Foresyn Pitch
Private pre-seed deck
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FORESYN
01 · Vision

Foresyn prepares evidence-backed release / hold / escalate packets for merchant payout and account reviews.

existing tools detecthumans still decideForesyn prepares the packet
Wedge: merchant payout review.
Existing tools detect. Humans still resolve. Foresyn prepares the packet.
Vision | resolution packets for money-in-review
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02 · Problem

Good funds get trapped in review.

A merchant payout hold is not one decision. It is KYB status, risk signals, payout history, disputes, support tickets, ledger state, policy, and prior cases rebuilt manually before a human can release, hold, reserve, or escalate.

7 tabs
One hold becomes cross-system work.Backlogs keep good funds held, move risky funds too early, and create inconsistent case notes.
Problem | manual trust review slows money movement
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03 · Packet

One held payout becomes one review-ready packet.

Foresyn prepares material facts, cited evidence, policy mapping, missing evidence, similar prior cases, recommended action, rationale, approval route, and a draft final case note.

Payout hold

Acme Goods

$42.8kheld 3 days
Release with rolling reserveVolume spike is explainable; KYB and dispute history are clean. Verify bank-account change before final release.
EvidenceKYB verified May 12. 14 prior payouts, no reversals. Dispute rate 0.2%. Shipping proof attached.
PolicyFirst-payout guardrail satisfied. Rolling reserve applies until volume normalizes.
MissingBank-account-change verification still pending before full release.
Similar cases4 prior merchants released after document verification and temporary reserve.
Draft case noteRelease payout after verifying bank-account change; apply reserve until volume returns to baseline.
ReleaseHoldReserveEscalate
Packet | product proof by workflow example
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04 · Existing Line Item

This is already a line item.

Foresyn does not ask buyers to create an “AI workflow” budget. It compresses the work already paid for by merchant risk, payments ops, compliance ops, QA, audit, and BPO teams.

Buyer / KPI map
Merchant payout reviewRisk Opspayout hold time, backlog age, false-positive holdsrelease packet
Payment exceptionsPayments Opsaged exceptions, failed-payment SLA, customer impactnext action
KYB exceptionsCompliance Opsmanual-review rate, time to approve, missing docsapproval packet
Sponsor-bank evidencePartner Compliancerequest turnaround, evidence completenessresponse pack
QA / auditCompliance QAQA fail rate, audit readiness, source trailcase quality
Existing line item | review spend is already budgeted
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05 · Today

Reviewers are human middleware.

The expensive work is not flagging the case. It is turning scattered evidence into a defensible release / hold / escalate decision.

How it works today

One payout queue item becomes a manual tour through the stack.

Payout queueheld funds, account review, payout exceptionstart
Evidence huntKYB, risk score, PSP, ledger, support, disputes, policy7 tabs
Manual noteanalyst writes rationale, escalates, waits for approvalbottleneck
Case updaterelease, hold, reserve, or escalate with source traildecision
Happy paths are automated. Edge cases turn into custom review work.
Pain
Queues back up when evidence work backs up.Good funds stay held, risky funds can move too early, and final notes become inconsistent.
Today | reviewers stitch systems together by hand
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06 · Product

Foresyn prepares the packet. Humans control the action.

No consequential update happens without human approval.

Product loop

1. Ingest case

Payout hold, account review, or payment exception enters the queue.

2. Gather evidence

KYB, risk, payout history, disputes, tickets, ledger, policy.

3. Prepare packet

Facts, citations, policy, missing evidence, recommendation.

4. Human action

Reviewer approves, edits, requests more evidence, or escalates.

5. Update + learn

Draft final note, update after approval, remember the correction.

One queue proves the product.
Start with merchant payout/account review, then expand by the same packet loop.
Product | prepared packets with human-approved case-system updates
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07 · Moat

Every reviewer edit improves the next packet.

Foresyn learns the queue’s policy interpretations, accepted evidence, escalation patterns, final-note style, and repeated failure modes.

