Investor White Paper
Intelletto.ai is the hiring sidecar that performs Data Fusion—résumés, AVAR audio/video, LinkedIn and public profiles, plus Applicant Tracking Systems(ATS)/HRIS context—into an explainable Role-Compatibility Score. Recruiters get hand-picked shortlists in hours, not weeks, with audit trails and human-in-the-loop control—no rip-and-replace. Unit economics: ~$10+ net savings per résumé at standard assumptions, delivering immediate payback and compounding productivity at volume. Target beachhead: high-volume BPO/Shared Services for fast, defensible impact.
Index
Vision
Intelletto.ai makes enterprise hiring clear, fast, and defensible. Sitting beside existing systems as an AI sidecar, we parse, normalize, and dedupe résumés, fuse them with AVAR signals from screens and interviews, and deliver an explainable Role-Compatibility Score—so recruiters get hand-picked shortlists in hours. We begin where volume and fairness matter most—BPO/Shared Services—proving value as a stand-alone portal, then embedding for scale. Our model—transactional per-résumé plus enterprise licensing—aligns price with value and expands margins as we optimize parsing pipelines, embeddings, and retrieval. The outcome is repeatable quality, full decision traceability, and measurable ROI.
What is Intelletto.ai
Intelletto.ai is an embedded AI sidecar—an attach layer that sits beside your ATS, HCM, and CRM via APIs and event hooks. It listens to business signals, reads only the minimum context with least-privilege access, applies policy-as-code, and writes back recommendations with rationale, confidence, and evidence. The result is no rip-and-replace and weeks-to-value inside the tools your teams already use.
Why it’s different
- Embedded, not replaced: your system of record remains the source of truth; zero change fatigue.
- Audit-ready by default: versioned prompts/models, immutable logs, exportable evidence packs.
- Closed-loop learning: 30/90/180-day outcomes are written back to sharpen signals and reduce bias.
- Governed by design: RBAC/ABAC, least-privilege access, encryption in transit/at rest.
- One architecture, many domains: consistent control plane for Talent, Workforce, and Revenue.
How the sidecar works
Listen → Read → Reason → Recommend → Act → Learn
- Event-driven: reacts to “application submitted”, “case updated”, or “opportunity stage changed”.
- Minimal read: ingests only necessary context via scoped APIs.
- Reasoned outputs: LLMs + policy-as-code produce explainable recommendations with guardrails.
- Human-in-the-loop: Approve / Edit / Decline with inline rationale and evidence.
- Write-back & replay: decisions and explanations saved with correlation IDs for audit and rollback.
- Outcome-linked learning: outcome data recalibrates prompts/weights to improve precision weekly.
Initial domains powered by the sidecar
Résumé Parsing & Scoring
Universal intake → deduped, clean profiles → explainable Role Compatibility Score with drill-downs.
- Faster shortlists; fewer false negatives/positives
- Reason codes with evidence (audit-ready)
- Closed-loop learning at 30/90/180 days
AVAR — Virtual Automated Recruitment
Video résumés, adaptive assessments & simulations with a transparent Composite/Role Fit.
- Faster time-to-hire; better quality of hire
- Bias monitors & audit trails; human-in-the-loop
- Recruiter command center & candidate portal
Perfect Match — Outcome-Linked Intelligence
Tie predictions to real outcomes; refresh scores and reason codes continuously.
- Outcome feedback at 30/90/180 days
- Exportable audit & fairness reports
- Plugs into ATS/HCM (no new portal)
AI Workforce Intelligence
Skills radar, readiness & growth nudges with wellbeing signals and completion likelihood.
- Shorter, continuous review cycles
- Readiness forecasting; burnout risk signals
- Real-time feedback loops
Customer & Revenue Intelligence
Instant lead profiles (<60s), next-best-action & opportunity health inside your CRM.
- Prioritize right accounts at the right time
- Revenue leak detection; churn & upsell signals
- Tighter, explainable forecasts
Financials At‑A‑Glance
Executive Summary
ntelletto.ai is not another HR tool. It’s a sidecar—an intelligent companion that sits beside your stack and makes hiring obviously faster, fairer, and easier to explain.
