Enterprise AI, deployed inside your security boundary.
Custom AI that runs inside your infrastructure — not on ours, not on OpenAI's. Built for regulated industries where data cannot leave the building.
- Compliance-ready
- regulated deployments
- Multi-tenant
- VPC isolation
- On-prem
- & air-gapped
# Inside your VPC. Inside your boundary.
deploy:
region: us-east-1
vpc: customer-vpc
isolation: multi-tenant
storage:
encryption: AES-256-GCM
keys: customer-managed (KMS)
network:
egress: deny-all
private-subnets: enabled
compliance:
- HIPAA
- SOC 2 Type II
- GDPR (EU residency)
models:
- name: claims-rag
base: open-weight (Llama-3.1-70B)
finetune: customer-private
- name: doc-ocr
base: custom-vision-7B
finetune: customer-private
guardrails:
pii-redaction: enabled
audit-log: full-trace
rbac: customer-rolesIndicative deployment manifest. Every project ships with a customer-specific architecture.
Recognized by the State of California as a top GenAI Innovator
Alongside Google, Microsoft, and OpenAI
Skills, technologies, and scale
- Private LLMs & RAGOpen-weight, fine-tuned
- OCR & Document AIMillions of pages/month
- Agentic AI & MCPTool-use & autonomy
- VPC & On-premCustomer-controlled
- HIPAA & SOC 2Compliance-aligned
- Multi-tenantIsolation at scale
Find what you need by industry, service, or technology.
Whether you're a CISO scanning for compliance posture, a CIO pricing a feasibility engagement, or an architect evaluating retrieval, the site is built around your question — not ours.
Who we serve
Six regulated verticals where deploying AI inside your security boundary changes the math.
- Financial Services
- Legal & Compliance
- Cybersecurity
- Healthcare
- Education
- Retail
What we do
Six engagements across the AI lifecycle: Advisory, Build, Enable, Run.
- AI Feasibility & ROI
- AI Pilot
- Custom AI Build
- Private AI Deployment
- Training & Enablement
- Managed AI & MLOps
What we know
Three pillars: AI capabilities, privacy & compliance, architecture & deployment.
- Private LLMs & RAG
- OCR & Document AI
- Agentic AI & MCP
- VPC & Private Cloud
- HIPAA Deployments
- Multi-tenant Isolation
Built for regulated industries.
Six verticals where private AI is not optional. Every industry page localizes compliance language and links to the technology pillars that power deployment.
Financial Services
Risk, compliance, and financial-crime AI for banks, insurers, and fintechs — with FINRA-aligned data residency and audit logging.
- Financial crime detection
- Compliance copilots
- Document and claims automation
Legal & Compliance
Privilege-preserving AI for law firms, in-house legal, and compliance teams — built so attorney work product never leaves your environment.
- Discovery and review
- Contract analysis
- Compliance monitoring
Cybersecurity
Air-gapped and on-prem AI for SOCs, MSSPs, and security platforms. Threat triage, alert summarization, and policy automation that runs where you operate.
- SOC alert triage
- Policy automation
- On-prem and air-gapped
Healthcare
HIPAA-aligned AI for providers, payers, and life sciences. PHI stays in your VPC — clinical and operational copilots ship to production.
- Clinical documentation
- Prior auth automation
- PHI guardrails
Education
FERPA-aligned AI for higher education, K-12, and EdTech. Student data stays where it belongs while AI powers learning and operations.
- Tutoring copilots
- Student support automation
- Faculty enablement
Retail
Agentic commerce and PCI-aligned AI for retailers and marketplaces — from product copilots to fraud and recommendations.
- Agentic commerce
- PCI-aligned data flows
- Demand and merchandising AI
Across the AI lifecycle: Advisory, Build, Enable, Run.
Six engagements designed for regulated environments. Most customers start with Feasibility & ROI (portfolio-level) or an AI Pilot (single use case), then graduate into a full Build and Private Deployment.
AI Feasibility & ROI
Portfolio-level scoring of AI use cases by value, feasibility, and risk — with a board-ready ROI case and buy/build/wait recommendation.
- Duration
- 6–8 weeks
- Engagement
- Fixed fee
AI Pilot
Hands-on feasibility study — working code on a sample of your real data.
- Duration
- 4–6 weeks
- Engagement
- Fixed fee
Custom AI Build
Model selection, fine-tuning, RAG, agents, integrated end to end.
- Duration
- 3–6 months
- Engagement
- T&M or fixed
Private AI Deployment
VPC, on-prem, or air-gapped deployment inside your security boundary.
- Duration
- 4–8 weeks
- Engagement
- Fixed fee
Training & Enablement
Executive workshops, engineering team upskilling, and certification curricula.
- Duration
- 1 day – 6 weeks
- Engagement
- Per session
Managed AI & MLOps
Production monitoring, drift detection, retraining, compliance reporting.
- Duration
- Ongoing
- Engagement
- Monthly retainer
Operators who teach, publish, and ship.
Our team speaks at Stanford, teaches at Columbia University, publishes original research, and was recognized by the State of California alongside Google, Microsoft, and OpenAI.
Our team worked at
Invited speaker — Stanford University
Market Design in the Age of AI, Feb 2026
Guest lecture — Columbia University
Invited talk on agentic AI and e-commerce to MBA and graduate students.
Field notes from regulated AI deployments.
Long-form essays on private AI architecture, compliance, evaluation, and the mechanics behind production systems.
Natural-Language Interfaces for the Software You Own
Natural-language-to-use (NL-to-use) lets teams ask for outcomes in plain English while the AI safely invokes the software they already own—APIs, tools, and repos—under explicit contracts and tests. With typed tool calling, shared standards (OpenAPI/JSON Schema), and execution-based verification, leaders can track reliability via ECR/TPR, control cost-of-pass, and scale from demos to dependable operations across dev, ops, data, support, and marketing.
Document AI Guide: From PDF/Scan to Reliable Extracted Data
Document AI converts messy PDFs and scans into reliable, auditable data—speeding closes, reducing manual work, and unlocking analytics. This guide explains what Document AI is (and isn’t), compares modular pipelines with end-to-end models, shows where value lands in operations and knowledge workflows, and outlines a pragmatic, hybrid roadmap for the next 2–3 years.
Edge AI, Explained: Why Decisions Are Moving to the Device—and What Comes Next
Edge AI is transforming how businesses deliver intelligence—moving decisions from the cloud to the device for faster speed, stronger privacy, and lower costs. This blog explains what Edge AI is, why it’s gaining momentum, where it’s already creating business value, and what leaders should expect in the next 3–5 years.
Get started
Ready to deploy AI inside your security boundary?
Most engagements start with a 4-6 week AI Pilot — hands-on feasibility on your real data. Book a discovery call to scope yours.