ranked automation opportunities with projected labor recovery. A governance gap analysis. An implementation roadmap you own and can act on with anyone. NDA signed before we see a single record or system.
Ranked automation opportunities with projected annual labor recovery
Governance gap analysis and immediate risk classification
Workflow-by-workflow complexity rating and time-to-impact estimate
Build-vs-buy recommendation on every opportunity identified
Full implementation roadmap yours to keep regardless of next steps
$6,000. Fully credited toward your first engagement if you move forward. If the ROI is not there, you keep the report at no additional charge. No pitch. No follow-up pressure. A working session with your operators and a written deliverable 48 hours later.
Stop funding
your own disruption.
Own the layer instead.
In 60 to 90 minutes we map where your team's AI effort is going and what it is actually returning. In 48 hours you have a written report: which workflows to fix first, what each returns in dollars, where your data is leaving your environment, and where your governance gaps are.
If the numbers work, we build the operating layer on your infrastructure. If they do not, you keep the report and we walk.
Best fit for organizations with 25 or more people actively using AI tools across production workflows. Managed operations from $5,000/month after the build.
