arosplatforms

We build AI OS for businesses.

[arosplatforms @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

https://t.co/nY1q4Nv4XR

#GrokBot #AIAgents

[arosplatforms @arosplatforms]

A demand forecast is only as useful as the decision it changes.

Most planning teams have a forecast. What they lack is a fast way to turn a demand shift into a re-plan across inventory, production and distribution.

When we build demand and supply planning AI, we design it in three layers:
- Sensing: models that blend order history with promotions, weather, point-of-sale data and supplier lead times
- Reasoning: agents that turn a forecast change into options, such as rebalancing stock between DCs, pulling a production run forward or expediting a lane, each with its cost and service impact
- Execution: approved actions written back to the ERP and planning system, with the planner in control of every change

Planners stop reconciling spreadsheets and start choosing scenarios.

What we design to measure: SKU-location forecast accuracy, forecast value added over the current baseline, inventory turns, and how fast the team responds to a demand shock.

The takeaway: the value is not a better number. It is a faster, better decision downstream of it.

#SupplyChain #EnterpriseAI

[arosplatforms @arosplatforms]

Most AML teams don't have a detection problem. They have a triage problem.

Monitoring is tuned to be cautious, so analysts spend their days clearing alerts that were never going to become a report. The real risk waits in the queue.

When we build AML triage for a bank, we leave the existing monitoring rules in place and put an agent layer behind them:
- It assembles the case file: KYC profile, transaction history, counterparties, adverse media
- It drafts a narrative of why the alert fired and what the evidence shows
- It recommends close, escalate or investigate, with every source cited
- An analyst makes the call, and every decision is logged for the regulator

The agent never files or closes a case on its own. It removes the evidence gathering, so investigators spend their time on judgment.

What we measure: time from alert to decision, analyst time on high-risk cases, and narrative quality scored by compliance.

The takeaway: in financial crime, AI earns its place by making human review faster and better documented, not by replacing it.

#FinTech #AIagents #Banking

[arosplatforms @arosplatforms]

A 78B-parameter model that runs on a single GPU changes the on-prem conversation.

Aleph Alpha released Kolibri this weekend: open weights under Apache 2.0, a mixture-of-experts design that activates about 3.5B parameters per token, a context window of up to one million tokens, and reasoning effort you can set per request.

For our regulated clients, the headline is deployability, not the benchmark table. A model that fits on one H200 can live inside a client's own data centre, next to the documents it needs to read.

Before any new model goes near a client workload, it passes the same gate:
- Our domain evals on real document types, not public benchmarks
- Long-context recall at the lengths the workflow actually needs
- Abstention behaviour when the evidence is thin
- Cost per completed task on the client's own hardware

Kolibri is built for German and English. Our question is how open models like it hold up in English and French.

The takeaway: open weights are now a serious option for sovereign workloads. Evaluate them on your work, not the leaderboard.

#AI #LLM #OpenSource

[arosplatforms @arosplatforms]

The model is not the moat. Every competitor can license the same one tomorrow.

At Arosplatforms, we tell every leadership team the same thing: the frontier models will keep getting better and cheaper, and that is good news. It means the advantage moves to what only you have.

Your proprietary data, cleaned and governed so it can be used.

Your integrations, so AI acts inside the ERP, CRM and core systems where work actually happens.

Your evaluations, the test sets that prove a system works on your cases before it reaches a customer.

Your governance, so risk, legal and audit can approve the next use case in weeks rather than quarters.

That is why we build systems to be model-agnostic. When a better model ships, our clients swap it in, rerun their evals, and keep going.

If your AI strategy is a vendor choice, it is not a strategy. Build the assets around the model.

#LLM #EnterpriseAI

[arosplatforms @arosplatforms]

For a Canadian bank, the question is not whether to use AI. It is where the model runs and who can see the data.

At Arosplatforms, we design private and sovereign AI deployments for banks, insurers and other federally regulated firms: models hosted in Canadian data centres or inside your own environment, with no customer data leaving your control.

We build to align with OSFI expectations on model risk management and technology and cyber risk. That means a model inventory, documented validation, ongoing monitoring for drift, clear ownership, and controls you can evidence to your examiners.

Architecture matters as much as policy. Data residency, network isolation, role-based access, encryption and full prompt and response logging are designed in from day one, not bolted on before an audit.

What we help leaders measure: model inventory coverage, validation status, monitoring alerts resolved, and time to approve a new use case.

Sovereignty is not a constraint on AI. It is what makes it deployable.

#AIGovernance #CanadaTech #FinTech

[arosplatforms @arosplatforms]

Most plants already have the data to predict their next breakdown. It is just sitting in historians nobody queries.

At Arosplatforms, we build two systems for manufacturers that work best together.

The first is predictive maintenance: models trained on vibration, temperature, current and maintenance history that flag degrading equipment early and open a work order in your CMMS, not just a dashboard alert.

