Almost a decade ago when I partnered in founding Windpower LAB the thesis was: human time is too precious to do repetitive work that is better done by a computer. With that in mind we built a very early AI model to detect defects on wind turbine blades such that engineers could focus on repair strategies, and higher value tasks.
Today, the potential for automation is greater than ever. Agentic workflows, code generation — all contribute to the cost of automating repetitive tasks with clear rulesets going towards zero. Within the last 18 months or so it has become viable to produce bespoke solutions at scale, and fortune 500s, tech companies and bio-tech are at the very forefront.
However, for most mid-cap companies the reality is that they are stuck with a fragmented IT landscape of various legacy databases, SaaS solutions, and manual processes that don’t talk to each other. And as the business grows, the only thing holding this together is humans acting as glue: doing manual data entry, reconciliation between different vendor systems, trying to extract useful consumer insights across 3-5 different funnel tools.
This is not right, and it is not meaningful. Your time is too precious to be doing this, and let us be honest — it is not contributing much to the bottom line for the company. It is a necessity for survival, not a value adding activity.
At Alida Labs, we are on a mission to automate this away. Either by deploying AI agents directly, or by having agents write the tools, scripts and infrastructure needed. Before, that was unheard of: bespoke solutions would cost too much engineering time to implement, be too costly to stand up, and create a maintenance burden. But this is changing, and it is allowing us to build something we think of as a factory — one that builds solutions rather than products.
What does that actually look like?
Take something mundane. Three people in your organisation spending two days a week reconciling vendor invoices against your ERP. Everyone knows it shouldn’t take that much effort. But the data formats don’t match, there are edge cases everywhere, and no off-the-shelf tool handles your specific combination of systems. So people do it. Week after week.
What changes now is that an agent can learn how your data maps across those systems, handle the weird exceptions that made you think automation wasn’t possible, and just run. Quietly, in the background. Occasionally flagging something for a human when it genuinely can’t resolve it. Not because it needs babysitting, but because some decisions should still be made by a person.
Or consider a product team drowning in PDF spec sheets from suppliers, manually pulling out attributes to feed into a PIM system. An agent can read those documents the way a person would. Except with greater precision, no fat fingering, and it doesn’t go home early on a Friday.
These aren’t science projects. These are the tedious tasks that everyone in your organisation knows shouldn’t be done by a human, but where the cost of building an alternative was never justifiable. That equation has changed. For a fraction of the FTE cost, these problems can be solved with scalable, automated solutions that keep running as your business grows.
Why is this suddenly viable?
Most automation you see out there is built for companies that already have clean data, modern APIs, and a team of engineers to integrate everything. That is not your average mid-cap company. You’ve got a fifteen-year-old database that someone set up in a hurry, a SaaS tool that was bought because it was cheap and did what it needed to, and an Excel sheet that one person in finance maintains and that is, if we’re being honest, critical infrastructure.
This is the messy reality of business. And it’s exactly where you get the most immediate operational efficiency and the most immediate ROI — because the gap between how things are done today and how they could be done is enormous.
The reason this can now happen at a price point that makes sense comes down to something simple: the patterns repeat. Different company, different systems, different data — but the underlying problems are remarkably similar. Vendor A sends data in one format, your system expects another, and someone sits in the middle and translates. We’ve built the tooling and the methodology to stand up that translation layer fast, adapt it to your specific situation, and have it running in weeks rather than months.
Every solution we deliver makes the next one faster and cheaper to build. That’s the factory. Not a one-size-fits-all product that forces you to change how you work, but a way of building tailored solutions so efficiently that bespoke is no longer a dirty word with a six-figure price tag attached to it.
What this is really about
When you free three people from spending two days a week on data reconciliation, you don’t just save money. You give those people back ten hours a week of their working lives. That’s ten hours they can spend on problems that actually require human judgment, understanding a customer, improving a process, coming up with something new.
The mid-cap companies that figure this out are going to look very different in five years. Not because they adopted AI as a buzzword, but because they successfully removed the friction that was slowing everything down. The manual workarounds disappear, the overall operation becomes more efficient, people focus more on the work they were hired to do, instead of working on menial tasks required to get to do the work.
Reconciliation is not sexy, cleaning data is not fun, but the result of letting machines do this instead of humans is meaningful. Both for your business and for your people