Co-founder & CEO
Samuel Mirpuri
Samuel is a co-founder of flowscope. He spent his career at McKinsey, leading digital-transformation programs for PE-backed portfolio companies and large enterprises, holds an MBA from Harvard, and is a graduate of MIT. A former Platoon Commander in the Singapore Armed Forces, he writes flowscope's posts on process redesign, the economics of professional services, and where enterprise AI value actually lands.
Posts by Samuel Mirpuri
10 July 2026
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
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
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
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
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
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
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
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.
22 June 2026
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.
2 June 2026
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
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
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
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
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
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
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
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
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
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
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
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
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
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.