Corrections improve future packets.Each edit teaches what evidence matters, when to escalate, how policy is applied, and how the final note should be written.
Moat | queue-specific memory and evaluations
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08 · Competition

Not another detector. Not another case system.

Existing tools detect and record. Foresyn prepares the human-approved resolution across them.

Existing tools detect. Foresyn prepares the resolution.

Use incumbents as evidence sources, not systems to rip out.
Unit21 / Sardine / Bretton / Variancefraud, AML, risk workflowsaround stack
Chainalysis / TRM / Ellipticwallet and KYT intelligenceevidence input
Alloy / Middesk / SumsubKYB/KYC verificationexception packet
PSP / ledger / case systemspayment state and recordscase update
Analysts / BPOmanual prep and writeupsfaster QA
Where Foresyn wins.When the decision crosses systems and still needs a human-approved record.
Do not replacerisk tools
Do not replacecase systems
Ownthe packet
Competition | incumbents detect and record; Foresyn prepares resolution
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09 · GTM

Benchmark one queue before production.

Buyer: Head of Risk Ops or Merchant Risk. Scope: 100–300 historical payout/account review cases.

Two-week benchmark
100–300historical cases
read-onlyno production changes
4core metrics
1queue owner
Inputpast cases, policies, notes, final decisionsbuyer data
Outputrelease / hold / reserve / escalate packetsscorecard
Measureprep time, evidence completeness, supervisor edit rate, escalation quality, QA reworkproof
Nextpaid production pilot in one queuehuman approved
We prove value before production case-system updates.
The strategy.
Use labor and backlog signals to find the queue owner, sell a controlled benchmark, then expand only if Foresyn beats the current review path.
GTM | labor signal to benchmark to human-approved production usage
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10 · Traction

Paid workflow benchmark sold; fintech proof next.

Clear proof boundary: Regtime proves paid benchmark execution. It does not prove fintech PMF.

Proven

  • Sold a paid workflow benchmark.
  • Built live task, review, memory, and approval runtime.
  • Defined first fintech wedge: merchant payout/account review.

Not yet proven

  • One real merchant payout/account benchmark.
  • Fintech PMF and repeatable sales.
  • Live queue expansion after human approval.
$2.5k paid, $5k contract.Now win one merchant payout/account review benchmark, then graduate into human-approved production usage.
Traction | paid workflow benchmark, first fintech queue next
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11 · Founder

Built by a Stripe Risk & Applied ML engineer.

At Stripe, Artemii worked on FM/LLM strategy, fine-tuning infrastructure, and agentic/risk workflows with evaluations, safety, caching, and production constraints. Founder insight: regulated AI must be reviewable: evidence, policy, approval, case notes, QA, and corrections.

Why him.
StanfordHigh-stakes ML for real-world decisions.
Nooks.aiFounding MLE; shipped applied AI.
StripeFound problem, designed solution, shipped at scale.
Founder | Stripe-scale fraud AI to reviewable fintech workflows
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Pre-seed round
12 · Ask

Next milestone: three benchmarks, one live queue.

We are looking for three design partners with merchant payout/account review queues. Each benchmark uses 100–300 historical cases. Success means one paid fintech benchmark converts into one live human-approved queue.

Round scales money-in-review proof.
$5k+benchmark
100-300cases
3+review queues
Ask | benchmark to live queue to account expansion
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FORESYN01
Vision

Get good money out of review.

Foresyn prepares evidence-backed release / hold / escalate packets for merchant payout and account reviews.

existing tools detecthumans still decideForesyn prepares the packet
Wedge: merchant payout review.Existing tools detect. Humans still resolve. Foresyn prepares the packet.
FORESYN02
Problem

Good funds get trapped in review.

A merchant payout hold is KYB status, risk signals, payout history, disputes, support tickets, ledger state, policy, and prior cases rebuilt manually before a human can release, hold, reserve, or escalate.