Here’s the idea: we take messy inputs—résumés, video screens, live recruiter feedback—and turn them into clean, explainable decisions. Time-to-shortlist drops from days to hours. Every call is traceable. Humans stay in the loop where it matters, with guardrails that regulators understand.
And it gets better on its own. 30/90/180-day outcomes feed back into the system so next week’s shortlist is smarter than last week’s. That’s not a feature; that’s a flywheel.
We land where scale hurts most: BPO/Shared Services sites hiring by the hundreds or thousands a month. Speed wins the first deal. Conglomerates keep us for the long run: we attach beside SAP or Workday—no rip-and-replace, no schema surgery—so security signs off, and trust compounds across BUs.
Simple. Fast. Explainable. Compounding.
That’s the promise—and the plan.
Seed Plan & Investor Takeaway
Our seed plan is intentionally pragmatic: ship fast, validate with high-volume customers, convert pilots to paid MRR, then embed where enterprises already work. With $200,000 in seed capital, we target a live portal by Month 4–6, an SAP sidecar connector by Month 7–8, and a run-rate of $10K–$15K MRR anchored by BPOs, e-commerce/logistics, and a first conglomerate pilot.
Investor takeaway: capital-efficient, fast to traction, and architected for enterprise upsell via sidecar integration.
Problem
Enterprises depend on ATS, HCM, and CRM systems to manage talent and customers. But these systems have three persistent flaws that customers remind us of:
- Messy and duplicate data. Résumés arrive in different formats, from multiple channels, and often repeat. Recruiters spend hours cleaning, formatting, and reconciling—time that adds no value.
- Opaque recommendations. Hiring managers see scores or matches but rarely know why. This lack of explainability erodes trust, slows decisions, and exposes risk with regulators.
- No feedback loop. Once a candidate is hired or rejected, the system doesn’t learn. Signals drift, bias persists, and outcomes stay static. The same mistakes are repeated quarter after quarter.
The result is wasted time, lower trust, and higher risk. Managers don’t trust the outputs. Regulators don’t accept them. Boards can’t defend them. Customers want the opposite: fewer clicks, better shortlists, clear reasons, and audit receipts.
Product & Domains
Intelletto.ai is a single sidecar control plane that attaches to systems of record and powers multiple domains. Each module is designed to do one thing well, deliver value quickly, and improve with use. We don’t replace platforms. We make them more trustworthy and efficient.
Parsing & Scoring
- Universal intake removes duplicates and normalizes candidate data.
- Role Compatibility Score explains why a candidate matches, with reason codes and drill-downs.
- Outcomes at 30/90/180 days feed back into the model to sharpen results.
AVAR (Virtual Automated Recruitment)
- Video résumé capture, transcription, and adaptive assessments.
- Behavioral simulations and composite fit scores.
- Recruiter Command Center for bulk actions and a Candidate Portal for transparency.
Perfect Match
- Closed-loop intelligence that links early predictions to on-the-job outcomes.
- Exportable fairness reports and audit receipts for compliance teams.
AI Workforce Intelligence
- Skills radar, readiness scoring, and development plans tied to outcomes.
- Well-being signals such as sentiment and burnout risk, measured with privacy constraints.
Customer & Revenue Intelligence
- Instant customer profiles, next-best-action prompts, and relationship health scores.
- Churn detection, upsell signals, and forecast confidence checks.
The same architecture powers all of these. That consistency lowers complexity, reduces cost, and speeds adoption. Customers don’t have to learn a new system. They just get better results from the ones they already use.
Market Opportunity
The near-term opportunity clusters around employee-dense segments where résumé volume and recruiter time are under pressure. When hiring is measured in hundreds or thousands per month, speed and defensibility win. We convert volume → signal, process → outcomes, and pilots → platforms.
