Snowflake Won While AWS Existed. So Will We.

Highlights

-Talent intelligence infrastructure is a new category, not a better tool. Like Snowflake for data or AWS for compute, infrastructure changes what's possible, not just how fast you do existing processes.

• Three compounding flywheels create moats that widen every quarter. Customer model flywheel, cross-industry intelligence flywheel, compliance distribution flywheel. Infrastructure compounds. Tools don't.

• Incumbents cannot build this because of position in the workflow. ATS vendors see candidates but not outcomes. HRIS vendors see outcomes but not candidates. Foundation model providers can't access performance data. We connect all three inside your VPC.

• The data moat compounds every quarter. Code moats erode. Data moats compound. 24 months of validated outcome data cannot be replicated by competitors starting today.

• The window is open now, not indefinitely. Open-source model capability, regulatory clarity, and talent data accumulation advantages compound over time. Companies starting now have 24-36 month advantages over companies waiting.

In 2012, Amazon Web Services already existed.

AWS offered object storage. It offered compute. It offered data warehousing tools. By every logical measure, there was no room for a new data infrastructure company.

Then Snowflake launched anyway.

Not by competing with AWS. By building on top of AWS while solving a problem AWS couldn't solve: data sovereignty, cross-cloud portability, and the separation of storage from compute.

Snowflake's answer to "why not just use AWS?" wasn't "we're better than AWS." It was: "We're a different category. AWS is cloud infrastructure. We're data infrastructure. You need both."

By 2020, Snowflake had the largest software IPO in history.

We're building the same category in talent.

OpenAI exists. GPT-4 exists. Every major cloud provider offers AI APIs. By every logical measure, there should be no room for a new AI infrastructure company in hiring.

We're building one anyway.

Not by competing with OpenAI. By deploying inside your infrastructure while solving a problem OpenAI can't solve: data sovereignty, on-premise deployment, and models trained on your actual performance data instead of internet text.

Our answer to "why not just use ChatGPT?" isn't "we're smarter than GPT-4." It's: "We're a different category. OpenAI is foundation model infrastructure. We're talent intelligence infrastructure. Regulated enterprises need both, and legal will only approve one of them for hiring."

This is the Snowflake moment for talent data. Here's why it matters for your organization.

The Infrastructure vs. Tools Distinction

Most enterprise software is a tool. It helps you do your current process faster.

Workday is a tool. It helps you manage candidate flow. It doesn't tell you who to hire.

Greenhouse is a tool. It helps you structure interviews. It doesn't predict performance.

Salesforce is a tool. It helps you manage customer relationships. It doesn't tell you which deals will close.

Infrastructure is different. Infrastructure changes what's possible.

Snowflake didn't help companies manage their existing databases faster. It changed what enterprises could do with data entirely. Cross-cloud queries. Separation of storage and compute. Data sharing without copying.

AWS didn't help companies manage their existing servers faster. It changed what companies could build entirely. Deploy globally in minutes. Scale instantly. Pay for what you use.

Talent intelligence infrastructure doesn't help recruiters screen resumes faster. It changes what enterprises can do with talent data entirely:

• Screen 100% of candidates instead of 1.5%
• Train models on actual top performer outcomes instead of generic credentials
• Capture decision traces that become queryable institutional knowledge
• Build intelligence that compounds with every hire

This is the distinction that matters for CTOs and Chief Data Officers evaluating this investment: you're not buying a better recruiting tool. You're buying the data infrastructure layer that doesn't exist in your current stack.

Why the Category Didn't Exist Before

Talent intelligence infrastructure requires three things to work:

1. Open-source models capable of enterprise tasks

Until recently, running sophisticated AI models required sending data to external providers. The only models capable of enterprise-grade prediction were massive foundation models (GPT-4, Claude, Gemini) accessible only via external APIs.

That changed in 2024-2025. Models in the 7B-20B parameter range, fine-tuned on domain-specific data, now match or exceed frontier model performance on specialized tasks. Llama 3, Mistral, and similar open-source models can be fine-tuned to outperform GPT-4 on specific hiring prediction tasks.

Enterprise AI no longer requires sending data to external providers. The capability gap closed in 2025.

2. Enterprise buying cycles for AI collapsed

Two years ago, enterprise AI procurement took 6-9 months. AI was an "innovation budget" item requiring extensive piloting, evaluation, and approval chains.