The second is computer-vision quality inspection at the line, catching surface defects, misalignment and assembly errors on every unit rather than a sample.

We connect both to the systems your operations teams already use, and we keep inspectors and reliability engineers in the loop to confirm findings and retrain the models.

What we measure: unplanned downtime, mean time between failures, defect escape rate, scrap and rework, and false-alarm rate. A model that cries wolf gets ignored.

Reliability is a data problem before it is an AI problem.

#Manufacturing #AI #Industry40

[arosplatforms @arosplatforms]

In claims, the expensive mistake is not a slow decision. It is an unexplained one.

At Arosplatforms, we build claims triage agents for insurers. They read the first notice of loss, pull the policy and coverage terms, check the documentation for gaps, score complexity and fraud indicators, and route each file to the right adjuster queue.

We design one rule into every deployment: a human signs off on every payout. The agent prepares the file and the rationale. The adjuster makes the call.

That boundary is what lets claims, compliance and legal teams adopt the system with confidence. Every recommendation is explainable, every override is captured, and the overrides become training signal.

What we ask leaders to track: time from first notice to assignment, rework on misrouted files, adjuster time spent on documentation versus judgment, and the agreement rate between agent and adjuster.

Triage is where AI belongs. Judgment stays with your people.

#InsurTech #GenAI

[arosplatforms @arosplatforms]

Month-end close should not depend on how many spreadsheets a controller can reconcile by midnight.

At Arosplatforms, we build AI agents that run the repetitive work of the close directly on top of your ERP: matching transactions, preparing accrual and reconciliation support, flagging variances and drafting the commentary.

What the agents do not do is decide. Every exception is routed to the right controller with the evidence attached, and nothing posts without a human approval.

Every action is written to an immutable audit log: what the agent saw, what it proposed, who approved it. Internal audit can replay any entry.

We deploy in Canadian-hosted environments so financial data stays in the country.

What we measure: days to close, share of reconciliations prepared automatically, exception volume per controller, and post-close adjustments.

The goal is not a faster close at any cost. It is a close your CFO can defend.

#EnterpriseAI #FinTech #AIagents

[arosplatforms @arosplatforms]

@tobi The flip side for merchants: if agents do the shopping, your product data is your storefront. Accurate specs, live stock, a clear return policy, all in structured form. A lot of small Canadian catalogues aren't ready for an agent to read yet.

[arosplatforms @arosplatforms]

@kimmonismus For businesses the lesson is less 'go local' and more 'know your data path'. Before a client file touches a model: where is it stored, how long is it kept, who can read it. For Canadian firms with PIPEDA obligations, that answer should be in writing, not assumed.

[arosplatforms @arosplatforms]

@karpathy @omarsar0 Same on the business side. A lot of owners we talk to met AI through ChatGPT within the last year. They don't care which model it is. They want to know if it can answer the phone on a Saturday without botching a booking. Starting from that question is most of the job.

[arosplatforms @arosplatforms]

@chamath This is the question SMBs should ask before picking a vendor: who keeps the corrections? Your SOPs, pricing logic and client edge cases are the asset. Keep them in your own knowledge base and retrieval layer so you can swap the model underneath.

[arosplatforms @arosplatforms]

@alexandr_wang Muse for small business is the one I'd push hardest. What owners ask for first is boring: answer the phone, book the job, update the CRM, chase the quote. Nail clean handoff to a human when it's unsure and Canadian SMBs will adopt fast.

[Rohan Bhanot @RohanBhanotAI]

I tested TypeSafe AI’s Jev by building an app that judges startups.

YCJudged turns one yes/no question across 6,232 YC company descriptions into a map of AI judgments.

Watch the walkthrough, then try your own question:
https://t.co/krG8P8UrQw

[Rohan Bhanot @RohanBhanotAI]

I tested TypeSafe AI’s Jev by building an app that judges startups.

6,232 YC company descriptions. One yes/no question. An interactive map of AI judgments.

See how I built YCJudged—and why a structured answer can still be wrong.

@typesafeai @arosplatforms

Watch: https://t.co/SnHXcSz7rk

[arosplatforms @arosplatforms]

@parker_brydon We are competing Trimble AutoBid tool >>> https://t.co/vPP7MaYmJd

[arosplatforms @arosplatforms]

@dsllwn We’re building the permission layer for AI.

No raw passwords. No unlimited access. Every sensitive action can be approved, limited, and revoked.

Think Authenticator , but for AI agents.

[arosplatforms @arosplatforms]

Introducing arosbid.

Pre Construction platform for contractors.

https://t.co/vPP7MaYmJd

#AI #Construction #tech #Automation

[arosplatforms @arosplatforms]

Introducing ArosBid.

Tender in. Bid-ready proposal out.

Built for mechanical and electrical contractors who are tired of bidding at reading speed.

Early access 🇨🇦

- https://t.co/vPP7MaYmJd

#Construction #ConstructionTech #Estimating #Preconstruction #Mechanical