Strategic operations consulting for tech companies who’ve raised capital and need systems that actually work. PM · Product · Data · Finance.
StratFlow AI is Smitha’s hands-on consulting practice. Every engagement is led directly by Smitha — not delegated to a junior team. She brings 15+ years of experience scaling tech companies from pre-seed through Series C, with deep expertise in product strategy, operations, data infrastructure, and financial modeling.
Tools accelerate information work. But the hardest problems in scaling companies aren’t information problems — they’re human ones. Here’s where experience and presence make the difference.
An AI can generate a roadmap. It cannot walk into your board meeting and tell your lead investor that the roadmap was wrong. That takes judgment, relationship capital, and the willingness to own a hard conversation.
I’ve spent 15 years helping tech companies scale operations without losing their minds. From pre-seed chaos to Series C complexity, I’ve seen every failure mode — and built the systems to avoid them.
I specialize in taking messy, fast-growing companies and giving them the clarity they need to scale — without the corporate bloat. My work spans product strategy, operational systems, data infrastructure, and financial modeling.
My approach: Practical systems your team will actually use. No buzzwords. No 50-page decks that sit in Google Drive. Just clarity, momentum, and results.
I also run BridgeYield, a product experimentation and innovation studio at the intersection of FinTech, AI, and democratized finance, and I’m building FraudGoblin, an AI fraud detection platform for the telco–finance gap.
Book a free 30-minute discovery call. No pitch. No deck. Just a real conversation about your biggest operational challenge.
BridgeYield started with a simple belief: wealth creation tools shouldn’t be reserved only for institutions. Each experiment is designed to test that belief in the real world.
BridgeYield is a product experimentation and innovation studio where bold ideas are validated at the intersection of FinTech, AI, and democratized finance.
Over the past year, BridgeYield has evolved through multiple experiments — each one driven by real-world conversations, user discovery, and market demand. Some experiments confirmed a thesis. Some disproved it. All of them sharpened product intuition.
The methodology is simple: rapid prototyping → user feedback → pivot or kill. No experiment runs longer than it should. No thesis is protected from reality.
Follow BridgeYield on LinkedIn →Each experiment is designed to sharpen product intuition. The learning from each one informs the next.
Click any thread to read the full framework and share your perspective. These discussions are seeded by BridgeYield; join the conversation on LinkedIn or reach us directly.
The Stripe and Snowflake data is consistent: companies that shift from seat-based to usage-based pricing see NRR increase 18–34% within 12 months. But the operational complexity triples. Billing becomes harder. Revenue recognition gets complicated. Customer success needs to change how they define value.
The question isn’t whether usage-based is better — it usually is. The question is when you have enough data on customer usage patterns to price it fairly, and whether your customer success motion can shift from “renewing seats” to “driving adoption.”
Three signals that say you’re ready:
Three signals that say wait: You’re pre-PMF, your sales motion is still explaining the category, or your ops team doesn’t yet have metered billing infrastructure in place.
Most founders frame Build vs. Buy as a cost question: “Which is cheaper?” That’s the wrong question. The right question is: “What am I trying to learn, and which path teaches me faster?”
The thing you build tells you something the bought version never will — how your customers actually use it, where it breaks under your specific load, what features matter and which ones sit unused. But only if you know what you’re trying to learn before you build.
Build when:
Buy when:
The test is simple: could your competitor ship the same AI capability in 6 months? If yes, it’s a feature. It might be a great feature — but wrapping it in a product doesn’t make it a moat.
Real AI moats come from exactly three sources: proprietary data (data your competitor can’t get), proprietary labels (human judgment encoded at scale that took years to build), or a feedback loop that compounds (more usage → better model → more usage).
Examples of each:
If none of the three apply, you have an AI-enhanced product, not an AI moat. That’s still valuable — but size your round accordingly.
Founders who close enterprise deals fast share one pattern: they treat the first 5 clients like bespoke consulting engagements. They over-serve, over-customize, and over-communicate. They extract every insight and use it to productize. The product they’re selling at client 10 is fundamentally better because of clients 1–5.
Founders who build first and then sell spend 18 months discovering the features they built weren’t the features buyers needed. They have a product, but not a sales motion. They have a deck, but not a champion inside an enterprise who knows how to justify the budget.
The enterprise GTM rules for first five clients:
BridgeYield is looking for practitioners with hard-won frameworks, investors with portfolio pattern recognition, and founders with problems worth debating in public.
A real-time AI fraud decision engine that bridges the gap between mobile network events and financial transactions — stopping attacks in the 90-second window every other system misses.
Every major fraud system deployed in banking today was designed before smartphones became the primary attack surface. They look in the wrong place, at the wrong time, for the wrong signals.
FraudGoblin ingests MNO signals (SIM swap events, device change timestamps, network anomalies) alongside financial signals and behavioral data — fusing all 42 in under 200ms. The bank finally sees what the telco sees, at the moment it matters.
Most fraud detection literature talks about milliseconds as a speed benchmark. For SIM swap fraud, it’s a survival requirement. The attack window is 90 seconds. A decision at T+5 minutes is not a slow decision — it is no decision at all. The money is gone.
The architectural implications are severe. You cannot run sequential signal lookups. You cannot wait for a database join. Every signal must be pre-computed at rest, fetched in parallel, and scored by a model already loaded in memory. FraudGoblin’s architecture pre-warms feature vectors in Redis, parallelizes all MNO and financial API calls, and runs inference on a model that never cold-starts.
Sequential: 5ms + 30ms + 80ms + 10ms = 125ms + queue wait + DB latency = 400ms+ (too slow)
Parallel (FraudGoblin): max(5ms, 30ms, 80ms, 10ms) + routing = <100ms core + <200ms end-to-end
FraudGoblin targets a focused, defensible slice of a very large market.
Every transaction runs through a five-stage pipeline. All stages run in parallel where possible. The model never cold-starts. Real-time or it doesn’t work.
A sequential lookup of 42 signals takes 400–800ms minimum. FraudGoblin parallelizes all signal fetches using async Kafka consumers and pre-cached feature vectors in Redis. Sequential is not an option at 90-second attack windows.
The XGBoost model lives in RAM on the inference server, pinned at startup. No disk reads, no model loading on request. First request = same latency as millionth request.
Every decision includes feature-level attribution. Required for EU AI Act compliance, Visa/MC third-party reviews, and every model risk committee at a regulated bank.
Each moat layer takes years to build and compounds with the others. Telco data agreements alone take 9–18 months per carrier.
| Comparable exit | Acquirer | Value | Why |
|---|---|---|---|
| Featurespace | Visa | ~$900M | Behavioral analytics + proprietary model data |
| Kount | Equifax | $640M | AI fraud prevention + identity network |
| ThreatMetrix | LexisNexis | $817M | Device fingerprinting + shared fraud signals |
| BioCatch | Francisco Partners | $1.3B val. | Behavioral biometrics for banking |
A Series B digital bank. $2B in monthly transactions. 500k active accounts. Three people in fraud ops. This is what their problem looks like — and what changes when FraudGoblin is live.
We’re raising $3.5M at seed to fund Phase 1–2: signal foundation, first pilot, and the shadow mode ROI that converts the pilot to a paying client. Full deck available on request.