That changed. AI moved from innovation budget to operational necessity. Companies that treated AI as experimental are now behind competitors who treat it as infrastructure.

Procurement cycles dropped from 6-9 months to 30-60 days for AI infrastructure. Companies are making decisions in weeks because they cannot afford to wait.

3. Legal frameworks created a clear path

For the first two years of enterprise AI adoption, legal teams operated in ambiguity. What was allowed? What created liability? Nobody knew.

That ambiguity is gone. Major settlements established liability for AI hiring tools. The EEOC issued guidance on algorithmic hiring. The Colorado AI Act, Illinois AI Video Interview Act, and NYC Local Law 144 established what compliance requires.

Fortune 500 legal teams now know exactly what they cannot approve: anything that sends candidate data to external APIs. And they know what they can approve: systems that deploy in customer infrastructure with full audit trails and bias controls.

The window for "SaaS AI on vendor servers" is closing. The window for "AI infrastructure you control" opened.

All three conditions converged in 2025. That's why this category exists now and not three years ago.

The Snowflake Parallel

Here's why the Snowflake analogy from our positioning documents is more than a marketing comparison:

Snowflake solved a specific problem that AWS couldn't solve: enterprises wanted data sovereignty without giving up cloud flexibility.

AWS's business model requires data to live in AWS. Cross-cloud queries were impossible. Migrating data was painful. Vendor lock-in was real.

Snowflake's architecture separated storage from compute and ran across clouds. Your data could live anywhere. You could query across clouds. You could share data without copying it.

AWS's business model was incompatible with the solution enterprises needed.

The same dynamic exists in AI hiring:

OpenAI's business model requires data to flow through their APIs. Candidate PII goes to their servers. Performance data would have to leave your environment. Model training happens on their infrastructure.

OpenAI's business model is incompatible with what regulated enterprises need.

Legal teams at Fortune 500 financial services, insurance, and fintech companies will not approve systems that send candidate data to external APIs. It's not a preference. It's a compliance requirement driven by GDPR, CCPA, state AI laws, and regulatory guidance.

Our architecture is the opposite: everything runs on-prem in your VPC. Zero data leaves your environment. You own the models. Legal has nothing to block.

Snowflake won while AWS existed because data sovereignty mattered more than convenience. We win while OpenAI exists for the same reason.

The Three Compounding Flywheels

Here's what separates infrastructure from tools: infrastructure compounds.

Tools deliver value while you use them. Stop using the tool, value stops.

Infrastructure compounds over time. The longer you use it, the more valuable it becomes. This is why infrastructure companies trade at higher multiples than SaaS companies. The moat deepens every year.

Our architecture creates three compounding flywheels:

Flywheel 1: The Customer Model Flywheel

Deploy → Train on top performers → Screen candidates → Generate hiring outcomes → Retrain on outcomes → Improve accuracy → Attract more hiring volume → Generate more outcome data

At a Fortune 500 insurance carrier, this flywheel operates on a quarterly cycle. Every quarter, the system retrains on new outcome data from their HRIS. Every quarter, prediction accuracy improves.

Models improve 40% in accuracy after 6 months of this continuous learning cycle compared to Day 1 deployment.

After 12 months: 88% accuracy. After 24 months: 92% accuracy (validated at the carrier, Q1-Q3 2025).

Competitors starting today are 24 months behind. They cannot catch up because they cannot access the carrier's training data. It lives inside the carrier's VPC and never leaves.

Flywheel 2: The Cross-Industry Intelligence Flywheel

Bank deploys → Insurer deploys → Patterns aggregate → Industry EVPs improve → New customers get better initial models → More deployments → More patterns

Through the EVP (Evals and Patterns) hierarchy, patterns from multiple deployments aggregate into industry-level intelligence without exposing underlying data.

When the carrier generates patterns about what predicts success for insurance sales agents, those patterns improve baseline accuracy for the next insurance company that deploys.

Not the carrier's data. The carrier's patterns. The data stays in its VPC.

New customers benefit from accumulated intelligence across the industry. The system gets smarter with every deployment. Early customers get better models because the industry flywheel keeps improving.

This is why incumbents can't replicate our accuracy even if they build the same architecture. They don't have the training data. We have it because we've been inside customer VPCs generating validated predictions against actual performance outcomes.

Flywheel 3: The Compliance Distribution Flywheel

Legal approves → Word spreads to peer institutions → Peer institutions ask how → We deploy at peer institution → Another legal team approves → Word spreads further

Legal teams at regulated enterprises communicate. When one bank's legal team approves an AI hiring tool, peer institutions call to ask how.

