Shopify for AI deployment
DRAGbot is AI deployment infrastructure for enterprises who need to control how LLMs operate inside their organizations.
The consumer story (no-code chatbots for regulated industries) is the wedge. The infrastructure story is the business.
Unblock compliance. Make deployment trivial. The demand and use cases will follow.
Two Blockers Stop Every Enterprise
Today, enterprises face two blockers to deploying conversational AI:
1. Compliance
Legal teams veto deployments because they can't prove PII isolation.
- •Sensitive data flows to LLM providers
- •No way to prove what AI said to auditors
- •Compliance teams can't sign off
2. Complexity
Deploying AI requires ML engineers, DevOps, and months of integration.
- •Container orchestration complexity
- •LLM provider management overhead
- •RAG pipeline implementation
Remove both blockers, and demand emerges.
Use cases that don't exist today become obvious when deployment is trivial and compliant by default.
What We Actually Built
Four infrastructure layers that make deployment trivial and compliant by default:
1. Deployment Orchestration
One-click provisioning of containerized AI agents. Configuration as data, not code.
- • Multi-provider abstraction (Claude, GPT, Gemini)
- • Customer-managed API keys
- • Bot Space composition for multi-agent routing
The Shopify model for AI.
2. Observability with Provenance
Every conversation is a cryptographic event stream. Not logging—provenance.
- • SHA-256 hash chains per conversation
- • Real-time debug panels (prompts, responses, markers)
- • Clickable hash verification for full audit chain
What compliance teams need to say yes.
3. Security Protocols
Security is architectural, not policy-based. PII physically cannot reach the LLM.
- • Ghostform - PII stays client-side, AI sees tokens only
- • Row-Level Security on all tables
- • Per-deployment containers with isolated SQLite
Not policy. Architecture.
4. LLM Cost Optimization
Claude-optimized caching strategy. Customers control their own spend.
- • Semantic prompt caching for repeated patterns
- • RAG with local embeddings to reduce LLM calls
- • Protocol-specific routing (simple queries → smaller models)
No shared liability.
| Layer | What's Built |
|---|---|
| Orchestration | One-click containerized AI deployment |
| Observability | Cryptographic audit chains |
| Security | Ghostform + RLS + RBAC |
| Optimization | Caching + customer keys |
How It Works
No code required. Describe what you need, deploy in minutes.
Define Identity
Upload Documents
Generate RAG summary
Deploy
Deployment Options
Architecture already supports customer-controlled infrastructure:
Managed Cloud
Customers bring their own API keys. Shared infrastructure with full tenant isolation.
Customer Cloud
Deployments on their AWS/GCP/Azure. Control plane orchestrates, compute on their bill.
Private Cloud
Fully self-hosted inside customer data centers. Control plane as licensed software.
The Product
Four bot types that cover the full front-desk workflow:
Answer Questions
24/7 Q&A grounded in your documents.
Collect Intake
Forms that never expose PII to AI.
Schedule Appointments
Calendar booking via Calendly.
Triage & Route
Direct to the right specialist.
Why Now
Regulation is accelerating
EU AI Act, HIPAA enforcement, state privacy laws. Compliance is no longer optional.
Enterprises are desperate
They need AI but can't risk compliance failures. Stuck between innovation and regulation.
LLM context windows are large enough
The models are intelligent enough to handle large contexts with with document knowledge.
Technical Architecture
Cryptographic integrity built into every conversation.
Immutable Conversation History
- 1Each turn is hashed: user prompt + LLM response + state
- 2Hash chains to previous turn (like blockchain)
- 3Tampering breaks the chain — mathematically provable
- 4Auditors verify via single API call
The Moat
| Competitor | RAG | Deploy | Privacy Guarantee1 | Crypto Audit2 |
|---|---|---|---|---|
| Botpress | ||||
| OpenAI Assistants | ||||
| Intercom Fin | ||||
| DRAGbot |
1 Privacy Guarantee: Architectural enforcement, not policy. PII captured client-side only; LLM receives tokens, never raw data.
2 Crypto Audit: SHA-256 hash chain per conversation turn. Verify integrity via /verify/:id API endpoint.
Compliance is hard to build, easy to buy. We're the buy.
Distribution Strategy
Partner-led infrastructure enables three distribution paths:
Strategic Partnership (Anthropic, OpenAI, AWS)
The "Claude deployment platform" or "AWS Managed AI Agents" model.
- • Leverage existing enterprise trust and compliance relationships
- • Position as natural extension of LLM API business
- • Accelerate adoption through established GTM channels
Vertical IT Integrator Licensing
Partner with implementation consultancies in regulated industries.
- • White-label DRAGbot for their hospital/insurance/financial customers
- • $100K-250K annual licenses
- • $300K-1M partner revenue potential per partner
Direct Enterprise (Requires Capital)
Traditional SaaS startup path.
- • 18-24 month sales cycles, high CAC
- • Compliance credibility challenges as unknown vendor
- • Least attractive path without funding
Current focus: Path 2 (partner licensing) with Path 1 exploration.
Business Model
Self-serve SaaS
- •SMBs in regulated verticals
- •Per-deployment, per-conversation, or seat-based pricing
- •Self-service onboarding
Enterprise
- •Custom deployments, SLAs, audit support
- •Licensed self-hosted on their own infra
- •Unlimited deploys with enterprise license
The Anthropic Advantage
The same infrastructure, positioned as an Anthropic offering, changes the conversation:
Why Anthropic wins
- •Claude is already trusted as the model provider
- •Compliance teams already evaluated Anthropic's security
- •"Deploy Claude with Claude's infrastructure" is natural
- •Enterprise relationships already exist
What Anthropic gets
- •Deployment layer - API customers → platform customers
- •Observability primitives - Provenance as differentiator
- •Compliance architecture - Ghostform solves regulated-industry blocker
- •GTM proof - Solo founder built enterprise platform with Claude
The Ask
For Partners
Seeking vertical IT integrators and implementation consultancies in healthcare, insurance, and financial services to white-label the platform.
For Strategic Acquirers
Exploring acquisition or partnership with LLM providers who want turnkey deployment infrastructure for their enterprise customers.
Or raising to:
Platform expansion
Multi-cloud deploy, SSO, teams capabilities
Certifications
SOC 2 / HIPAA certification
Go-to-market
Partner licensing sales motion
Founder
Claude-augmented, solo AI infrastructure builder.
Full-stack product developer with 10 years of experience building performant systems across the stack—backend, cloud infrastructure, and React frontends. Background in client/server architecture and deploying internal applications for enterprise environments.
Prior career in FP&A as a CPA, operating in regulated utilities industries.
Lets talk: admin@drabot.io
The thesis is not "we know the use cases."
Make deployment trivial and compliant. The market will show us what it needs.
Shopify didn't know about dropshipping, print-on-demand, or digital downloads when they started. They made selling easy, and the creativity of millions of merchants revealed use cases no one predicted.
DRAGbot makes compliant AI deployment easy. The real value is in what we can't see yet.
Built solo with Claude. That's the proof of concept—for the platform and for Claude's capability.