Illustrative venue
Amsterdam live practice session
A live operating room for legal and procurement teams turning policy into reusable Workblocks.
The platform for AI-first enterprise apps
Enterprise apps. Built to adapt.
Beyond Work is the platform for running Workblocks: secure enterprise app containers for AI-first applications.
Start with WORKABLE, our engineered enterprise products. Use AI agents to deliver and adapt them to your business.
Product depth. AI adaptability. Enterprise control.
How it fits together01 / What we sell
The product you need. The freedom to make it yours.
The depth and quality of a specialist enterprise product, with the flexibility to adapt as your business changes.
- The products
WORKABLE
Engineered products for savings, contracts, procurement and more. Each starts with a defined business job, workflows, rules and quality requirements.
See Workable - The app containers
Workblocks
Secure enterprise containers for AI-first apps. They give each app a place to run, with defined access, permitted actions and reviewable versions.
See Workblocks - The platform
Beyond Work
The platform to run and manage Workblocks. Connect apps to your systems and bring identity, deployment and operational controls together.
See the platform
Workable is a family of enterprise products. Each one owns one real responsibility, runs as a Workblock, and pauses for human judgement where it counts.
Beyond Labs
Good products have authors.
Beyond Labs is the product studio inside Beyond Work. It is where Workable is made, by named people rather than an anonymous roadmap.
02 / Beyond Work
Delivering intelligence to the hardest problems in the enterprise. Safely.
Built with the firms that already hold the enterprise relationship, on a platform that belongs to no single model vendor.
Built with
Global consulting firms, on the work their clients already run. Named firms held pending written approval.
Plenty of enterprise AI is built by people who have read about enterprise scale. The hard part of enterprise software was never the interface. It is everything behind it, and this is built by people who have already built that part. Named firms held pending approval
Meet the teamBuilt on
The Beyond Work Platform. Fully model-agnostic by design. We onboard bespoke and region-specific models where a client needs them, and no part of the platform is coupled to one vendor's roadmap.
See the platform03 / How it works
AI is the exception, not the process.
Most AI platforms send every request through a model, every time, so the bill grows with the use. A Workblock works the other way round. AI works with expert humans to design the system of work once. Software runs it. AI returns only where judgement is needed, and a person approves what should never be automatic.
Intelligence becomes an asset you own, not a bill that grows.
04 / Workblocks
The unit of intelligence in the enterprise.
A Workblock is the unit of execution. It takes inputs, applies business logic and AI where useful, brings in people where judgement matters, acts across systems, records evidence, and produces an outcome. Workblocks are governed, reusable, and improve with use.
- 01
Author
The person who understands the problem builds the solution. No engineering handoff. No translation layer.
- 02
Run
Workblocks run the work in production. They stop for a person whenever judgement is needed. Every run is governed, audited and single tenant.
- 03
Audit
Every step, every decision, every input, recorded as it happens. So you can check and prove compliance whenever you need to.
- 04
Reuse
Author a Workblock once and reuse it across clients, teams and processes. Each new client starts from what already works, in their own single tenant. The pattern copies over. The data never does.
See Workblocks
05 / Applied Work
In action.
- 01
Supplier invoices, at the scale of a global logistics operator.
75,000 invoices a month. Work that took four hours takes thirty minutes.
- 02
Sustainability extraction at a €35bn pharma.
A hundred-attribute schema across 23 languages, and 20,000 hours of manual work returned.
- 03
Vendor onboarding at an energy major.
Onboarding cycles cut by more than half, manual corrections down about 40%.
The operational record of what the platform does in the world: use cases, case studies, research papers and demos. Every piece carries an author, a date and a version.
The platform
Trust is what lets intelligence act.
Beyond Work is a mature enterprise platform. Every deployment is a single isolated tenant, compliant, governed and auditable, in the region the client chooses.
Security and compliance06 / Partners
Where the network gathers.
Hackathons, briefings, demo days, practice sessions and the occasional dinner. Some are open to the network, some are invite-only.
Illustrative venue
Berlin Workblock hackathon
Two days in-room designing reusable procurement Workblocks with industrial teams.
Illustrative venue
London partner day
A completed partner session on turning consulting delivery patterns into reusable Workblocks.
Partner network event · Demo dayPartner network members can sign in to view details.
Invite-only event · RoundtableThis event is invite-only. Contact your alliance lead for access.
07 / Field Notes
The magazine layer.
Reflective writing about the field: the work we are doing, the field we are operating in and the arguments we are making. Beyond pieces, Briefings and Dispatches.
Why consultancies will win the trillion-dollar AI war
The market keeps insisting that smarter AI products, sold direct, are how the trillion-dollar enterprise AI shift gets captured. That is not how the work moves.
Long-form essays.
Bi-monthly. Signed. Voiced.
The synthesis cadence.
Quarterly. Synthesises what we have seen, learned and changed. The annual flagship, State of Enterprise Intelligence, sits here too, when it ships.
Short-form. Weekly.
Technical-adjacent. The lab's notebook: what is in front of us this week, briefly.
We are rebuilding the website, so more notes follow soon. Thank you for your patience.
The ideas behind the work.
Writing from Christian Lanng on enterprise software, human expertise and what changes next.
A Love Letter to Consulting and the Human FactorThe people closest to the problem are the place to begin.
While the world has been racing to announce the next model, the next benchmark, the next trillion-parameter breakthrough, we have been busy. Busy believing something that sounds almost naive in 2026: humans will always create better outcomes the closer they are to the problem.
Not closer to technology. Closer to the problem. There is a difference, and the firms that are the closest partners have understood it for decades. You sit in the room with the CFO who cannot sleep. You see the supply chain that breaks every quarter. You know which process actually matters and which one is theater. That proximity, that knowledge, is not something an LLM can replicate. It is the most valuable asset in enterprise AI, and it already sits inside your firm.
The Blackberry moment of legacy SaaSWhat happens when a new technology is seen through an old product?
Apple did this because their organization approached a new market as a bottom-up design exercise to deliver the best possible user experience, not just circumvent current limitations or slightly improve the previous economic model. Conway's law, in full effect, allowed them to “think differently”.
We are at a similar moment today in software. SaaS applications are mostly an evolution of what was before, disrupting by optimizing within the constraints of the architecture of existing enterprise software, business, and organizational models. This also means that as AI becomes a reality, they see it through the lens of their products today. They are prisoners of Conway's law.
The answer so far has been copilots and AI chatbots as an addition to existing applications. This is like Blackberry's thinking that to compete with Apple, all they had to do was remove the keyboard. By the time Blackberry came out with a phone with no keyboard Apple had put a full computer in its customers' pockets.
The dirty secret of enterprise softwareNew ways to work change the economics of software.
The economics of distribution also change in other ways. Once we have applications that can understand most kinds of unstructured data and transform them, we will have to question the amount of work we have put into automation or even capturing and normalizing structured data at the source. LLMs are essentially the container of computing (not in the Kubernetes sense), but in the sense that they completely transform the economics of large-scale work systems, just like containers transformed world trade.
Anyone who tells you they know how this will play out doesn't have a clue, but all I can tell you is that tomorrow won't be like yesterday, and that is really exciting.