Not another tool.
An intelligent advantage.
An embedded, explainable AI sidecar that attaches to the ATS, HCM, and CRM systems enterprises already run — delivering auditable intelligence, closed-loop learning, and weeks-to-value. No rip-and-replace. No change fatigue. Decisioning that boards, managers, and regulators can all trust.
Index
- What is Intelletto.ai
- Financials At-a-Glance
- Executive Summary
- The Intelletto Difference
- Market Opportunity
- Product & Domains
- The Sidecar Loop
- Workflow Journeys
- Architecture, Security & Governance
- Governance & Explainability
- Competitive Benchmarking
- Parsing & Scoring — Detail
- Costing — Per Résumé
- Go-To-Market & Pricing
- Delivery & Implementation
- Financial Plan & Unit Economics
- The Ask & Use of Funds
- Risk & Mitigation
- Team
Brand & Platform
What is Intelletto.ai
In Italian, intelletto means intellect — the human capacity to understand, exercise judgment, and make sense of complexity. That word was chosen deliberately. What enterprises need is augmented intellect: systems that help recruiters, managers, and executives see patterns, understand the reasoning behind decisions, and move with confidence. Not a black-box oracle. Not a score with no story. Intellect, augmented.
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: 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 stays the source of truth; zero change fatigue.
- Audit-ready by default: versioned prompts and models, immutable logs, exportable evidence packs.
- Closed-loop learning: 30/90/180-day outcomes written back to sharpen signals and reduce bias.
- Governed by design: RBAC/ABAC, least-privilege access, encryption in transit and at rest.
- One architecture, many domains: consistent control plane across Talent, Workforce, and Revenue.
- Regulator-ready: aligned to EU AI Act transparency mandates and PDPA/GDPR obligations.
Three initial domains powered by the sidecar
- Résumé Parsing & Scoring — universal intake, deduplication, explainable Role Compatibility Score
- AVAR — video résumés, adaptive assessments, composite fit score
- Perfect Match — outcome-linked intelligence with 30/90/180-day feedback
Parsing & Scoring
Universal intake → deduped, clean profiles → explainable Role Compatibility Score with drill-downs.
AVAR — Virtual Automated Recruitment
Video résumés, adaptive assessments & simulations with transparent Composite/Role Fit score.
Perfect Match
Tie predictions to real outcomes; refresh scores and reason codes continuously at 30/90/180 days.
Financials At-A-Glance
Overview
Executive Summary
The world's largest enterprises — banks, BPOs, retailers, healthcare networks — run their talent and revenue operations on platforms built before modern AI existed. Workday, SAP SuccessFactors, Oracle HCM, Salesforce, Greenhouse. These are entrenched systems of record. They will not be replaced. But they can be made dramatically smarter.
Intelletto.ai attaches an explainable AI sidecar to those systems. No rip-and-replace. The sidecar listens to events, reads the minimum context, applies policy-as-code, and writes back outcomes with reason codes, confidence scores, and evidence links. Managers see the "why." Regulators see defensible evidence. Boards see decisions they can stand behind.
The timing is structural, not cyclical. The EU AI Act (2024, applying 2025–2026) mandates transparency and human oversight for AI systems in high-risk domains — hiring and HR sit explicitly in Annex III. PDPA frameworks across APAC are tightening data subject rights. And the post-ChatGPT wave of enterprise AI adoption has created both a massive opportunity and a governance gap: tools were deployed before auditability was designed in. Intelletto.ai is the governance layer that regulators are now requiring and boards are demanding.
What we deliver
- Weeks-to-value: shadow mode → gated decisions → safe automation, measurable ROI inside the current quarter.
- Explainability by default: every recommendation includes the "why" — policy checks, confidence, evidence links.
- Governance built-in: least-privilege, RBAC/ABAC, audit receipts, fairness monitors. SOC 2, GDPR, PDPA roadmap.
- Closed-loop compounding: real outcomes at 30/90/180 days recalibrate every recommendation cycle.
- One architecture, many domains: each new module leverages the same platform — marginal cost, maximum leverage.