This is the flywheel that creates distribution without a traditional sales motion.

The carrier's legal approval in 17 days didn't just win the account. It became a reference point for every insurance company whose legal team was evaluating AI hiring tools. "How did they get approved so fast? Who did they use? Can we talk to their team?"

Every competitor blocked by legal is a warm lead. Legal is our distribution channel.

Why Incumbents Cannot Build This

This is the question every CTO asks: "Why can't our ATS vendor or HRIS vendor just build this?"

The answer isn't features. It's position in the workflow.

ATS Vendors (Workday, Greenhouse, Lever, Avature)

ATS vendors see candidate flow. They know who applied, who got interviewed, who got hired.

What they don't see: who became a top performer after hire.

That data lives in the HRIS. It's a separate system, separate vendor, separate database. ATS vendors don't have access to performance outcomes. They can't train models on what actually predicts success because they can only see the hiring side of the equation, not the performance side.

Additionally, ATS vendors have a conflict of interest: their revenue comes from managing hiring workflow. Telling companies that their screening criteria are wrong threatens their relationship with existing customers.

HRIS Vendors (Workday HCM, SAP SuccessFactors, Oracle HCM)

HRIS vendors see performance data. They know who got promoted, who hit quota, who received excellent reviews.

What they don't see: what the candidate pool looked like.

They know the outcomes but not the inputs. They can't train models on hiring patterns because they don't have access to the ATS data that captures what candidates looked like before hire.

Additionally, HRIS vendors receive data downstream, after decisions are made. By the time a performance record lands in the HRIS, the context that produced the hiring decision is gone.

Foundation Model Providers (OpenAI, Anthropic, Google)

Foundation model providers have the AI capability. They cannot access the training data.

Legal will never approve sending performance reviews, compensation data, and candidate PII to external APIs. The compliance exposure is too high.

Foundation models train on internet text. They're excellent at general reasoning. They're poor at predicting who will be a top performer at a specific company because they've never seen that company's performance data.

Generic models achieve 20-25% accuracy on hiring predictions. Our fine-tuned models achieve 80-92% because they train on actual performance outcomes inside the customer's VPC.

Internal Builds

Every enterprise CTO considers building internally. Here's why they don't:

Time: Internal build = 12-18 months. Deploy Nodes = 4-6 weeks. In 12-18 months, you've spent $2-3M in engineering costs and competitors using our infrastructure are already 12-18 months ahead in model training.

Engineering cost: Internal build requires 10-12 engineers for 12-18 months. At fully-loaded cost of $250-350K per senior engineer, that's $3-4M in labor before you've processed a single candidate.

Access to training data: Even if you build the architecture, you still face the same cross-system data access problem. Getting ATS, HRIS, and CRM systems to talk to each other in a way that enables model training requires the integration work we've already built.

Ongoing maintenance: The architecture isn't a one-time build. Quarterly retraining, bias monitoring, compliance reporting, ATS integrations across vendor updates, this requires continued engineering investment.

$300K-$600K annually for infrastructure that's already built, already deployed at Fortune 500 scale, and already improving every quarter beats $3-4M to build something equivalent from scratch.

The Data Moat

Here's the deepest competitive advantage, and why it matters for your organization's decision timeline.

In traditional software, the product is the code. Competitors can reverse-engineer the product, build similar features, and compete on execution.

In AI systems, the product is the dataset.

The model architecture can be replicated. The training data cannot.

Our models are valuable not because of architecture. Competitors can replicate architecture. They are valuable because of the training data: performance outcomes, decision traces, and validated predictions from inside regulated enterprises.

This is the dataset that cannot be bought and cannot be scraped. It lives inside enterprise VPCs and never leaves.

Every deployment generates more training data. Every outcome validation adds to the dataset. Every quarter of production use widens the gap.

After 24 months of production use at the carrier, their models are trained on 850,000+ applicant evaluations validated against actual performance reviews. The prediction accuracy is 92%.

A competitor starting today:

• Has zero training data from the carrier's environment
• Cannot get it because legal won't approve access
• Would need 24 months of production use to generate comparable data
• Would start from scratch, not from 92% accuracy

The data moat is the moat that matters. Code moats erode. Data moats compound.

