Your yield knowledge, your HACCP program, your SOPs, and your live PPS production data — readable by your whole team through one assistant that never sends a word outside your building. AI is overwhelming; owning your pipeline shouldn't be.
PPS Intelligence holds your written knowledge in one hand and a read-only line into your live PPS database in the other. Questions that used to take an afternoon of pulling reports and flipping binders become one sentence.
The AI model is an engine. Your documents are the fuel. Fuel never becomes part of an engine: the assistant reads your material fresh each time and keeps nothing. It is never trained on your data — update a document, and it's current that minute.
Imagine a librarian who has read every page in your filing cabinet — the yield book, the HACCP plan, every SOP — and never forgets where anything is. Ask a question; it pulls the exact page and tells you which binder it came from.
Chatbots are famously bad at math — so ours doesn't do math. When a question needs a number, the assistant asks PPS itself to compute it, then explains the result. The arithmetic is done by the same system that runs your operation today.
The engine is a brilliant consultant with a perfect briefcase and no memory: handed your file fresh for every question, retaining nothing afterward. Conversation history lives as ordinary records in your building — auditable, exportable, deletable.
Document answers with citations, PPS-computed numbers, and read-only traceability — this is what day one looks like.
What's our historical yield on boneless skinless breast from jumbo birds?
Your yield book shows 68–71% for jumbo BSB across the 2019–2024 sheets, with 69.4% the most common standard. Winter lots run to the low end — and the 2022 revision notes trimming-spec changes that moved it up half a point.
Yield Book · Poultry §4 · Sheets 2019–2024Raw ribeyes came in at $2.41/lb today. What should our yielded cost and list price be?
At your 71.3% yield standard, PPS computes yielded cost at $3.38/lb. Your margin policy for this class suggests a list of $4.12/lb — and two current price-list lines sit below that. Want the list?
⚙ compute_yielded_cost(ribeye) · Yield Book §2 + Price PolicyReceiving just logged fresh poultry at 46°F. What does our plan require?
Your HACCP plan sets the receiving limit for fresh poultry at 41°F. At 46°F it requires: reject or segregate the load, record temps and times, notify QA, and document supplier corrective action — product may not enter storage until dispositioned.
HACCP Plan · Receiving CCP §3.2 — cited for the auditorWhich customers received product from lot 2607103?
14 customers received 212 cases from lot 2607103 between 6/28–7/3 — full list with case counts and BOL numbers ready to export. Largest: 61 cases across 3 orders. A mock recall that took an afternoon takes a sentence.
⚙ trace_lot(2607103) · Your PPS database¿Cuál es el procedimiento para la limpieza de la sierra al final del turno?
Según su SOP de saneamiento: bloquee y etiquete la sierra, desmonte las guardas, lave con detergente aprobado, desinfecte, y registre en la hoja de saneamiento del turno. El supervisor verifica antes de energizar.
SOP · Sanitation §6 — same documents, your team's languageTop 20 customers by margin this year, compared to last year.
Ready — 20 customers ranked by margin dollars YTD with last-year comparison. Headline: your #3 by volume is #11 by margin, and two mid-size accounts moved into the top ten on mix alone. Export to a spreadsheet?
⚙ run_report(margin_by_customer) — shows the exact query it ranIllustrative conversations with representative figures — shown as the assistant behaves: cited, computed, and honest when the documents don't answer.
Your pricing methodology and yield knowledge are trade secrets. This system is designed around that fact — not with promises, but with walls. Open source is becoming necessary to take control of your data.
| Public AI (cloud chatbots) | PPS Intelligence On-Prem | |
|---|---|---|
| Where the AI runs | Someone else's cloud, shared worldwide | A machine in your building |
| Where your documents go | Uploaded to their servers | Never leave your walls |
| Where conversations are stored | Their systems, their policies | Your building, your records |
| Works without internet | No | Yes — unplug it, it still answers |
| Is your data used to train AI | Depends on their terms | Never — architecturally impossible here |
| Who can see your yield book | Unknowable | Your staff. Full stop. |
PPS Intelligence is built local-first on open-source models — but it's not a closed box. It speaks the same tool-calling standards the wider AI world runs on, and can reach out to frontier models when you choose.
The assistant works through the Model Context Protocol — the open standard for connecting AI to real systems. That's how it queries PPS, pulls documents, and runs reports: as governed tools with audit trails, not screen-scraping or guesswork.
It's also how the pipeline grows — new tools plug in without rebuilding anything.
Some moments call for the biggest brains available. When you want it, PPS Intelligence can route specific questions to frontier models from Anthropic or OpenAI — as an explicit, opt-in path you control, never a silent default.
Day to day stays private and local; the frontier is there when the right moment calls for it.
PPS Intelligence runs a variety of open-source models — chosen for your workload, swappable as better ones arrive. The engine upgrades; your knowledge base never moves.
The model lineup
The everyday workhorse — tuned for documents and tool-calling, streaming answers at a brisk reading pace.
The larger Nemotron for complex, multi-step questions — deeper analysis when the workload calls for it.
A strong open all-rounder with excellent multilingual reach — the same SOPs, answered in the language your team works in.
The heavyweight — frontier-class open reasoning that the DGX Station runs comfortably.
The hardware we run it on
A silent box the size of a thick cookbook, on a shelf in your office.
A dedicated tower built on the NVIDIA RTX 6000 — the engine class we run for hosted clients.
Data-center-class compute at your plant — quoted per installation.
A tool you'll trust for compliance and pricing has to be honest about its edges. These aren't fine print — they're design decisions.
Its database access is read-only by construction. Future action features execute only after a person approves, with an audit trail.
Yields, costs, and totals are computed by PPS itself. The AI requests the calculation and explains the result.
If your documents don't answer a question, it says so — and points to the nearest related section.
The engine stays generic; your knowledge stays in your files. That keeps it upgradeable — and keeps your data portable and private forever.
Every plant is different — team size, document volume, how hard you'll load it. We offer multiple tiers and custom solutions that integrate with PPS; we'll scope the right fit together.
Start with frontier models from Anthropic or OpenAI connected to your PPS — the fastest way in, with your keys and your controls.
Your knowledge base and assistant running on hardware inside your walls. Data never leaves the building.
The full engine — whole-company capacity, the largest local models, and headroom for everything on the roadmap.
Multi-site, hybrid, staged rollouts — we build custom solutions around PPS every week.
Bring us one binder and ten questions. We'll show you the rest.
Start the conversation →