Focus #1 — BPO / Shared Services (primary beachhead)
- Why now: BPO is a $300B+ global engine. The category is on track to ~$525B by 2030—proof that enterprises are still externalizing workflows and now expect AI-augmented outcomes, not just lower cost. In the Philippines alone, the IT-BPM sector closed 2024 at $38B revenue and 1.82M jobs, underscoring sustained demand for faster, defensible hiring at scale.
- What we change: We cut time-to-shortlist from days to hours, stabilize quality with explainable scoring, and preserve human-in-the-loop for regulatory confidence. That turns perpetual backfilling into a measured, compounding loop tied to business outcomes.
Focus #2 — Conglomerates / Large Enterprises (expanded)
The reality: Conglomerates don’t lack software; they’re rich in systems-of-record (SAP, Workday, Oracle). What slows them is change friction—security reviews, data-governance approvals, and the risk of destabilizing core HR. The winning move isn’t “rip and replace.” It’s a sidecar: attach intelligence beside the core, prove value fast, and scale without breaking trust.
Installed-base tailwind (why this is big):
- SAP SuccessFactors is used by 10,000+ customers across 200+ countries—meaning our SAP connector can address a massive, standardized footprint.
- Workday reports 6,200+ HR customers worldwide—another large, uniform surface for the same sidecar pattern.
What we change (the Jobs-style clarity):
- We keep the core clean. No schema surgery. We read via SAP OData (V2/V4) and Workday RaaS/REST, and we write outcomes only where policy allows. The system of record stays the system of record.
- We make signals portable. Parsed, de-duplicated, explainable Role-Compatibility Scores flow back to HR and hiring managers—traceable to features and sources.
- We wire the loop. 30/90/180-day outcomes feed continuous recalibration, so Week 12 shortlists are smarter than Week 1.
- We pass the audit. Full decision trace, policy-as-code for data access, and HITL gates on sensitive steps.
Strategic Objectives (6–8 Months)
We don’t boil the ocean. We ship, learn, and lock in revenue.
- Stand-Alone MVP (M0–M4)
Upload → Parse → Deduplicate → Explainable Score → Export shortlist.
Acceptance bars: Time-to-shortlist in hours, Role Fit ≥ 85%, audit log on by default. - BPO Pilots Live (M3–M4)
Two sites in PH/IN, production résumés, recruiter feedback loops, weekly KPI reviews.
Show it matters: resumes-per-hire trending to 15–25; recruiter hours per hire dropping. - Weekly Calibration Loop (from M4)
Close the loop with 30/90/180-day outcomes; ship weekly model updates; monitor drift/bias; keep HITL gates on sensitive steps. - Monetize (M5–M6)
Convert pilots to paid.
Optional surge lane: add e-commerce/logistics roles during seasonal spikes to prove elastic scale. - SAP Sidecar (M6–M8)
Embed explainable scoring into SuccessFactors Recruiting events and recruiter flows (read via OData, write outcomes where policy allows).
One conglomerate BU live; security/governance approved; audit pack published. - 12-Month Terms (M6–M8)
Convert pilots to annual contracts with capacity tiers and outcome-linked incentives (time-to-shortlist targets, early-attrition deltas). - Financial Target (M8)
Exit the period at $10K–$15K MRR and ≥1 SAP pilot in production.
Capital Allocation ($200,000)
- Product & Scoring (60% — $120,000)
Ship the stand-alone MVP and the core scoring pipeline: résumé parsing/normalization/deduplication, Role-Compatibility Score with explanations, AVAR (audio/video) signal ingestion, recruiter portal, sidecar APIs, SAP SuccessFactors connector (read via OData; write outcomes where policy allows), test harnesses, and end-to-end observability.
- Cloud Infra & Tools (20% — $40,000)
Usage-scaled workloads for inference and document understanding (e.g., Bedrock, Textract), vector/metadata store and retrieval (e.g., OpenSearch), CI/CD and monitoring. Optimized batching/caching to hold ≤ $0.03–$0.05 per résumé infra target.
- Compliance & Data (5% — $10,000)
SOC2-lite controls, GDPR/PDPA readiness and DPIA, curated/licensed datasets for benchmarking, and an audit pack with policy-as-code and decision trace.