What This Means for Infrastructure Investment

For CTOs and Chief Data Officers evaluating this investment, here's the framework:

This Is Not a Software Budget Decision

Most AI hiring tools are priced as SaaS subscriptions: $50-150K annually for seats and API calls. That's a software budget decision evaluated against other software tools.

Our infrastructure pricing is $300K-$1.5M annually. That's an infrastructure budget decision evaluated against other infrastructure investments.

The comparison isn't "Nodes vs HireVue." The comparison is "Nodes vs building internal AI infrastructure."

Build internally: $3-4M in engineering costs, 12-18 months to deploy, ongoing maintenance, no cross-industry intelligence.

Deploy Nodes: $300K-$600K annually, 4-6 weeks to deploy, continuous improvement, industry EVP flywheel.

ROI payback in 3-6 months based on documented time-to-hire reduction and screening cost savings.

The Compounding Return Argument

Software tools deliver flat returns. You pay $100K per year, you get $100K per year in value. The value doesn't increase unless you pay more.

Infrastructure delivers compounding returns. You pay $300K in Year 1, you get $300K in value. You pay $300K in Year 2, you get $400K in value (because models improved 40%). You pay $300K in Year 3, you get $600K in value (because 24 months of outcome data dramatically improved accuracy).

The cost stays flat. The value compounds.

This is the infrastructure investment logic that CTOs understand from data infrastructure decisions: Snowflake, Databricks, AWS. The returns compound because the system learns.

The Cost of Not Deciding

Every quarter you wait is a quarter of training data you don't have.

After 12 months of production use, you have:

• Validated success profiles for every role
• Decision traces from thousands of hiring decisions
• Outcome data connecting predictions to actual performance
• Models that are 40% more accurate than Day 1

A competitor starting in 12 months starts from zero. They cannot buy your 12 months of validated outcome data. It lives inside your VPC.

The cost of waiting isn't just delayed ROI. It's a compounding competitive disadvantage that widens every quarter.

The Enterprise Deployment Reality

For technical evaluators assessing feasibility, here's what deployment actually looks like:

Architecture

The system deploys as standard Kubernetes containers in your VPC (AWS, Azure, or GCP). Single-tenant architecture. No shared infrastructure with other customers.

Technical requirements:

• Standard cloud compute (no GPU clusters required, 7B-20B parameter models run on CPU)
• VPC with standard networking configuration
• API access to existing ATS and HRIS systems
• SSO integration (SAML 2.0: Okta, Azure AD, Google Workspace, Ping Identity, OneLogin)

Security Certifications

• SOC 2 Type I and Type II (our held certifications)
• Other frameworks (ISO 27001, FedRAMP, and the like) are supported as customer-program alignments inside your environment, not certifications we claim to hold

Deployment Timeline

• Weeks 1-2: Infrastructure provisioning and security review
• Weeks 3-4: ATS and HRIS integration
• Week 5: Initial model training on top performer data
• Week 6: Go-live. First shortlist delivered in 72 hours.

Integrations

ATS: Workday, Greenhouse, Lever, Avature, BambooHR, SAP SuccessFactors

HRIS: Workday HCM, SAP SuccessFactors, Oracle HCM, ADP

SSO: SAML 2.0 (Okta, Azure AD, Google Workspace, Ping Identity, OneLogin)

Data Architecture

Everything runs in your VPC. No external API calls. No data transmission to third parties. Customer owns models, data, and all IP. Nothing shared with us.

ELK Stack logging for every decision. Full audit trail. EEOC/OFCCP compliance exports available.

Why Now Is the Window

Three conditions that created this market window won't stay open forever:

1. The open-source model capability window

7B-20B parameter models are capable of enterprise-grade predictions today. In 2-3 years, everyone will have fine-tuned open-source models. The companies that start now will have 2-3 years of training data advantage.

2. The regulatory clarity window

Legal teams know what to approve now. The Colorado AI Act, Illinois law, and NYC Law 144 created clear compliance requirements. Companies that deploy compliant infrastructure now are ahead of companies scrambling to comply when enforcement ramps up.

3. The talent data accumulation window

Every quarter of production use generates training data competitors can't replicate. The companies that start accumulating data now will have insurmountable advantages in 24-36 months.

30% of large enterprises have already committed to sovereign AI platforms according to industry research. 95% will within 3 years.

The question isn't whether to deploy talent intelligence infrastructure. It's whether to deploy it now (while the data moat is still buildable) or later (when competitors have 2-3 years of compounding advantage).

The Talent Context Graph

Here's where this leads in 24-36 months.

