Pre-Launch Decision Testing

Predict audience response before launch

Predikta helps brands and decision-makers test messages, campaigns, and concepts using localized behavioral simulations in days instead of weeks. Built on psychographic modeling of Filipino consumers and validated in controlled studies, Predikta is already being used by 3 paying clients as a faster, lower-cost alternative to traditional research. This begins as a research replacement workflow and can evolve into broader decision infrastructure if real-world validation continues to hold.

Today, customers use Predikta to test campaign concepts, messages, and creative directions before spending on media or surveys.

⚡

10x

Faster than traditional research

Results in hours vs. 3-4 weeks

💰

90%

Lower cost vs. surveys

₱10-15K vs. ₱100-120K per study

🎯

88%

Sentiment accuracy

Lab-validated prediction

Why Now · Market Timing

1. Campaign velocity has outpaced research cycles. Brands now iterate campaigns weekly, but traditional consumer research still operates on 3-4 week cycles at ₱100-120K per study. The gap between decision speed and validation speed creates willingness to adopt simulation-based alternatives.

2. The shift from retrospective to prospective is now possible. Standard AI tools analyze what happened after the fact. Predikta models what may happen next. This shift from retrospective analysis to prospective simulation became viable because foundation models can now encode nuanced human psychology at scale — something impossible 24 months ago.

3. The earliest players to collect localized validation data may build a meaningful accuracy advantage. Every validated simulation strengthens future predictions. If this compounds as we expect, competitors starting later face a cold-start problem. The window to establish this data advantage in Southeast Asia is open now.

Proof: Pattern, Not Anecdote

Across 3 paying clients, Predikta has been used multiple times per client — not one-off pilots, but repeat usage replacing internal research workflows. GlutaMAX uses Predikta before every campaign launch. thynkertech integrated it into their brand strategy process. UNBOX Philippines runs simulations for each content planning cycle. This is workflow adoption, not demo testing. Sample size is still small (n=3 clients, ~15 total commercial simulations), but the pattern is clear: once clients validate accuracy in their first use case, they return for subsequent decisions. This is the behavior pattern we need to scale.

Customer Example #1

Consumer Finance: Selecting the Strongest Campaign Concept Before Launch

Major SEA fintech company (5M+ users) · Loan product messaging validation

Challenge: Client had 3 competing campaign concepts for a new loan product. Traditional research (focus groups + survey) would cost ₱120K and take 4 weeks — too slow for agile campaign iteration.

Solution: Used Predikta to simulate 10,000 synthetic personas matching target demographics. Tested all 3 concepts for sentiment, trust, and intent. Completed in 48 hours for ₱15K.

Validation: Predikta identified Concept B as the strongest option (72% positive sentiment). The client then ran an n=200 survey, and the result came in within 3 percentage points of the prediction. The selected concept was used in a campaign with a ₱2M media budget.

3.5wk

Time saved

₱105K

Cost saved

±3%

Prediction accuracy

₱2M

Media budget informed

Customer Example #2

Beauty Brand: Message Variant Selected by Predikta Correlated With 18% Higher Campaign Performance

Philippine beauty/wellness brand · Social media campaign optimization

Challenge: Client planning ₱800K social media campaign. Wanted to test 5 messaging angles but traditional A/B testing requires significant media spend upfront.

Solution: Simulated audience response to all 5 variants using 5,000 synthetic personas. Predikta identified clear winner: "dermatologist-recommended" angle scored 68% vs. 45-52% for alternatives.

Outcome: Predikta identified the strongest message variant before launch. The campaign that used this variant delivered 3.2% conversion versus a 2.7% historical baseline, or roughly 18% higher performance. Attribution remains directional rather than fully isolated, since seasonality, creative execution, and channel mix may also have contributed.

18%

Performance lift

Messages tested

₱0

Media for testing

Turnaround

Scientific Foundation · What Has Been Validated

What we've proven (arXiv:2505.22125v1): Predikta achieves 95% trait alignment with real humans in psychographic modeling and 88% accuracy in consumer sentiment simulation. Study used 2,485 nationally representative Filipino respondents across 12 consumer scenarios. This validates that our psychographic modeling framework (HEXACO + Schwartz Values) can simulate survey-style sentiment with statistical significance (p<0.0001, Wilcoxon test). This is survey replication accuracy, not yet real-world behavioral prediction accuracy.