Financial targets
- ARR: $200K–$400K at exit Year 1 (2–3 paying pilots); ~$1.0–1.5M at exit Year 2 (post follow-on); breakeven at ~$2.5–3M ARR.
- Margins: Gross margin improving from ~45% (Y1) to ~62% at breakeven, driven by model routing and volume discounts.
- Retention: NRR ≥ 120%; GRR ≥ 90% — expansion across domains is the primary growth engine.
- CAC payback: ≤ 12 months; KPI-gated pilots with 40% prepay compress payback further.
The ask
Raising $250K seed (SAFE at a negotiated valuation cap, or priced round by lead preference) to fund first connector builds, KPI-gated pilots, and lean compliance foundations. This extends runway 6–10 months with disciplined spend and 40% prepay target on pilot contracts — enough to prove the model with paying customers and position for a larger follow-on raise. The sidecar is not another tool — it is the intelligence and governance layer that makes existing enterprise systems auditable, defensible, and compoundingly smarter.
The Opportunity
The Intelletto Difference
A résumé parser extracts text from a PDF. It doesn't fix the system. It digitizes the chaos. At enterprise scale — thousands of résumés monthly, scattered channels, inconsistent screening, compliance exposure, and burnout — the problem isn't a lack of candidates. It's a lack of signal. Intelletto.ai goes beyond parsing by turning hiring into an intelligence pipeline built for agility with governance.
The Intelligence Pipeline — Beyond Parsing
The job definition becomes explicit and measurable — success criteria, required outcomes, true skill requirements, and compliance commitments — so the pipeline has a real target, not a vague brief that drifts recruiter to recruiter.
Skills synonyms multiply and drift across regions and teams. Intelletto.ai standardizes terms, maps real-world phrases to canonical skills, and preserves rationale. Without this, search lies and scoring drifts.
Intake at scale is an orchestration problem, not an upload problem. Every résumé arrives with provenance: source, timestamp, campaign linkage, requisition linkage, and full audit history.
Duplicates inflate pipelines, confuse outreach, distort analytics, and increase compliance risk. Intelletto.ai resolves identity across sources with reversible merge history. If you can't trust identity, you can't trust decisions.
Parsing must produce evidence, not just extraction. Skills are linked to proof: where demonstrated, how recent, how confident. That's the difference between keyword matches and defensible shortlists.
Cleaning, normalization, canonical structuring, versioned history, and indexing built for retrieval at volume. This is where "it worked in the pilot" becomes "it works at enterprise scale."
Job intent mapped to candidate evidence. Not a mysterious number — an explainable breakdown: why this candidate fits, where they don't, what's missing, and what risks exist. Speed without trust is chaos. Trust without speed is failure.
The offer is not the end. Intelletto.ai correlates post-hire outcomes back to hiring signals, tunes weights, monitors drift and fairness, and logs governance changes with rollback controls. The pipeline improves over time instead of repeating the same mistakes faster.
"Lots of vendors do AI features. Almost none deliver an enterprise-grade sidecar that is explainable, governable, and deployable beside existing systems without rip-and-replace."
Opportunity
Market Opportunity
There is a moment every large enterprise is hitting right now: AI has been deployed into hiring, screening, and workforce decisions — but the governance infrastructure hasn't caught up. Regulators are arriving. Boards are asking questions. And the clock on EU AI Act enforcement is ticking toward August 2026. Intelletto.ai exists precisely in that gap.
Three forces converging — right now
99% of Fortune 500 now use AI in recruitment. BCG finds 70% of corporate AI experimentation is happening inside HR. Yet more than half of organizations have not established a systematic inventory of the AI systems they operate — the minimum requirement for EU AI Act compliance. The tools are deployed. The governance isn't.
The EU AI Act classified AI used in recruitment and HR decisions as explicitly high-risk under Annex III. Core obligations — human oversight, bias audits, immutable logs, explainability — become enforceable on August 2, 2026. Penalties reach €35 million or 7% of global annual turnover. The regulation has extraterritorial reach: any company using AI that affects EU candidates must comply, regardless of where it is headquartered.