Every hiring decision generates a decision trace. Every outcome validates or refutes predictions. Every quarter adds to the dataset.

After 24 months of production use, you have a Talent Context Graph: a queryable record of how talent decisions were actually made, why they were made, and whether they worked.

You can query it like a database:

• "Show me every exception we granted for candidates without a degree and how they performed."
• "What sourcing channels actually produced top performers for engineering roles?"
• "How did our interview panel resolve split decisions and which approach correlated with better outcomes?"
• "Which hiring manager's gut calls turned out to be right most often?"

These questions are unanswerable today because the reasoning was never captured.

With 24 months of decision traces, you have institutional knowledge that doesn't exist anywhere else. Not in your ATS. Not in your HRIS. Not in any vendor's database.

It lives in your VPC. Trained on your outcomes. Capturing your institutional knowledge.

This is what Snowflake built for data. This is what AWS built for compute. This is what we're building for talent.

The coordination layer for enterprise talent decisions.

The Infrastructure Decision

For CTOs and Chief Data Officers, the decision framework is straightforward:

Option 1: Wait and see

In 12 months, you've lost 12 months of training data. Competitors who deployed are 12 months ahead in model accuracy. The gap is widening.

Option 2: Build internally

12-18 months. $3-4M in engineering costs. No cross-industry intelligence. Start from zero on training data.

Option 3: Deploy Nodes

4-6 weeks. $300K-$600K annually. Start with baseline accuracy from existing industry EVPs. Improve 40% in 6 months. Own your models and data forever.

The infrastructure investment logic is clear. The data moat compounds. The compliance window is open now.

The only question is timing.

FAQs

How is this different from Workday's AI features?

Workday has added AI features to their ATS and HRIS products. These features operate within Workday's data environment, they can only see candidate data that lives in Workday.

The fundamental limitation: Workday ATS and Workday HCM don't fully integrate. The AI features in Workday ATS can't train on performance data from Workday HCM because they're separate products with separate data architectures.

Our system connects ATS, HRIS, and communication systems simultaneously inside your VPC. We train models on the cross-system data that no single vendor can access. This is what enables 80%+ prediction accuracy versus the generic AI features built into existing HR software.

Additionally, when you use Workday's AI, Workday owns the intelligence. When you use our infrastructure, you own the intelligence. The models are yours. The data is yours. The IP is yours.

What's our exit strategy if we want to switch vendors later?

You own everything. Exit is simple.

The models are trained in your VPC and are legally yours. The decision traces are stored in your databases. The integration architecture connects to your existing ATS and HRIS systems.

If you decide to stop using our infrastructure, you retain:

• All trained models (continue using them independently)
• All decision traces and audit logs
• All candidate scoring history
• All performance prediction data

You lose: ongoing model updates, quarterly retraining cycles, new feature deployments, and support.

But your institutional knowledge, 12-24 months of validated hiring intelligence, stays in your environment. That data doesn't disappear when the vendor relationship ends.

This is fundamentally different from SaaS: when you cancel SaaS, you lose access to everything. When you stop using infrastructure you own, you keep everything.

How do you handle model governance? Who controls updates?

You control all model updates. Nothing deploys to production without your approval.

Quarterly retraining cycle:

1. System proposes model updates based on new outcome data
2. Your team reviews proposed changes
3. You approve or reject specific updates
4. Only approved changes deploy

Updates are targeted, not wholesale. If the "insurance sales" success profile needs updating, only that adapter changes. Other profiles stay untouched.

ELK Stack logging captures every model decision. You can audit any prediction at any time. EEOC/OFCCP compliance exports are available on demand.

Legal teams appreciate this governance structure because it eliminates the "black box" problem. You're not dependent on a vendor's model governance. You govern your own models.

We already have Workday and Snowflake. Where does this fit in our stack?

Think of us as the intelligence layer between your existing systems.

Workday manages your candidate flow (ATS) and employee data (HRIS). Snowflake stores and queries your enterprise data. We sit between these systems and add the decisioning layer:

Workday ATS → feeds candidate data to our screening agents Workday HCM → feeds performance data to our training pipeline Snowflake → receives decision traces and analytics from our system

We don't replace any of these systems. We make them more intelligent by connecting them and adding AI decisioning that none of them can provide independently.

The analogy: Databricks sits on top of your data lake. We sit on top of your talent data. You keep your existing infrastructure and add the intelligence layer.