What we're testing now (200+ campaign validation): Whether simulation outputs align with observed real-world campaign outcomes — not just survey responses, but actual market performance (conversion rates, engagement metrics, sales lift). This is the critical de-risking milestone. Results expected Q2 2026. Success = directional alignment above chance baseline. This will be the first peer-reviewed test of commercial prediction accuracy.

What clients see today: In pilot deployments, Predikta predictions matched real follow-up survey results within ±3 percentage points (fintech case: predicted 72%, actual was 75%). Sample size is small (n=3 commercial validations). Not yet statistically robust, but directionally consistent with lab results. This gives us confidence the 200+ campaign validation will succeed, but we're explicit that this remains unproven at scale.

Investment Opportunity

Strategic backing from Globe/917Ventures (term sheet signed, follow-on option). Current: ₱70K MRR / ₱840K ARR from 3 paying clients. 5 active pilots (Home Credit, Globe, AXA, AdSpark, Inquiro) plus strong enterprise pipeline across FMCG, tech, banking, and education. Near-term focus on converting pilots into repeatable revenue. Funds will be used to hire enterprise sales capacity (2 AEs + 1 CS), expand localized behavioral datasets to additional SEA markets, and build self-serve features that improve margins and reduce cost-to-serve. This round is designed to prove repeatable commercial demand and prepare the business for a disciplined Series A raise.

Current Traction & Metrics

ACV Range

₱10-60K

/month

Based on 3 paying clients

Pilot → Paid

2/5

converted (60d)

3 pilots still active

Sales Cycle

30-60d

first call → close

Median 45 days

CAC / Payback

₱35K

~2 mo payback

Early data, will evolve

Why This Could Become Hard to Replicate

1. Behavioral Validation Feedback Loop

Accuracy May Compound With Usage

Every client simulation generates validation data when we compare predictions to real outcomes (campaign results, survey data, conversion metrics). If this feedback loop strengthens accuracy as expected, it creates an advantage: better predictions attract more usage, more usage generates better training data. We're 18 months into this cycle in the Philippines. Competitors starting today would face a cold-start problem. This is a testable hypothesis, not yet a proven moat.

2. Localized Psychographic Datasets

Regional Cultural Calibration Takes Time

We built psychographic modeling framework trained on 2,485 nationally representative Filipino respondents (via UP partnerships), scaled to represent 68.9M population via census integration. Expanding to additional Southeast Asian markets over the next 18-24 months. Each market requires 18-24 months: local survey data collection, university partnerships, cultural calibration, validation studies. Western tools lack this infrastructure. This creates a time-based advantage if we execute consistently.

3. Cultural Psychology Barrier

Localization Is Not Translating Prompts

Filipino decision-making differs from Western models (collectivism, family influence, risk aversion, religious context). Our HEXACO + Schwartz Values implementation is culturally recalibrated for Southeast Asian psychology. Replicating this requires partnerships with local behavioral researchers and 2+ years of validation work. This is behavioral science infrastructure, not prompt engineering. Advantage persists if local calibration continues to show accuracy gains.

4. Strategic Partnership

Strategic Access Through Globe/917Ventures

Strategic shareholder support provides three practical advantages: access to relevant portfolio companies as design partners and early customers, faster introductions into enterprise decision-makers within the Globe network, and potential collaboration opportunities around behavioral data and validation. This does not guarantee distribution, but it materially improves our speed of learning and access compared with pure cold outreach.

Customer Pipeline & Segments

Current Pipeline

₱70K

MRR (3 Clients)

Active Pilots

30+

Product Testers

Paying Clients (3)

thynkertech — AI product studio (₱30K/mo)
UNBOX Philippines — Tech media (₱15K/mo)
GlutaMAX — Beauty brand (₱25K/mo)

Active Pilots (5)

Home Credit — Consumer finance
Globe — Telco (2 pilots running)
AdSpark — Marketing agency
Inquiro — Research firm
AXA — Insurance

Qualified Pipeline

Metrobank — Banking
MAPÚA — Education
LENOVO, HONOR, OPPO, Xiaomi — Consumer tech
Kojie-san, Century Pacific — CPG/FMCG
DDB Group — Ad network

Expansion Path · If This Works

Believable wedge-to-expansion path

Today · Marketing & Campaign Testing

Current wedge: Replace traditional market research for pre-campaign testing, message optimization, and creative validation. Target: agencies, brands, research firms in Philippines + SEA.

This is where we are now: 3 paying clients (₱70K MRR), 5 active pilots across finance, tech, and insurance, and early signs of repeat workflow adoption. The immediate goal is to prove repeatable commercial demand in this wedge.

