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%
Survey-alignment accuracy
Lab-validated in controlled studies
Why Now · Market Timing
Three market forces make this the right moment
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.
Real Workflow Examples
Early workflow integration signals, not yet statistical proof
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.
Real workflow examples, not audited case studies
These examples are based on active client and pilot engagements. They are shown to illustrate how Predikta is being used today, not as audited large-sample proof of commercial prediction accuracy.
Real Workflow Example
Consumer Finance: Retrospective Campaign Validation Before Broader Deployment
Leading consumer finance company · Marketing workflow validation
Challenge: A leading consumer finance company needed a way to evaluate whether Predikta could fit real campaign decision workflows without requiring full production deployment upfront.
Workflow: Predikta was used in a pilot setting to assess decision impact, operational value, and alignment with marketer judgment. The documented next step was to compare Predikta outputs against historical campaign performance data, using retrospective campaign details provided by the client.
Why this matters: This is a real consumer-finance validation workflow. It supports campaign validation and creative-screening use cases, but should not yet be framed as a completed prospective prediction case study.
Stage 1
Validation completed
Stage 2
Historical comparison pending
Real Workflow Example
Beauty / FMCG: Pre-Testing Creative Before Media Spend
Leading wellness brand via agency partner · Live campaign workflow
Challenge: A leading wellness brand and its agency partner needed a way to refine campaign briefs, test creative directions, and improve copy before launch, while staying within workflow and compliance constraints.
Workflow: Predikta was integrated into the brand's campaign process to support audience insighting, recommended tone and approach, ad-copy testing, benchmarking, and iteration prior to launch.
Why this matters: The documented workflow includes simulation, refinement, benchmark scoring, and eventual A/B testing after deployment. This is a real live commercial workflow, but not yet a quantified ROI case study.
Pre-launch
Creative testing
Benchmarks
Iteration support
Scientific Foundation · What Has Been Validated
Honest assessment: Lab validation complete, real-world validation in progress
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
Raising $1.5M Seed · $6M Valuation Cap
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.
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
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
Three conditions must hold for venture-scale outcomes
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, 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 Softbank,Genoa Ventures,Xeraya Capital). University of the Philippines MPA.
Jason Albia, MS
CSO & CTO & Co-Founder
15+ years across interdisciplinary science, R&D, and startup ventures. Secured about $4M in aggregate funding for AI-driven R&D projects and co-authored 40+ scientific papers. Holds degrees in Computational Science and Applied Physics, and has built and led multidisciplinary research teams applying AI and computational methods to data-driven industries.
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.
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