- Operations & Founder Draw (15% — $30,000)
Lean ops (billing, bookkeeping, domains, minimal tooling), pilot support (PH/IN coordination), and minimal founder draw (~$3K/month shared).
8-Month Cash-Flow Projection
- Founder-led sales and revenue starting Month 5 with a ramp to $10–15K MRR by Month 8. Don’t pull revenue forward; credibility > optimism.
- Front-loaded compliance (~$10K) and usage-scaled cloud spend. These make the “governance-ready, pay-as-you-grow” narrative real.
- Runway preservation (~$26.5K after M8). Keep this cushion to de-risk the SAP pilot period.
Assumptions
- Founder‑led sales (no sales salaries).
- Minimal founder draw ~ $3K/month shared.
- Cloud spend scales with pilot volume.
- Compliance/data costs are front‑loaded (≈ $10K total).
- Revenue starts Month 5, ramping to $10K–$15K MRR by Month 8.
| Month | Product Dev | Infra/Tools | Compliance/Data | Ops & Founder Draw | Revenue In | Net Monthly Burn | Cumulative Balance |
|---|---|---|---|---|---|---|---|
| Start | – | – | – | – | – | – | 200,000 |
| M1 | 25,000 | 5,000 | 5,000 | 3,000 | 0 | 38,000 | 162,000 |
| M2 | 20,000 | 5,000 | 5,000 | 3,000 | 0 | 33,000 | 129,000 |
| M3 | 20,000 | 6,000 | – | 3,000 | 0 | 29,000 | 100,000 |
| M4 | 20,000 | 6,000 | – | 3,000 | 0 | 29,000 | 71,000 |
| M5 | 15,000 | 7,000 | – | 3,000 | 5,000 | 20,000 | 51,000 |
| M6 | 10,000 | 7,000 | – | 3,000 | 7,500 | 12,500 | 38,500 |
| M7 | 7,500 | 8,000 | – | 3,000 | 10,000 | 8,500 | 30,000 |
| M8 | 7,500 | 8,000 | – | 3,000 | 15,000 | 3,500 | 26,500 |
Highlights: Runway preserved with ≈ $26.5K after Month 8; revenue climbs M5 → M8; spend weighted to shipping MVP, de‑risking compliance, and enabling SAP connector.
- United States: ~50k companies (conservative floor: 19.7k; plausible range: 20k–77k).
- Europe (EU‑27): ~53k companies (large enterprises, ≥250 employees).
- Asia: rough order‑of‑magnitude 60k–140k companies (includes Australia’s 5.2k ≥200‑employee firms, plus Japan/Korea/SEA/India/China large‑firm cohorts).
| Industry / Function | Average Resumes Reviewed per Hire | Notes |
|---|---|---|
| High‑volume BPO / Contact Center | 15–25 | Screening automation & skills filtering common; higher throughput roles. |
| IT / Software Engineering | 50–100 | Technical tests & cultural fit add friction; often <2% hire rate. |
| Data Science / AI Roles | 100–150 | Complex multi‑stage evaluation, limited qualified pool. |
| Finance / Banking | 40–60 | Conservative selection; higher compliance & background checks. |
| Sales / Marketing | 30–50 | Balance between experience and behavioral alignment. |
| Executive Search | 150–300 | Passive candidates dominate; manual curation remains high. |
| Average across industries | ≈ 40–60 |
Costing Table by Number of Clients
| Clients | Resumes / month | Human time (HH:MM) | Human cost (USD) | Cost — Parsing (USD) | Cost — Scoring (USD) | Selling — Parsing (USD) | Selling — Scoring (USD) | Gross Profit (USD) |
|---|
Founders & Team
With 40 years of leadership in emerging-market financial services across the Middle East, Asia, Russia, Europe, and Africa, Gerrit Heyns has held pivotal roles at marquee global institutions such as Troika Dialog, J.P. Morgan, Lehman Brothers, and Kleinwort Benson. His operational depth spans market strategy, trading infrastructure, and regulatory navigation during economic cycles. Complementing this background is academic rigor—an International Finance master’s and a finance bachelor’s—ensuring both strategic insight and quantitative grounding.
