Pit @pitdotcom

Four stages of enterprise AI: 1. AI suggests, humans still do the work. 2. AI assists, workflows stay the same. 3. AI executes parts of the workflow. 4. AI runs the workflow, humans govern. Most companies think stage 2 is transformation. The ones pulling ahead are building for stage 4.

Fredrik Hjelm 🇸🇪 @FredrikHjelm4

a week ago

The most common job at every enterprise that nobody writes down: human API. Trillions of dollars of human potential disappear every year into manual work that exists only because systems can't talk to each other. We founded Pit to close that gap. Context, model capability, and

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The best explanation of where enterprise software is going.

Adam Jafer @mradamjafer

a week ago

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We keep hearing the same thing from ops teams: "We already tried automating this. It didn't stick." Usually it's because the tool they used built around an ideal process, not their actual one.

Solutions built with Pit don't optimise the old way. They make the old way unimaginable to go back to. This is one of them.

Fredrik Hjelm 🇸🇪 @FredrikHjelm4

3 weeks ago

How Stena Recycling is replacing manual contract validation with AI, built on @pitdotcom Stena Recycling (multibillion industrial) operates across seven countries with over 170 sites. Every load of scrap, electronics, or industrial waste that arrives generates contracts.

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Pit is building the system of intelligent execution, most systems of records will soon be demoted to just plain databases.

Fredrik Hjelm 🇸🇪 @FredrikHjelm4

3 weeks ago

How Stena Recycling is replacing manual contract validation with AI, built on @pitdotcom Stena Recycling (multibillion industrial) operates across seven countries with over 170 sites. Every load of scrap, electronics, or industrial waste that arrives generates contracts.

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SAP is worth $250B because it became the source of truth for enterprise operations. The next $250B company will be worth much more because it made SAP a dumb database. At Voi we replaced a lot of mid and long tail SaaS with custom internal software. Scheduling, ops dashboards, reconciliation layers. It worked. At Klarna, some of the same people went further and went after the critical systems themselves. Replacing an ERP directly is brutally hard. It did not fully work at the core system level, but they understood enterprise software internals at a depth nobody else has. The team that did that work is now building @pitdotcom together. The real value was never inside the systems of record. It was in the human layer around them. The person copying data from SAP into a spreadsheet. The analyst reconciling NetSuite against a supplier PDF. The ops manager chasing approvals by email because the workflow lives between systems and nobody connected them. Real work, and the software just could not do it. Until AI. You build net new software that runs those workflows end to end. SAP stays. It just stops being where the work happens. Phase 2 is more structural. As your software performs the work around a system of record, you extract its logic. You learn what it actually does in practice, not in theory. At that point it goes dumb. A database you act on via API. The value moves up the stack. Phase 3 is the one nobody has built yet. The company that runs the execution layer across thousands of enterprises in the same vertical understands how those operations actually run at a depth no single enterprise can. Every manufacturer sees its own workflows. The execution layer sees the patterns across all of them. That asymmetry grows with every customer and every month in production. The systems of record captured the data. The execution layer captures the intelligence. SAP spent 50 years becoming indispensable. The company that wins the next 50 is not building a better SAP. It is building the layer where the work actually happens, compounding in ways SAP never could.

Thanks @JesseDLandry, we agree.

From the comments on our launch: "Pit feels less like another AI company chasing headlines and more like a team building the plumbing underneath the next generation of enterprise operations while everyone else argues about prompts on LinkedIn." A look under the sink:

Companies are measuring AI adoption. They should be measuring how much work has actually changed.

Pit @pitdotcom

a month ago

The current state of enterprise AI: People copying emails into a chatbot to write their replies. Companies buying Copilot seats. Vendors promising transformation with multi-agent workflows. But inside most companies very little has actually changed. The real impact won’t come

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This is the gap we built Pit to close. Pit produces real, professional-grade software using harnessed code generation that’s documented, maintainable, and ready for production from day one.

Production-ready AI needs infrastructure that doesn't leave things to chance. Documented intent, tests, guidelines. Isolated environments, DB backups, CI on every test. SSO, RBAC, ISO compliance, incident management, observability.

If AI prototypes look impressive to you, it's because you're not an expert. Experts can tell the gaps immediately.

Autonomous software is already here Last week, @mradamjafer and the Pit team were in a customer meeting. The customer had feature requests What they didn’t know: we had built a coding workflow that pulls notes straight from the AI notetaker, distills signal from noise, and automatically opens PRs. Written to our architecture without hand-holding When the team walked out of the meeting, 5 PRs were waiting for review. They were good The customer got a response before they were back from lunch That’s a…shift. Software doesn’t wait for the sprint anymore

The current state of enterprise AI: People copying emails into a chatbot to write their replies. Companies buying Copilot seats. Vendors promising transformation with multi-agent workflows. But inside most companies very little has actually changed. The real impact won’t come from making individuals slightly faster. It will come from deeply understanding how the business works, redesigning processes around what AI now makes possible, and building the systems to run them. The companies that figure this out first will operate in a way nobody else can copy.

@WilcosX @Defi_Warhol @a16z @arc @TheCiridae @tesseralabsai Pit is live in production across telecom, industrials, healthcare, and retail. Real ops gains, already shipping.

Companies do not need another tool they have to adapt to. What they need looks more like an embedded product team. One that understands how the business actually runs, redesigns the process around what AI now makes possible, and builds the systems to run it. That gap between AI potential and production-ready systems is what Pit was built for.

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The new advantage is operational originality.

Henrik Schildt @pHoSchildt

a month ago

@pitdotcom The age of generic solutions is over. The age of custom intelligence is here.

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