Next · Product & Strategic Decisions

Natural expansion: If campaign validation holds, product teams will want the same capability for testing features, pricing strategies, and UX decisions before building. Target: product orgs, founders, VCs doing diligence.

Revenue model evolves: Enterprise licenses (₱280K-1.4M/mo) + strategic consulting. Requires stronger evidence of prediction accuracy before this market opens.

Later · Risk & Institutional Decisions

High-value expansion: Credit risk modeling, insurance underwriting, loan product design. Simulate borrower behavior before capital deployment. Target: banks, fintechs, insurers, PE firms.

This requires: Multiple years of validated prediction accuracy in lower-stakes categories first. Not credible to pitch this now — but possible to build toward if methodology proves robust.

What Has To Be True For This To Work

1. Simulated outputs must continue to show directional alignment with real-world decisions. If the 200+ campaign validation succeeds (Q2 2026 results) and shows Predikta predictions align with observed outcomes above chance baseline, that proves the methodology works commercially. This is testable and falsifiable. If validation fails, the category thesis weakens significantly.

2. Customers must use the product repeatedly, not just experimentally. Early signals are positive: all 3 paying clients have run multiple simulations, integrating Predikta into recurring workflows. But sample size is small. Scaling from 3 to 25+ clients while maintaining repeat usage is the key commercial validation. One-time pilots don't build a business. Workflow replacement does.

3. Localized datasets must produce a meaningful accuracy advantage over generic global AI tools. If culturally-calibrated Philippine models consistently outperform generic LLM prompting, that proves the localization investment creates defensible value. If accuracy converges, the moat weakens. This is an empirical question we can measure.

Next 12 Months

2026 Milestones

Conservative targets based on current pipeline + realistic conversion

Q1 2026 (LIVE)

Foundation

3 paying clients (₱70K MRR)
5 enterprise pilots running
Globe/917V term sheet signed
arXiv paper published

Q2 2026

Scale Sales

Target: 8-10 total clients
Convert 2 pilots to paid
Hire 1 AE + 1 CS lead
Launch image input (v2.0)

Q3 2026

Product + API

Target: 10-12 total clients
API beta for agencies
Client data integration
SEA market research begins

Q4 2026

Series A Prep

Target: 15-20 total clients
₱150-250K MRR
First SEA expansion dataset
Series A materials ready

Team

Founding Team + Advisors

Interdisciplinary founding team: Statisticians (via UP partnership), computational and AI engineers, behavioral scientists, and serial entrepreneurs. This cross-functional depth is rare at seed stage and critical for building behavioral infrastructure that combines rigorous science with commercial execution.

Axel Kornerup, MPA

CEO & Co-Founder

26+ years building companies. Founded 4 ventures: netopia (PH internet café chain, acquired), netVoice, netGames, netSolar. Part of founding team at InterVenn Biosciences (AI liquid biopsy, raised $100M+ from Catalio, DCVC, Khosla). Harvard Kennedy School MPA.

Jason Albia, MS

CSOO & Co-Founder

MS in AI systems. Previously led technical operations for enterprise SaaS at scale. Deep expertise in psychometric modeling, ML pipelines, scientific validation. Owns product architecture, research partnerships, and technical roadmap.

Advisory Board

Active Advisors

Aldo Carrascoso (InterVenn, >$1B outcomes) — fundraising + US expansion · Jojo Flores (Plug and Play, $10B+ portfolio) — enterprise BD + VC intros · Jane Walker (ex-PLDT/Singtel/San Miguel) — SEA partnerships

Research Partners

Academic Collaboration

University of the Philippines (Los Baños Statistics, Diliman Psychology) — co-authors on published research, ongoing collaboration on dataset expansion and validation studies.

The Ask

We are raising $1.5M to prove repeatable commercial demand

This round funds the next de-risking phase of the company: converting active pilots into recurring accounts, building repeatable enterprise sales capacity, expanding the behavioral dataset into additional Southeast Asian markets, and shipping self-serve product features that improve scalability. Over the next 18 months, our goal is to demonstrate repeat usage, stronger commercial validation, and evidence that localized datasets create a durable product advantage in Southeast Asia.

📧 info@netopia.ai 🌐 netopia.ai 🎯 predikta.ai 🎥 Watch Demo

Use of Funds

Sales & Success

35%

of raise

2 AEs + 1 CS hire

Product & Eng

30%

of raise

Self-serve features

Data Expansion

20%

of raise

Additional SEA markets

Operations

15%

of raise

Runway buffer