CEO @varickagents, Implementing AI at Enterprise. Prev AI @meta
SF | NYC
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> cutting edge AI architecture advice, something a Fortune 500 CTO would pay $10,000 for
> posted for free on X
> 2 likes
You need to stop following the big accounts and start following ones like this
How to keep agent memory intact when execution environments crash or sleep:
Keep the loop itself on a boring persistent server.
Run code execution and bash calls in throwaway Firecracker microVMs.
When an environment is idle, snapshot the microVM and spin it down.
Route interrupts and webhooks directly to the server so nothing drops while the VM is paused.
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every tech company is going to have a creator program
here’s how i think the good ones will actually work
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Our heterogeneous cloud just got better. We're announcing a cluster scale deployment of @positron_ai as our next partner.
We believe demand for fast inference will outstrip supply for the next decade. Our bet on Positron is another step towards getting fast inference for opensource models into builders' hands.
Hardware goes live Q1 2027.
Thanks to @mitesh711, @trsohmers and the whole Positron team! We're excited for this partnership.
LFG!
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The biggest AI workflows, ripe for agent-ification in each department, that we've seen at Varick:
1. Finance
a. Manage AP exceptions: most AP teams are managing 600-800 exceptions a month where invoices don't match POs or receipts and require manual resolution
b. Automate your month end close: background execution of bank recs, accruals, intercompany, etc. can get your close cycle from 18-22 days to 7-9 days
c. Cash application and collections: automate matching remittance advice to open invoices and getting customers to pay on time
2. Sales Ops
a. Manage CRM data and rep admin: we see sales reps spend 38% of their time updating Salesforce
b. Deal desk approvals: we see deals take 23 days to get discounts, pricing, etc. approved
c. Prepare for forecast calls: ensure all the right data is available for each deal to prepare for Monday forecast calls (call notes, stage changes, next steps, etc.)
3. HR
a. Manage payroll changes: compare your ATS, HRIS, and payroll systems before payroll to make sure all new hires, raises, terminations, etc. are accurate. Most payroll corrections are due to issues before the payroll run.
b. Handle employee inquiries: we've seen up to 80% of inquiries for PTO, benefits, policy questions, etc. can be handled without human intervention
c. Onboarding: manage the process from offer acceptance to day 1, including background checks, equipment, account provisioning, I-9, etc.
4. Legal
a. Redline contracts: reduce turnaround time from 5+ days to same day for first pass redlines on NDAs, MSAs, vendor contracts, etc. based on your contract playbook
b. Contract intake: manage the intake process for contracts, including determining the requestor, type of contract, contract template, contract lawyer, etc.
c. Track contract obligations: extract contract renewal dates, notice periods, auto-renewal provisions, etc. from signed contracts
5. Operations
a. Disruption management: move from a week-long process to manage shipment delays, supplier issues, etc. to a few hours
b. Order management exceptions: many orders have exceptions that require manual management, whether it's a customer on credit hold, bad ship address, lack of inventory, etc.
6. Procurement
a. Supplier onboarding: reduce the time it takes to onboard new suppliers from 26 days to 4 by automating collection of W-9s, banking info, insurance, sanctions, etc.
b. 3-way match: compare invoices to POs and receiving documents to identify instances of duplicate invoices, invoices over PO price, etc.
7. IT
a. Joiners, movers, and leavers: ensure employees have all the right access on day 1, instead of day 4, and remove access the day they leave
b. Ticket routing: we see 1 in 3 IT tickets are routed to the wrong team and require rerouting
8. Marketing
a. Speed to lead: enrich, score, and route inbound demo requests to the right sales rep in under 5 minutes
b. Media reconciliation: reconcile media invoices to insertion orders and media actually run to identify the 8-12% of media spend that is lost
9. Customer Operations
a. Route escalations to the right team the first time, instead of having them bounce back and forth between support and engineering multiple times
b. Create case summaries to ensure your engineering team doesn't send cases back for additional details
All of these can be run in the background using your existing systems (NetSuite, Salesforce, SAP, ServiceNow, Dynamics, etc.) and exceptions can be routed to your people.
