Blog
Field notes from the work.
Essays on services-as-software, the post-SaaS landscape, and the delivery models that ship production AI to operating businesses. Why the existing AI consulting firms leave a deck instead of software, what an aligned engagement looks like end to end, and where the next decade of category-defining enterprise software is being built.
13 July 2026 · Javier Leguina
Working a receivables ledger down: a redesigned collections process
Late payment is a working-capital cost most operators carry without measuring it. We walk one redesigned collections workflow and the mechanism by which it closes the gap between what is invoiced and what is collected on time.
10 July 2026 · Samuel Mirpuri
Access is the moat, not the algorithm
When any competitor can generate equivalent agent code, the scarce asset is permission to write into a customer's production systems and the accountability that earns it. Why outcome-aligned pricing and embedded delivery are the structures that win and keep that access.
8 July 2026 · Samuel Mirpuri
The context flywheel: how each engagement makes the next one cheaper and the agents better
In AI services, code is becoming copyable and cheap, so it cannot compound into a moat. What compounds is accumulated context: the library of exceptions, document variants, and write-back patterns that each deployment teaches the shared agent infrastructure. This post states that loop literally and argues why it is a context flywheel a new entrant cannot buy.
6 July 2026 · Samuel Mirpuri
Where value accrues when the code is free: the cost anatomy of an AI services engagement
When model inference and code generation cost almost nothing, the dominant terms in an AI services engagement become context acquisition and the human-in-the-loop exception tail. A line-by-line walk of which costs fall toward zero and which stay.
3 July 2026 · Samuel Mirpuri
Why an agent does discovery and delivery better than a consultant does either
A consulting engagement loses information at every handoff between discovery, redesign, build, deployment, and monitoring. One agent holding the whole arc removes the self-report gap and the handoffs at once, which is where the accuracy and the speed come from.
1 July 2026 · Samuel Mirpuri
Forward-deployed agents, not just forward-deployed engineers
The third-layer story has centered the scarce human forward-deployed engineer. The next move is that agents do most of that work, observing, mapping, building, and monitoring, while a small human core keeps the judgment and accountability that model capability relaxes but does not remove.
29 June 2026 · Samuel Mirpuri
Code was never the moat: context is the only durable asset in AI services
The deliverable a services-as-software firm sells and the asset that makes it defensible are two different things. As model inference collapses in price, the code an agent runs commoditizes, and the durable moat moves to the observed, accumulated context of how a specific business actually runs.
28 June 2026 · Samuel Mirpuri
A map of who else sits in the third layer
The forward-deployed services model has gone from a Palantir curiosity to a contested category with labs, vertical startups, and services firms all in it. Here is who's in it, and where an independent mid-market implementer sits.
26 June 2026 · Samuel Mirpuri
How to baseline a process well enough to stand behind a result
An outcome-aligned engagement is only as honest as its baseline, and most baselines are self-reported and wrong. Here is the measurement machinery that makes a before-and-after comparison defensible.
24 June 2026 · Javier Leguina
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.
22 June 2026 · Samuel Mirpuri
When not to automate, and what an honest engagement does when the savings don't show up
A clear-eyed account of the workflows that make bad automation candidates, why first principles predict it, and what a vendor actually does when the projected hours never appear.
20 June 2026 · Javier Leguina
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 · Javier Leguina
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 leave a stubborn share of every document stack for humans to key, and the long tail of document variability is the whole job, where a language model plus a human earn their place.
16 June 2026 · Javier Leguina
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 · Javier Leguina
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 · Javier Leguina
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 · Javier Leguina
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 · Javier Leguina
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 · Javier Leguina
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 · Javier Leguina
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.
2 June 2026 · Samuel Mirpuri
How to tell an AI delivery vendor that ships from one that demos
A buyer's procedure for separating vendors that put working software on your data from vendors that sell decks, built on the base rates that explain why most enterprise AI pilots never reach production.
31 May 2026 · Samuel Mirpuri
Why services-as-software firms scale where AI consulting cannot
A services firm that ships software, prices against outcomes, and reuses agents across customers is not the same business as a consulting firm with AI bolted on. The unit economics diverge from inception.
29 May 2026 · Samuel Mirpuri
What an aligned AI engagement actually looks like
An engineer in the customer's environment from day one. Discovery in days. Redesign before automation. Pricing against measured savings. This is what an aligned AI engagement looks like end to end.
27 May 2026 · Samuel Mirpuri
There's a third layer of consulting that nobody has named yet
The consulting market has had two layers for fifty years. A third has been forming as AI spreads: teams that wire AI into live systems, govern them in production, and stay accountable six months after the platform vendor moves on.
25 May 2026 · Samuel Mirpuri
The 80-to-99% problem
Foundation Capital named the ratio: eighty percent of capability with twenty percent of effort gets you to a pilot, and the remaining nineteen percent requires roughly one hundred times more work. That work is engineering, in the customer's environment.
23 May 2026 · Samuel Mirpuri
The unbillable hour
Professional services firms still selling time are watching their revenue base compress into the technology. The unbillable hour is the structural P&L problem at the center of every consulting firm in 2026, and outcome pricing is the only way out.
21 May 2026 · Samuel Mirpuri
Services-as-software is the right frame. AI roll-ups are the wrong one.
Two competing strategies are bidding for the post-SaaS opportunity. They are not the same bet. The roll-up plays for multiple arbitrage. Services-as-software builds the firm with software economics from inception.
19 May 2026 · Samuel Mirpuri
The length of the discovery phase is a business-model decision
Interview-based process discovery produces an authored map that starts going stale the day it ships. Observation-based agents produce a more accurate map in days and keep it current. The duration of discovery is set by the engagement model rather than by the work.
17 May 2026 · Samuel Mirpuri
The customer all four delivery models leave behind
There are four ways to buy enterprise AI in 2026. Each assumes a customer profile. Each leaves the same business behind: the mid-market operator with no AI team and a manual workflow consuming a meaningful share of payroll.
15 May 2026 · Samuel Mirpuri
Don't automate. Obliterate.
Michael Hammer's 1990 essay is more correct in 2026 than it was then. AI is automating cow paths instead of obliterating them, and the seventy-five-percent reduction nobody captures is the cost.
13 May 2026 · Samuel Mirpuri
Why your AI consultants left you a deck
By month six of a typical Big-4 AI engagement, the deliverable is a deck and a pilot proposal, with nothing written to a system of record. The model serves the customer it was built for. Most operators are not that customer.
11 May 2026 · Samuel Mirpuri
Where this goes next: the system of action
Systems of record are not going anywhere. What changes is the layer above them: the system of action that reads, acts, writes, and stays accountable for the result.
9 May 2026 · Samuel Mirpuri
The state of enterprise AI in 2026: a map
Five delivery models are bidding for enterprise AI budgets in 2026: Big-4 transformation, AI boutiques, SaaS copilots, in-house teams, and AI roll-ups. None serves the largest segment.
7 May 2026 · Samuel Mirpuri
SaaS is dead: long live services-as-software
Two trillion dollars came off software stocks, the steepest non-recession drawdown in decades. The repricing names what comes after SaaS: services-as-software, where the customer buys the outcome and AI delivers it.