SHRM data shows AI-powered recruitment delivers 31% faster hiring and 50% improvement in quality of hire. But 66% of US adults say they would avoid jobs where AI makes the hiring decision. The same tools that drive efficiency are eroding candidate trust. Explainable AI — showing why, not just what — is the only path to deploying AI at scale without destroying employer brand or accumulating legal risk.
Sizing the addressable market
Intelletto.ai targets the attach-layer opportunity across HR Tech, AI Governance, and Recruitment AI — platforms already in use that urgently need explainability and compliance infrastructure bolted on.
Sources: Fortune Business Insights, Mordor Intelligence, Demandsage, SHRM, BCG 2025. Figures directional.
- AI deployed in hiring, but no audit trail
- Scores surfaced to managers, no "why" attached
- Override decisions lost in email threads
- Bias monitoring non-existent or manual
- GDPR/PDPA rights requests impossible to fulfill
- EU AI Act deadline approaching; readiness unknown
- Immutable decision logs, reason codes, evidence links
- Explainable scores with one-click drill-down
- Every Approve · Edit · Decline captured with actor ID
- Fairness dashboards with demographic parity reporting
- DSAR-ready: full individual decision history on demand
- Exportable compliance bundle, one click
Intelletto.ai is not competing to replace Workday, Greenhouse, or SAP. It is expanding the defensibility of platforms enterprises have already paid for. No switching cost. No migration risk. No retraining budget. The buying trigger is not "we want a new tool" — it's "our board is asking, our regulator is asking, and our August 2026 deadline is six months away." That is not a discretionary purchase. That is a compliance imperative.
Product
Product & Domains
Intelletto.ai is a single sidecar control plane that attaches to systems of record and powers multiple intelligence domains. Each module is engineered to do one thing precisely, deliver value quickly, and improve with use. We do not replace platforms. We make them more trustworthy, more efficient, and audit-defensible.
Parsing & Scoring
Universal intake removes duplicates and normalizes candidate data into structured, comparable profiles.
- Multi-format ingestion: PDF, DOCX, HTML, scanned images via OCR
- Skills taxonomy normalization across job families and seniority levels
- Role Compatibility Score with 6 deterministic scoring buckets plus AI soft-signal inference
- Reason codes with linked evidence — drill-down to exactly which signals drove the score
- 30/90/180-day outcome feedback recalibrates the model automatically
- Fairness monitors and bias dashboards visible to customer and auditor
Production cost: $0.0110/résumé (Standard) · $0.0187/résumé (Premium) — based on live metering with Gemini 2.5 Flash.
AVAR — Virtual Automated Recruitment
Structured video résumés, adaptive assessments, and behavioral simulations with a transparent Composite Fit score.
- Video résumé capture, transcription, and rubric-based AI scoring
- Adaptive question sequencing based on live candidate responses
- Behavioral simulations calibrated to role competency profiles
- Composite Fit Score with bias monitors and audit trail
- Recruiter Command Center for bulk actions, shortlist management, and override capture
- Candidate Portal for transparency — candidates see how they were assessed
Perfect Match
Closed-loop intelligence that links early predictions to on-the-job outcomes, keeping scores current and grounded in reality.
- Outcome feedback loops at 30/90/180 days — actuals fed back into scoring weights
- Exportable fairness reports and audit receipts for compliance teams and external regulators
- Plugs directly into ATS/HCM — no new portal, no recruiter retraining
- Counterfactual analysis: "what would this candidate have scored under alternative criteria?"
- Continuous confidence recalibration as market conditions shift
The same architecture powers all three domains. This consistency lowers engineering complexity, reduces COGS, and accelerates adoption. Customers don't need to learn a new paradigm — they simply get better, defensible results from the systems they already own.
How It Works
The Sidecar Loop — Six Steps, One Closed System
The intelligence loop is the operating model. Every event that passes through the sidecar follows the same six-step cycle. Each turn of the loop makes the next recommendation more accurate, more fair, and more grounded in your organization's specific context.
Reacts to key business events as they occur: "application submitted," "case updated," "opportunity stage changed." Event-driven architecture means zero polling overhead and sub-second reaction time.
