AI that runs your business, not assists it.
Stockholm
Joined August 2025
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Pit retweeted
At Pit we believe software and agents will run most internal operations at companies in the years to come, and those who do this transformation will be much more competitive than those who don't. Agents will obsess over your KPIs and their sub-drivers in the business, be proactive, chase things down and close the loop on value creation work.
To do this though, you need to know where in the organisation to look and discover the value creation opportunities within an organisation. Traditionally a company may have bought in one of the large consulting houses to do that work and deliver a report, leaving you to implement it yourself.
Today, agents can do that at a far greater scale, and at a fraction of the cost. We built Pit for this world, our Pit Studio takes an organisational question or challenge, and goes out and finds the answer, the KPIs, the bottlenecks, proactively asks the relevant people in the organisation and builds an opinionated view of the value creation opportunity. This becomes the foundation on which Pit can build the software and a long-reasoning agent to proactively work on improving your business until a goal or KPI is reached.
We are onboarding select customers into Pit Studio, reach out if you would like to preview these capabilities.
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.
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 real-world outcomes.
That's the whole bet.
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.
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.
Hundreds of thousands per year, each needing to clear a complex Microsoft and Oracle stack before an invoice can be raised.
Validation rules were buried inside the systems. When something failed, staff got a cryptic error and started reworking by hand. At that scale, the hours add up fast.
On top of Pit, Stena built a Contract Healer on their existing stack.
No rip and replace. Validates in milliseconds, surfaces failures in plain English, and lets ops update business rules without raising an engineering ticket.
"With Pit, we replaced manual data validation with a real-time AI system, reducing cycle times, errors, and hours spent each year." Helena, Stena.
Projected to save thousands of hours per year.
This is what diffusing AI into the real economy looks like.
Pit retweeted
Pit is building the system of intelligent execution,
most systems of records will soon be demoted to just plain databases.
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.
Hundreds of thousands per year, each needing to clear a complex Microsoft and Oracle stack before an invoice can be raised.
Validation rules were buried inside the systems. When something failed, staff got a cryptic error and started reworking by hand. At that scale, the hours add up fast.
On top of Pit, Stena built a Contract Healer on their existing stack.
No rip and replace. Validates in milliseconds, surfaces failures in plain English, and lets ops update business rules without raising an engineering ticket.
"With Pit, we replaced manual data validation with a real-time AI system, reducing cycle times, errors, and hours spent each year." Helena, Stena.
Projected to save thousands of hours per year.
This is what diffusing AI into the real economy looks like.
Pit retweeted
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.
Companies are measuring AI adoption. They should be measuring how much work has actually changed.
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鈥檛 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.
If AI prototypes look impressive to you, it's because you're not an expert. Experts can tell the gaps immediately.
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.
Pit retweeted
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鈥檛 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鈥檚 a鈥hift. Software doesn鈥檛 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鈥檛 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.
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.
