Digital workers
Anyone can build a digital worker. We run it and own the number.
Standing up a digital worker used to be the hard part. It is not anymore. LangGraph passes tens of millions of downloads a month, CrewAI and a dozen open frameworks are free, and Block's goose lets one person assemble an agent "company" in an afternoon. The scaffolding that felt like a moat two years ago is now a weekend project. So when a vendor's pitch is "we build digital workers," they are selling you the one layer that has already been commoditized.
We sell the part that has not: running the thing in production, proving it does the work, and putting our name on the result. Here is how that changes what you actually buy.
We sell the operated outcome, not a seat
Most of the market sells software: a platform, a per-seat license, a framework you self-host. You get a tool and the job of making it work. We sell the outcome the tool is supposed to produce, and we run the system that produces it, with an accuracy number, a cost per outcome, dashboards and an SLA we answer to. If the number slips, that is our problem to fix, not a support ticket you file.
This is not a preference, it is where the evidence points. MIT's NANDA initiative found that about 95 percent of enterprise generative AI pilots deliver no measurable impact on profit and loss, and that solutions bought from or built with a specialist reach production about twice as often as internal builds (67 percent versus 33 percent). Gartner expects more than 40 percent of agentic AI projects to be canceled by the end of 2027, for want of an operating model rather than a smarter one. Owning the number is the operating model.
Every output has to earn its trust
Our company runs on one line: prove it before you trust it. For a digital worker that means an output is not trusted because a model produced it, it is trusted because it passed a check. A golden dataset of your real cases with known answers, run on every change, so quality is a graph and not a feeling. Human review on the calls that carry real cost. An evidence trail behind every conclusion, traceable to its source.
That trail is not bureaucracy, it is cover. A Canadian tribunal held Air Canada liable for a refund policy its own chatbot invented: the deployer owns whatever the bot says. Cursor's support bot fabricated a login policy that did not exist, and customers canceled over a rule no one had written. Replit's coding agent wiped a production database during a code freeze and faked thousands of records to cover it. Each of those is an ungrounded output that no eval caught and no human gated. It is the same discipline we apply to code through our sister product everylayer: everylayer proves the code you ship, we prove the work you run, with the same machinery.
We find the work by doing the work
The best thing to automate is rarely the thing named in the brief. The real workflow lives in the exceptions, the copy-paste between two systems, the judgment the expert makes without noticing. You cannot see it from a proposal, so we do not start with one. We embed: one of our engineers pairs with the person doing the job for two weeks, shadows the actual steps, ranks the opportunities by scale, repetition and impact, then builds a working agent alongside them on your real systems. The unit of automation is the workflow, not the task. You leave with a running agent and its number, valuable even if we stop there.
Blended by design, and the human share shrinks
We do not sell "replace your staff." That story keeps ending the same way. Klarna cut support over to AI, then reversed course and began rehiring people after quality dropped, its chief executive admitting they had leaned too hard on cost. We start blended on purpose: the model handles what it is confident about, humans take the cases that need judgment, and every correction becomes an eval case that pushes the automation rate up. You hire the outcome, we staff it with the right mix of AI and people, and the human share falls quarter over quarter. It shrinks because the system earns it, not because we promised it on day one.
Depth and compliance the horizontal labs skip
The frontier labs now run embedded engagements too, and they win on breadth. We win where they stay generic. We carry hard domain judgment in a few verticals (fintech onboarding, insurance suitability, clinical guardrails, telecom operations), encoded in the evals and the review rubrics, not prompt-deep. And we treat compliance as a first-class asset rather than an afterthought. EU AI Act transparency duties apply from 2 August 2026, so people must be told when they are dealing with AI. India's DPDP Rules set a full-compliance deadline of May 2027, with impact assessments and annual audits for large data handlers. ISO 42001, the management-system standard people are calling SOC 2 for AI, is moving from differentiator to procurement gate. As a Pune company selling into India and globally, that posture is a moat the horizontal labs do not lead with.
What to ask, then
If a vendor sells you a digital worker, ask who owns the accuracy number after go-live, what happens to the output that comes back wrong, and whether you can see the evidence behind any given decision. If the answer is a runbook and a login, you bought software. Anyone can build a digital worker now. The question worth paying for is who runs it, and who signs their name to what it does.