Secure AI Workforce
Inside Your Infra
For mid-sized teams bleeding $30k-$50k/mo to manual ops. Private LLM + RAG, self-hosted, source code you own. 14-day ship.
// runs inside your VPC, no external calls
model.run(locally, {
encrypted: true,
rag: qdrant.search(userQuery),
audit: true,
zeroEgress: true // verified
}); // zero data leavesThree workforces. One private brain.
Not chatbots. Autonomous workers inside your infra.
AI Receptionist
PRIVATE LLMGreets, qualifies, routes, books. Private RAG over your SOPs.
AI Sales
PRIVATE LLMInstant reply, ICP check, books calls, drafts proposals.
AI Support
PRIVATE LLMResolves from KB + past tickets, escalates with diagnosis.
HOW IT WORKS
Audit
Map $ leaks across sales, support, ops. Loom of actual flows.
Architecture
Private LLM + RAG + tools design. Threat model. Cost model.
Build
Build inside your Git. Daily demos. SOPs become prompts.
Deploy
Ship to your infra. Source, runbooks, monitoring, training.
Buy the factory, not the subscription.
One-time build. You own source, infra, data.
Starter
1 WORKFORCE- 1 workforce
- Private LLM + RAG
- Self-hosted
- Source + runbooks
- Security hardening
Growth
POPULAR- 3 workforces integrated
- Cross-memory + handoff
- Gmail, Notion, Slack, Cal
- MCP + custom tools
- Security audit + training
- 30d async support
Frequently Asked Questions
Everything you need to know about our private AI workforce deployments.
01What is KMZHASAN?+×
KMZHASAN is a specialized Forward Deployed Engineer practice by Hasan Mahmud (13 yrs cybersecurity) that builds self-hosted, private AI workforces — AI Receptionist (342 calls/day, 1.2s avg response), AI Sales (89 leads qualified/day, 22% conversion), AI Support (156 tickets resolved/day, 94% CSAT) — inside your enterprise infrastructure with 0 KB external data egress. Source code you own. No vendor APIs. Deployed on your private GPUs.
02How does KMZHASAN protect sensitive business data?+×
All inference (vLLM / Ollama Llama 3.1 70B), RAG (Qdrant vector database), and workflows (n8n, MCP) run exclusively on your private GPUs and VPC. Zero data sent to OpenAI/Anthropic. 98.7% security score, 24 threats blocked in pre-launch hardening, 100% SOC2 / GDPR compliant. Encrypted at rest, ephemeral logs, no training on your data.
03Do I own 100% of the source code?+×
Yes. 100% of source code, runbooks, Docker configs, Qdrant pipelines, n8n workflows, and model configs belong to your org in your Git repo. MIT licensed. No vendor lock-in, no per-seat fees, no usage meters. You can fork, extend, and self-host forever.
04How long does deployment take?+×
14 business days: Day 1-2 Ops Audit, Day 3-4 Architecture (Private LLM + Private RAG + Private Workflows), Day 5-10 Build (vLLM, Qdrant, MCP), Day 11-12 Security hardening, Day 13-14 Team training & handoff. Includes Ops Audit Doc + Architecture Diagram + Runbook PDF. Live on Day 14.
05What is the pricing for private AI workforce?+×
Starter $5k (1 AI workforce — Receptionist, Sales, or Support), Growth $8.5k (3 workforces orchestrated), Ops Retainer $4k/mo for monitoring, retraining, and new automations. $99 Workforce Audit is credited to build. One-time build, you own it. No monthly AI vendor tax.
06What tech stack does KMZHASAN use?+×
Cloudflare Tunnel for secure ingress, vLLM / Ollama for Llama 3.1 70B inference, Qdrant for Private RAG, n8n + MCP for agent orchestration, Docker + CUDA + Linux for self-hosted deployment, Nomic Embed for embeddings. No public APIs. 100% air-gapped capable.
07What results can I expect in 14 days?+×
Average across deployments: 40% cut in human support tickets, 92% lead qualification accuracy, 22 hrs/week saved per workforce, 1.4m avg handling time, 98.7% task success rate, 1248 interactions/week handled with only 3 flagged for human review. ROI in <30 days for teams bleeding $30k-$50k/mo in manual ops.
08How does private LLM compare to public ChatGPT API?+×
Public: Your data -> OpenAI API -> logs on US servers -> compliance risk, retention, egress. Private: Your data -> on-prem vLLM (Llama 3.1 70B) -> Qdrant RAG -> encrypted VPC, zero egress, 100% compliant, 1.2s latency, source code you own. Private is faster for internal data, cheaper at scale, and secure by default.
09Who is private AI workforce for?+×
Mid-sized B2B teams (10-200 employees) bleeding $30k-$50k/mo to manual sales/support/ops, handling 100+ calls/tickets/leads per week, with SOC2/GDPR requirements. If you cannot send customer data to OpenAI and need AI Receptionist, AI Sales, AI Support that lives inside your infra — we are for you.
10What is included in the $99 Workforce Audit?+×
30-min call mapping 3 AI workforces to save 20+ hrs/week, custom architecture diagram (Private LLM + RAG + Workflows), ROI calculation, 14-day ship plan, and Runbook PDF. Credited to build if you move forward. Book at cal.com/kmz-hasan/30min. Delivered in 24 hours.
Get your 14-day workforce audit.
I map leaks, show what to automate first, give you architecture to own it. No deck.
Security-first. Source in your Git. Runs inside your VPC. If I disappear, it keeps working.