Advisory & Governance Board: Post-bank career, Gerrit co-founded a sustainability-driven investment firm in 2009, cementing his reputation in principled finance. He is a recognized voice in sustainable investment, blending environmental stewardship with financial discipline. This unique mix makes him ideally suited to guide a mission like Intelletto.ai’s—one that emphasizes explainability, governance, and trust in AI-powered workflows.
Scott B. Darrow is a globally experienced Chief Technology Officer with over four decades of leadership in digital transformation. He has architected enterprise systems across FinTech, eCommerce, ESG, blockchain, and AI-powered platforms. His track record includes designing 36 modular ERP, SCM, and MRP systems across 28 industries; leading Build–Operate–Transfer (BOT) operations in the Philippines and Japan; modernizing sustainability-aligned platforms recognized by Oracle; and deploying AI in core HR, recruitment, and operational systems at scale.
Operational Core: As an operational technology strategist, Scott combines strategic vision with execution: redesigning legacy systems into federated, AI-native architectures such as SuperApp ecosystems that reduced onboarding time by 40%, recruiter effort by 25%, and accelerated delivery across engineering squads. He has embedded AI-first infrastructure into talent, performance, and wellbeing systems, and launched AI-augmented “super apps” for mobile, micro-front-end orchestration, and big data analytics.
Julio Endara, a founder of technology-enabled professional services and offshoring businesses, has led end-to-end delivery across EMEA, the Americas, and APAC. His career includes senior leadership at a Manila-based outsourcing group focused on recruitment, staffing, and software services. Earlier roles span management consulting at a global firm (UK base) with multi-region engagements, followed by operating positions blending business development with hands-on execution.
For Intelletto.ai, Julio’s value is practical and immediate: structuring KPI-gated pilots, packaging BOT/managed services around the sidecar, and aligning delivery playbooks to enterprise expectations on cost, quality, and speed.
Richard Mills is a senior business-development leader with two decades of experience building C-suite networks and enterprise relationships across the Philippines and broader APAC. He chairs a national business events platform that convenes thousands of senior leaders annually and has delivered keynote addresses across HR/BPO and executive-compensation forums—translating reach into qualified enterprise pipeline and sponsorship/partner revenue. Richard also co-founded an executive-search firm serving multinationals across Southeast Asia.
Rebecca Bustamante is a founder-operator who scaled one of the country’s largest executive forums and co-founded a regional executive-search firm. She runs high-frequency event programs and multi-channel outreach that consistently attract senior decision-makers—turning stage presence and community into targeted account engagement and warm introductions. Rebecca leads top-of-funnel creation (events + media), partner marketing, and executive briefings tied to KPI-gated pilots and governance-ready rollouts.
Borja Hernández Celorio is a founder–operator with deep experience building outsourced engineering and DevOps organizations, as well as leading mobile/SaaS delivery practices. His background spans standing up distributed teams, instituting modern CI/CD and SRE guardrails, and delivering multi-tenant products for enterprise clients.
For Intelletto.ai, Borja brings pragmatic “factory floor” discipline to an explainable-AI control plane: codifying playbooks for KPI-gated pilots, right-sourcing (in-house vs partner), and hardening the sidecar’s operational perimeter—observability, change management, and incident response.
Resources
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Parsing & Scoring — White Paper
Universal intake, de‑duplication, explainable scoring, and role compatibility.
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AVAR — White Paper
Video résumés, adaptive assessments, simulations, composite fit score.
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Perfect Match — White Paper
Outcome‑linked intelligence with 30/90/180‑day feedback.
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AI Workforce Intelligence — White Paper
Skills radar, readiness scoring, continuous feedback, well‑being signals.
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Customer & Revenue Intelligence — White Paper
Lead profiles, next‑best action, relationship health, forecast confidence.