We've shipped 40+ AI engagements and have led FDE efforts in the last 12 months... here's the actual AI problems we've been solving for enterprises
1. Compressing long, multi-system business processes. Mapping a process that lives across email, Excel, SharePoint, ERPs, and DAMs, then collapsing it with agentic workflows.
2. Document and unstructured data extraction. Pulling structured data out of messy inputs at scale.
3. Internal knowledge and search across fragmented systems. Querying institutional knowledge that lives in calls, memos, CRM, and docs.
4. Customer-facing AI agents (chat, voice, support). Production agents handling end-customer interactions.
5. Agentic commerce. Catalogs, checkouts, and brand surfaces ready for AI agent traffic.
6. Computer vision in physical workflows and applied to specific operational decisions.
7. AI in regulated/healthcare environments. On-prem, HIPAA, data sovereignty work where the AI has to live where the data lives.
8. AI governance and internal AI sandboxes. Building the safe environment where staff can use AI compliantly.
9. Engineering productivity and software factories. AI inside the SDLC, ticket to PR.
10. Custom model and platform builds for AI startups. Helping AI companies build their own products and stand up FDE arms.
11. Evals, benchmarks, and RL environments. Measurement infrastructure that decides whether agents are safe to ship.
12. Data and ML infra for AI workloads. The foundation under everything else: pipelines, GPU clusters, IaC.
13. AI advisory at the strategy and PE portfolio level. Helping investors and operators decide where AI fits.
@ZainManji Great insight, brands are crazed about agentic commerce but need to understand how these commerce agents perform vs. the traditional experience of going on a website.
Today we're excited to announce CommerceBench, an eval harness and benchmark for real-world agentic commerce, alongside reinforcement learning (RL) environments
CommerceBench evaluates whether AI agents can reliably complete end-to-end commerce workflows across storefronts and platforms
Until now, there's been:
- No commerce-specific benchmark
- No credible, repeatable way to measure true end-to-end commerce performance
- No safe, controllable RL environment to post-train them when they fail
As AI begins to mediate more commerce interactions, this gap becomes critical
Now with CommerceBench, we can measure:
- Is agentic commerce actually "good enough yet" compared to humans
- Which models/labs perform better on success rate, reliability, efficiency, and cost
- Which platforms are most agent-friendly (e.g. Shopify, Salesforce, etc)
- Which interaction methods are best (e.g. Browser, MCP, UCP)
Our goal is for CommerceBench to become the default substrate for evaluating these agents and for post-training
If you're a researcher or frontier lab thinking about commerce agents, reach out, we'd love to talk ❤️
https://t.co/n5rerXFzqe
@tferriss 10/10 - compared to other agencies / services I would say they’re better trained and prepared to work with founders, builders, CEOs, etc. We have two EAs on our team.
Are you curious about how NFTs and web3 can unlock value for your brand and your customers? Do you wonder what the recent volatility means for you? If so, come join me in a fireside chat! 6/7 at 2pm EST https://t.co/2e3PfOXfPo
Lots of companies are playing defense, slowing down, and accepting defeat when they should be playing offense, speeding up, and finding ways to grow.
Are you playing offense or defense?
We’ve hired 80 Senior Engineers & Designers over the last 3 years with 5% turnover. We can hire 5 per month and easily scale up to 10 per month with a 2 person team. Here is what we do: