Unclear launch criteria
Everyone likes the demo, but no one has agreed what good enough means.
Independent senior product consultant
The AI demo worked. Production is the part that keeps slipping.
I'm a senior product leader who helps founders, CPOs, and product teams move AI prototypes into production-ready products, with the evals, rollout gates, privacy and cost decisions, and cross-functional alignment that the demo never needed.
Launch-readiness board
AI product ops
Decision memo
What should ship, what must wait, and who owns the next decision?
Strategy, evals, rollout gates, platform dependencies, privacy, cost, support, and leadership alignment in one operating view.
Quality
Eval rubric defined
Ready
Control
Human approval boundaries
Review
Privacy
Data minimization confirmed
Ready
Support
CS playbook updated
Blocked
Track record
Previously Chief Product Officer in financial services and Head of Product in fintech and open banking. Currently lead PM on production marketplace AI in Europe.
Why teams bring me in
The demo works. Production is the hard part.
AI product work usually gets difficult after the first promising prototype. The team has to define quality, decide where humans stay in control, make privacy and cost trade-offs explicit, and line up product, engineering, data, legal, GTM, and leadership around one path.
Fragmented ownership
Product, platform, data, legal, support, and GTM each see different risks.
Weak operating layer
Feedback loops, evals, monitoring, rollout gates, and decisions are still ad hoc.
Selected proof
What it took to move marketplace AI into production.
Senior product work that moved AI from concept and rollout planning into live production while keeping risk, ownership, and product quality visible.
Anonymized European marketplace
Making marketplace AI real in production.
Role
Lead PM managing production marketplace AI work end to end with a cross-functional team across product, engineering, design, data, legal/privacy, platform, GTM, and leadership.
Product
Engineering
Data
Legal/privacy
GTM
Leadership
What shipped
A real-estate assistance workflow and a professional-seller draft messaging workflow moved into production.
What the work required
- Product direction, requirements, and rollout planning.
- Quality definitions, evals, feedback loops, and follow-up signals.
- Seller control boundaries and human approval for risky AI actions.
- Privacy, cost, platform, support, and GTM readiness decisions.
Where teams get stuck
Expertise I bring in
No shared definition of good
Turn subjective AI quality debates into eval criteria, failure modes, launch thresholds, and a product decision the team can stand behind.
Eval rubric and launch criteria
Risk ownership is fragmented
Bring product, engineering, data, legal/privacy, support, GTM, and leadership into one operating view so decisions stop bouncing between teams.
Risk register and ownership map
Human control is vague
Define where AI can act, where a human approves, what needs an override path, and how risky actions are caught before users feel them.
Approval boundaries and rollout gates
The prototype has no operating layer
Add feedback loops, support readiness, measurement plans, release checks, and a cadence for making the next launch-or-wait call.
Product ops system for launchHow to work with me
Senior product structure for AI work that needs momentum.
These offers are built for teams with AI ambition, cross-functional friction, and no time for generic consulting decks.
AI Rollout Readiness Audit
For teams whose AI prototype is impressive but the path to production keeps slipping.
Two to three weeks. Fixed scope. You get a readiness scorecard, launch criteria, a risk and decision register, a measurement plan, and a cross-functional ownership map enough to make the launch-or-wait call with confidence.
Typical engagement: 2–3 weeks.
Send me your AI product challengeInterim AI / Product Lead
Senior product leadership embedded in your team while the AI work ships.
Typical engagement: 8–12 weeks.
AI Evaluation and Product Ops System
Build the operating layer: evals, feedback loops, release gates, and monitoring.
Typical engagement: 4–6 weeks.
AI Product Strategy Sprint
For founders and product leaders with too many AI ideas and not enough clarity on which to back.
Typical engagement: 1–2 weeks.
Fit check
Who this is for, and who it isn't.
- You have an AI prototype that's stalling on the path to production.
- You're a founder or product leader with too many AI ideas and not enough clarity on which to back.
- You need senior product leadership on a complex AI or platform program, fast.
- You need someone to build the model itself.
- You're looking for general management consulting on AI strategy without a product or engineering team to work with.
- You want a fixed-deliverable agency relationship without senior product judgment in the loop.
Method
I turn ambiguity into operating decisions.
Separate what is possible from what is worth shipping.
Clarify user value, buyer trigger, rollout shape, and the decision the team needs next.
Define what good means before scale makes mistakes expensive.
Create evals, failure modes, feedback taxonomies, and release gates the team can use.
Bring privacy, cost, platform, and support risks into the product plan.
Make hidden constraints explicit enough for product and engineering to act on them.
Get the right people making the same decision.
Align product, engineering, data, legal, design, GTM, and leadership around trade-offs.
Experience
A senior product operator across AI, fintech, payments, and consumer products.
Product Lead (AI), freelance
AI agents, marketplace AI, product evals, product ops, and rollout readiness.
Chief Product Officer
Card, credit, A2A/APM, routing, settlement, payment APIs, and team leadership.
Head of Product
Open banking, banking-data products, enterprise product verticals, and experimentation.
Senior Product Manager
Mobile products, monetization, retention, launches, and data-informed roadmap work.
About
Hands-on enough to prototype. Senior enough to lead the room.
I am Sagar Datta, a senior product leader focused on AI rollout, product operations, marketplace products, platform-aware delivery, and fintech/payments products.
My best work happens when the problem is ambiguous, technical, cross-functional, and important enough that a generic roadmap will not survive contact with reality.
In practice, that means turning fuzzy AI ambition into launch criteria, eval rubrics, rollout gates, decision memos, stakeholder cadences, and product plans that engineering teams can actually use.
I work well as an advisor, interim product lead, or sprint operator. I am not the fit for teams looking for model-building alone or broad AI transformation work without a product and engineering team in the loop.
Contact
Bring the prototype, the messy roadmap, or the launch question.
If the AI product is promising but the path to production is not clear yet, that is a useful place to start.