Los Angeles program desk
TitanSphere AI Inc sells three enterprise objects as one file: grounded retrieval over buyer document stores, fixed evaluation benches for those answers, and GPU inference chassis that run the same jobs on-prem.
No consumer chat product. Buyers receive circuit-like packs: corpus maps, citation screens, score tables, and rack photographs that match the live cluster.
Fig. 01 — On-prem GPU inference aisleDownload HD
01 — Systems
Models drift when retrieval, scoring, and hardware live in different tickets. TitanSphere keeps the citation screen, the evaluation dashboard, and the GPU aisle in the same identifier.
The left monitor holds the buyer PDF. The right monitor holds the generated paragraph with numbered citations that land on the same passages. If a sentence cannot be tied to a page, it does not ship.
Every retrieval change is scored on a frozen set: accuracy, hallucination rate, and citation-match percentage. The dashboard is the same object photographed on the monitor — tables and charts, not a slide.
Jobs run on chassis the buyer can walk to. Mesh-front racks in the aisle and open GPU boards in the tray are inventoried to the same job ID that produced the cited answer.
02 — Retrieval
Grounding is a desk layout, not a slogan: source document on one screen, cited answer on the other, highlights that a reviewer can check without opening a second tool.
Retrieval object
TitanSphere indexes the PDFs the buyer already keeps: contracts, policies, and operating reports. Retrieval returns passages; generation may only reuse those passages. Highlights on the answer panel must match the PDF viewport.
03 — Evaluation
A window is not closed until the dashboard tables update and the runbook binder is signed against the same build ID.
Bench object
The evaluation dashboard shows test-set names, accuracy, hallucination rate, and citation-match percentages, with trend charts across builds. Those numbers are copied into the printed checklist on the workbench next to the terminal that launched the jobs.
Runbook object
Evaluation is not a screenshot in chat. The binder lists scored items; the laptop runs the jobs; labeled USB drives hold the test corpora so a later audit can replay the same set.
| Object | What the buyer sees | Pass rule | Stored with |
|---|---|---|---|
| Corpus map | Document list and page counts | Every file has a stable ID | Retrieval pack |
| Citation screen | PDF + numbered highlights | Each sentence has a page | Answer log |
| Score table | Accuracy / hallucination / match | All three above the gate | Dashboard export |
| Trend chart | Build-to-build lines | No unexplained drop | Eval folder |
| Checklist | Signed binder page | IDs match the table | Window ticket |
| GPU aisle | Rack photo and unit map | Job ID on the chassis label | Inference pack |
04 — Inference
Cited answers and evaluation jobs run on GPU chassis in the buyer’s aisle or in TitanSphere’s scheduled racks. The open tray photograph is the same object as the queue graph on the Los Angeles desk.
Accelerator boards are photographed in their slots. Serials go on the same row as the build that passed the bench.
Power cables and fans are checked before the queue is opened. A loose dress fails the aisle close-out.
Operations monitors show cluster graphs for the same chassis. Retrieval and eval jobs share the labeled queue.
Rack unit and job ID are printed for the window. Rollback is a known spare board in the same tray.
PDFs receive document and page IDs before any model is pointed at them.
Dashboard tables and the binder must agree. Failed rows do not reach the aisle.
Chassis photos and serials are attached to the job ID that will serve retrieval.
Cited paragraphs go live only after queue graphs match the labeled racks.
05 — Desk
The Los Angeles desk is small: dual monitors for cluster status and retrieval search, plus a whiteboard pipeline sketch. The work is the pack, not a campus tour.
Operating posture
Corpus maps, citation screens, dashboard exports, runbooks, and rack photos clip to a single program identifier. Later model swaps reuse that ID so citations do not orphan.
No. TitanSphere AI Inc provisions retrieval, evaluation, and inference as named objects for buyers who already hold document stores.
Yes. Each system can stand alone. The identifier format stays the same so chassis can be added without remapping citations.
A corpus map, a frozen bench version, and a rack or queue target. Incomplete rows do not enter the binder.
Aisle, chassis, dual-monitor retrieval, dashboard, runbook, and desk photos are evidence of the objects in the table — not mood images.