Co-founder & CTO
Javier Leguina
Javier is a co-founder of flowscope. At ModelML (YC W24) he was a founding engineer building for the world's biggest banks, and before that he was an AI engineer at advanced startups including Encord and did research on hierarchical reinforcement learning at UCL, where he holds an MS in Machine Learning. He writes flowscope's posts on agents, production reliability, security, and the mechanics of shipping AI into systems that were never built to be driven by software.
Posts by Javier Leguina
24 June 2026
What process redesign looks like inside an industrial staffing firm
A workflow-level walk through recruiting and onboarding at a staffing firm, where the delays in time-to-submit and time-to-fill come from, what an agent can run, and where a human stays in the loop.
20 June 2026
The employee-monitoring laws that decide how an observation agent can be deployed
A handful of facts about US law govern where a capture agent can run, and the bill most blogs called a 2026 law never passed. This is what's actually on the books, and why flowscope adopts the failed bill's principles anyway.
18 June 2026
The long tail of document variability is the whole job
"Automate data entry" names only the easy part of the work. Rules and template OCR plateau around seventy percent straight-through processing, and the long tail of document variability is the whole job, where a language model plus a human earn their place.
16 June 2026
What the agent-reliability curve says about which workflows are automatable now
A mid-2026 reading of the METR task-length curve, turned into a workflow-selection rule for operators deciding what to automate this quarter and what to wait on.
14 June 2026
Writing back into a system that has no usable API
Most mid-market automation stalls at the integration surface, not the model. The engineering case for acting at the desktop a clerk already uses, walked through the accounts-payable queue and QuickBooks.
12 June 2026
Shadowing instead of surveillance, and why the difference is behavioral, not cosmetic
The objection to an observation agent is grounded in real evidence: monitoring backfires when its data disciplines individuals. Diagnostic shadowing inverts every variable the research blames.
10 June 2026
Rebuilding the month-end close, from trial balance to statements
The monthly close is the cleanest worked example in operations because the benchmarks are public. We walk one redesigned close end to end, from where the days actually go to what a finance team does with them back.
8 June 2026
What a capture agent records, and what it's built to throw away
The first thing operators ask about an observation agent is what it collects and where the data goes. We answer with the actual architecture: scoped capture, redaction at the endpoint, minimum retention, and processing inside your own tenant.
6 June 2026
Why documented processes rot, and why watching the work beats reading the SOP
Standard operating procedures go stale because the real process lives in tacit knowledge that resists being written down. Observing the work recovers the layer documentation cannot.
4 June 2026
How production reliability gets engineered, and why a demo is not evidence of it
A pilot at eighty percent on a clean slice tells you almost nothing about whether the workflow runs unattended on Monday. The machinery that closes the gap, with named benchmark numbers.