arosplatforms

I built an AI team with Grok Bot for ArosPlatforms.

One request: “Get me ready for today.”

But the real question wasn’t what it could do.
It was what I should LET it do.

Full build + security walkthrough 👇

@bot @SpaceX

Grok Bot Explained: I Built an AI Team for My Business

youtu.be/SMCgdqjom-0

#GrokBot #AIAgents

One of the first things we design in any enterprise AI platform is the router: the part that decides which model handles which task.

Sending everything to the largest frontier model is the fastest way to a cost problem. Sending everything to the smallest model is the fastest way to a quality problem.

Our pattern: classify each task by difficulty and risk. Routine extraction and classification go to a small, fast model. Ambiguous or high-stakes cases escalate to a stronger model. When the strong model is still uncertain, the case goes to a person.

Every route is backed by evidence from the client's golden set, not by preference. We publish a cost budget per case type, cache what is safe to cache, and log which model answered every request so finance and audit can see it.

Then we re-test the routing table every time a provider ships a new model.

The goal is not the cheapest platform. It is the lowest cost per correct answer.

#AI #LLMOps #FinOps

Retail markdowns are one of the largest margin decisions a company makes, and most are still set by habit and a spreadsheet.

Here is how we architect markdown and promotional pricing for a retailer.

First, a demand model by item, store cluster and week that estimates price elasticity from the retailer's own sales, inventory and promotion history, including cannibalization across similar items.

Second, an optimization layer that proposes markdown depth and timing against a clear objective: sell-through by a target date at the best achievable margin, within brand and pricing rules the merchant team defines.

Third, an agent that writes the recommendation in plain language for each category: what it suggests, why, and what happens if we wait a week.

Merchants approve every price. Nothing goes to the point of sale without a named owner.

What to measure: gross margin on cleared inventory, end-of-season leftover units, forecast error, and merchant override rate by category.

#Retail #PricingStrategy #AI

Most mines run the pit, the plant and the stockpile on three different plans. We build the layer that makes them one.

The problem we design for: the mine plan assumes an ore grade, the mill sees something different by the time it arrives, and the gap shows up as lost recovery, unplanned blending and a tense morning meeting.

Our approach: connect block model, fleet dispatch, stockpile and mill historian data into one governed view. A forecasting model predicts feed grade and hardness by shift. An agent drafts the blend and throughput recommendation with every assumption cited, and flags where sampling data conflicts.

Metallurgists and mine planners decide. The agent never changes setpoints or dispatch orders.

What we measure: forecast error on feed grade, recovery against plan, how often recommendations are accepted, and the time it takes to produce the shift handover brief.

Mining does not need more dashboards. It needs one plan that every shift can trust.

#Mining #IndustrialAI #CanadaTech

A capable open-weight model now runs offline on an ordinary laptop. That changes where enterprise AI can go.

169Pi released Alpie Seed this week: a 9B-parameter open-weight model under Apache 2.0, about 5 GB at 4-bit, built to reason, write code and read images with no internet connection.

For our builds, the interesting part is not the benchmark. It is the deployment surface: a mine site with patchy connectivity, a plant floor that is deliberately air-gapped, an inspector's laptop in the field, a secure room where documents cannot leave.

Our process before it touches a client workload: test it on that client's own golden set, on the actual hardware, with the network off. We score accuracy, abstention when evidence is missing, latency on a CPU-only machine, and how it behaves when a document is in French.

Small offline models will not replace the central platform. They extend it to the places the central platform cannot reach.

#AI #EdgeAI #EnterpriseAI