If you can solve these for your clients, you'll make them very happy.
But in order to succeed across the entire company, you have to go about this systematically.
Read more on how to go about it below:
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What has to go right for AI to actually show up as positive in your P&L:
- Having the right sponsors: someone from the C suite, with a budget that they can spend and, very importantly, the involvement of the owners of the processes that you're going to be automating. And, also, the early involvement of key stakeholders, like legal, information security, IT, and finance.
- Really understanding the as-is processes that you're going to be automating: this requires process mining of the systems of record, and an understanding of the detail of each process, who's involved, all of the variations, and the volumes and the cycle times.
- Understanding the baseline of the process and the metric that you're going to measure to understand how much value you're creating: are you looking at time saved, cost saved, revenue increased after spending the tokens, or risk reduced, and agreeing the key KPIs that you're going to measure.
- Re-engineering the processes, as you automate them. Usually, you'll end up with about five steps, of which four of them will be automated using AI. And use plain code wherever plain code works.
- Building AI agents that work with all of the systems that you've already got in place, like Salesforce, NetSuite, SAP, ServiceNow, Dynamics, et cetera, so that you don't have to move to a new system, or have people log into a new system. And having one architecture layer underneath all of those AI agents.
- Deciding which decisions that the AI agent can make versus where you need human input, and making that decision based on both the risk and the cycle time to make the decision. The agent proposes, a person makes the call, and every action is reversible and traceable.
- Running the AI agents in shadow mode, in live with your volumes, and comparing their output to the people doing the process now. And not paying out anything they produce until you're absolutely sure that the AI agents are doing what they're supposed to be doing.
- Identifying the 5% to 10% of your people who are already using AI, and getting them to be the AI ambassadors and evangelists, and then training all of your people on how they work with the AI agents, and explaining how their jobs will change.
- Having a plan for 7 months from now, where you own all of the AI agents that have been developed, all of the data, all of the evaluations, all of the logs, they're all in your cloud, and they're built to be agnostic to any particular AI model.
We do all of these at Varick. Partner with us below:
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Introducing SKY-001, the first Engineering World Model.
On CAD and BIM tasks, it beats GPT-6 Astra and every frontier model we tested:
-> 51-125x faster
- > 150-210x cheaper
-> Higher IOU than all frontier models
SKY-001 lets you dream a physical object before you build it.
18 months of work. Built by the team out of Maluuba's deep learning lab (acq. by @MSRResearch).
The first ‘AI General Engineer’ will be the backbone for Autonomous Manufacturing.
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"We are spending millions sending AI experts to learn business. We should be sending business operators to learn AI.
Business knowledge is slow. It took most of my career to instinctively know which trade-offs to make around working capital and inventory.
AI execution is fast. An experienced operational team can master agentic tooling, evals, and model orchestration in a matter of months.
We are running the harder transfer at immense cost and mistaking it for progress."
- Praveen Jonnala, CIO of Vistance Networks
I can back this up from every implementation we've done at Varick (Finance, Operations, HR, all of it).
The exceptions often live in one person's head. Think the vendor who keeps invoicing against a PO that was closed months ago, or the customer whose payment terms got changed on a phone call and no one updated the ERP. These nuances are never documented, and it's not something you can pick up during a 6-week engagement as an AI engineer.
On the flip side, we've seen controllers and Accounts Payable managers learn to read evals and write rules that improve agent outputs in just a few short weeks.
As a result, when we come into a company to implement AI, we start with the operators of a workflow. Our engineers sit with them to understand every nook and cranny of the workflow, translate that into rules the agents can use, and then train those operators to manage the agents once we leave.
If you're interested in doing this kind of AI transformation inside your company, book a call below.
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