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How This Actually Works
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The technical architecture behind what you just saw — why it's different from a database, why your data never leaves this building, and how it handles millions of records.
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Traditional CRM / Database
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+ Stores records in rows and columns.
+ Search = exact text matching ("forklift" finds "forklift").
+ Can't understand that "warehouse help" = forklift operator.
+ Slows down as data grows — millions of rows = slow queries.
+ Every search is the same — doesn't learn or improve.
+ Data lives on someone else's cloud server.
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This System (Lakehouse)
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+ AI reads every profile and understands the meaning.
+ Search = semantic understanding ("warehouse help" → finds loaders, forklift ops, shipping clerks).
+ Combines exact filters + AI ranking in one call.
+ Tested at 10 million records at 5ms search — gets faster, not slower.
+ Learns from successful placements — builds playbooks over time.
+ Runs entirely on hardware you own. Nothing leaves this server.
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Your Data Never Leaves This Building
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Local AI Models
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Four AI models run directly on your GPU — no OpenAI, no Google, no cloud API. Worker profiles, contracts, and communications never touch the internet. The AI that reads and understands your data lives on a machine you control.
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Local Storage
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All data stored on S3-compatible object storage running on this server. Encrypted at rest. No third-party databases, no cloud subscriptions. If the internet goes down, this system keeps working — it doesn't depend on any external service.
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Your Hardware
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${g.name || "NVIDIA RTX A4000"} GPU with ${g.total_mib || 16376} MB memory. 128 GB system RAM. All AI processing happens here. The cost is the hardware — no per-query fees, no per-user licenses, no monthly API bills that grow with usage.
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How It Handles Scale
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+ The system uses two search engines that work together — each handles what the other can't:
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HNSW (In-Memory)
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Keeps frequently-used worker profiles in RAM for instant search. Under 1 millisecond response. Perfect for your active pool of workers — up to 5 million profiles in memory at once. 98% search accuracy.
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Lance (On-Disk)
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For massive archives — 10 million+ records stored on disk. 5ms search speed. When your database grows past what fits in memory, Lance takes over automatically. No performance cliff. 94% search accuracy. New data appends in milliseconds without rebuilding the index.
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The system automatically uses the right engine for each query. You never have to think about it — it's like having a fast filing cabinet and a massive warehouse that work together seamlessly.
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Hot-Swap Profiles — Different AI for Different Jobs
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+ The system runs multiple AI models and switches between them in seconds depending on the task. Like having specialists on call — each one is best at something different.
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Qwen 3
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Reasoning & analysis. Understands complex requests. 40,000 word context.
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Qwen 2.5
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Fast structured queries. Generates database searches from plain English.
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Mistral
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Writing & communication. Drafts personalized outreach messages.
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Nomic
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Reads profiles & understands meaning. Powers the semantic search.
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When you switch tasks — from finding workers to drafting messages to analyzing trends — the system loads the right AI model automatically. Only one model uses the GPU at a time, so there's no performance penalty.
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Starting From Scratch — No Data Required
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+ You don't need rich profiles to start. The system works with whatever you have — even just a name and a phone number. Here's what happens as you use it:
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1
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Day 1 — Import what you have
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Upload a spreadsheet with names, phone numbers, and roles. That's enough. The system organizes them by role and location so you can find who you need faster than scrolling a list. No scores, no metrics — just organized contacts.
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2
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Week 1 — You work, it watches
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Every placement you make, every timesheet that comes in, every call you log — the system records it. Not extra data entry — you're already doing this work. The system just starts keeping track. After a week, it knows which workers showed up on time and which didn't.
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3
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Month 1 — The AI starts helping
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Enough data has accumulated that reliability scores become meaningful. "Based on 8 placements, this worker has 95% reliability." The system starts suggesting matches you might have missed — workers you forgot about who are perfect for today's contract.
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→
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The data you saw in the demo above?
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That's what the system looks like after it's been running. Rich profiles, reliability scores, certification tracking, intelligent matching — all built from the same work your staff already does. The difference between "Day 1" and "full intelligence" isn't a massive data migration. It's just time and normal operations.
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What the System Remembers (and Why It Matters)
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+ Every successful operation becomes a playbook entry — a record of what worked. When a similar situation comes up, the system doesn't start from scratch. It checks: "Last time we needed welders in Ohio, here's who we placed and how it went."
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+ This is the fundamental difference from a CRM. A CRM stores data. This system stores decisions and outcomes. Over time, it becomes an institutional memory that doesn't retire, doesn't forget, and doesn't depend on one person knowing everything. Your senior staff's expertise becomes embedded in the system — not replacing them, but making sure what they know is available even when they're not in the room.
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Measured, Not Promised
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+ | Capability | Measured | What It Means |
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+ | Search 500K workers | 341ms avg | Results before you finish typing |
+ | SQL query on 3M rows | sub-100ms | Any analytical question answered instantly |
+ | 10M vector search | 5ms | Scale to 10 million profiles, still fast |
+ | Search accuracy (HNSW) | 98% | Finds 98 of 100 truly relevant workers |
+ | Search accuracy (Lance) | 94% | At 10M+ scale, still highly accurate |
+ | Filter accuracy | 100% | State, role, reliability filters are SQL-verified — never wrong |
+ | Concurrent users | 10+ simultaneous | Tested with 10 parallel queries in 82ms total |
+ | Cloud dependency | Zero | Works offline. No internet required after setup. |
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