7 tabsone hold becomes cross-system work.Backlogs keep good funds held, move risky funds too early, and create inconsistent case notes.
FORESYN03
Packet

One held payout becomes one review-ready packet.

Material facts, cited evidence, policy mapping, missing evidence, similar cases, recommended action, approval route, and draft final case note.

Acme Goods · $42.8k held
Recommended action: release with rolling reserve after bank-account verification.
Evidence
KYB verified, 14 clean payouts, 0.2% dispute rate, support ticket resolved.
Reviewer action
Release / hold / reserve / escalate with draft final case note.
FORESYN04
Existing Line Item

This is already a line item.

Foresyn does not ask buyers to create an “AI workflow” budget. It compresses work already paid for by merchant risk, payments ops, compliance ops, QA, audit, and BPO teams.

Merchant riskrelease / hold / reserve / escalate packet
Payments opsroot-cause and next-action packet
Compliance opsmissing-evidence and approval packet
QA / auditcase-quality packet with source trail
FORESYN05
Today

Reviewers are human middleware.

The expensive work is not flagging the case. It is turning scattered evidence into a defensible release / hold / escalate decision.

Payout queueheld funds, account review, payout exceptionstart
Evidence huntKYB, risk score, PSP, ledger, support, disputes, policy7 tabs
Manual noteanalyst writes rationale and waits for approvalbottleneck
FORESYN06
Product

Foresyn prepares the packet. Humans control the action.

No consequential update happens without human approval.

1. Ingest case
Payout hold, account review, or payment exception enters the queue.
2. Gather evidence
KYB, risk, payout history, disputes, tickets, ledger, policy.
3. Prepare packet
Facts, citations, missing evidence, recommendation, draft note.
4. Human action
Reviewer approves, edits, requests more evidence, or escalates.
FORESYN07
Moat

Every reviewer edit improves the next packet.

Foresyn learns policy interpretations, accepted evidence, escalation patterns, final-note style, and repeated failure modes.

Corrections improve future packets.Each edit teaches what evidence matters, when to escalate, how policy is applied, and how final notes should be written.
FORESYN08
Competition

Not another detector. Not another case system.

Existing tools detect and record. Foresyn prepares the human-approved resolution across them.

Unit21 / Sardinefraud and AML workflows
Chainalysis / TRMwallet and KYT intelligence
Alloy / SumsubKYC and KYB checks
Foresynhuman-approved packet
Where Foresyn wins.When the decision crosses systems and still needs a human-approved record.
FORESYN09
GTM

Benchmark one queue before production.

Buyer: Head of Risk Ops or Merchant Risk. Scope: 100–300 historical payout/account review cases.

Inputpast cases, policies, notes, final decisionsbuyer data
Outputrelease / hold / reserve / escalate packetsscorecard
Measureprep time, evidence completeness, edit rate, escalation quality, QAproof
FORESYN10
Traction

Paid workflow benchmark sold; fintech proof next.

Regtime proves paid benchmark execution. It does not prove fintech PMF.

Provenpaid benchmark, live workflow runtime, first fintech wedge
Nextmerchant payout benchmark, fintech PMF, live queue expansion
$2.5kpaid
$5kcontract
FORESYN11
Founder

Built by a Stripe Risk & Applied ML engineer.

At Stripe, Artemii worked on FM/LLM strategy, fine-tuning infrastructure, and agentic/risk workflows with evaluations, safety, caching, and production constraints. Founder insight: regulated AI must be reviewable.

StanfordStanfordhigh-stakes ML
Nooks.aiNooks.aifounding MLE
StripeStripereview pain at scale
FORESYN12
Ask

Next milestone: three benchmarks, one live queue.

We are looking for three design partners with merchant payout/account review queues. Success means one paid fintech benchmark converts into one live human-approved queue.

$5k+benchmark
100-300historical cases
3+review queues
Round scales money-in-review proof.Benchmark to live queue to account expansion.