Pulls only the minimum, relevant context from your systems via scoped APIs with least-privilege access. Only what the reasoning step needs — nothing else — is ever fetched.
Combines large language model inference with your policy-as-code so every decision reflects both intelligence and governance. Guardrails are not an afterthought — they are the reasoning layer.
Produces human-readable guidance with explicit rationale — not a score, not a black-box answer. Every recommendation includes confidence, evidence links, and the policy checks applied.
Writes actions and justifications back into your ATS, HCM, or CRM. Human-in-the-loop: Approve · Edit · Decline — every choice captured with correlation ID for audit and rollback.
Improves from real outcomes — approvals, overrides, and on-the-job performance results at 30/90/180 days. Each cycle teaches Intelletto what "good" looks like in your specific context.
THE LOOP · Listen → Read → Reason → Recommend → Act → Learn ↺
In Practice
Workflow Journeys
Intelletto.ai's three domains each map to a detailed, scene-by-scene workflow journey — showing exactly how the sidecar operates inside your existing tools, from first signal to closed-loop learning. These interactive journeys are available in full on the Intelletto.ai website.
From JD to shortlist — without the noise
Hiring doesn't need more tools. It needs a breakthrough. The Hiring Workflow Journey walks through seven scenes that transform messy inputs into confident, auditable shortlists — all inside your existing ATS.
Culture lives in what happens after the hire
The Culture Feedback Sidecar (CFS) turns 30/60/90/180-day check-ins into a light, repeatable operating rhythm — capturing signal without burden. It closes the loop between hiring intent, manager reality, and outcomes so every requisition gets smarter over time.
Interviews that scale with evidence, not meetings
AVAR (AI-Powered Virtual Automated Recruitment) standardizes content, pacing, and decision logic — supporting adaptive interview flows with the analytics and auditability enterprises need to scale fairly across roles, regions, and clients.
Infrastructure
Architecture, Security & Governance
Architecture is not a technical afterthought — it is the commercial argument. Trust is not a feature we add; it is the architecture. Every layer has a clear purpose: process signals, protect data, and provide reproducible evidence that every decision can be explained and defended.
Control Plane
- Policy-as-code directs model selection, cost caps, and latency targets
- Every decision carries a correlation ID for replay, rollback, and audit export
- Feature flags enable instant rollback at any deployment stage
- Shadow → Gated → Automate deployment model with one-click reversion
Data Plane
- Durable storage combining relational (Cloud SQL), vector (embeddings), and inverted (OpenSearch) indexes
- Event bus for low-latency sidecar activation and streaming feedback loops
- GCS for résumé artifacts with lifecycle management and retention controls
Resilience
- Multi-AZ deployments, health-based routing, circuit breakers
- P99 latency target < 800ms for scoring pipeline
- Error budget < 1% per connector; chaos tests validate recovery
Security & Privacy
- Least-privilege access by default; RBAC and ABAC enforced at every service boundary
- Encryption in transit (TLS 1.3) and at rest (AES-256)
- Regional data residency: customers choose where data lives to meet local law
- DSAR support: data subject requests traceable and fulfilled on demand
- Retention controls: customers set lifecycle rules; expired data deleted automatically
- Scoped API tokens with per-connector permission boundaries
Governance
- Immutable audit logs with reason codes, evidence links, and actor IDs
- Exportable compliance bundles for regulators, boards, and internal audit teams
- Clean-room IP policy: contributor attestations, license scans, SBOMs on every release
- Fairness monitors with demographic parity and equalized odds reporting
- SOC 2, GDPR, and PDPA readiness built into the product roadmap, not bolted on post-launch
Compliance by Design
Governance & Explainability
The EU AI Act Article 13 requires transparency for high-risk AI systems; Article 14 mandates human oversight. Annex III explicitly classifies AI used in employment and recruitment decisions as high-risk. Intelletto.ai is built to these standards from the ground up — not retro-fitted to them.
Explainability Architecture
- Reason codes: every score and recommendation contains structured explanation codes that reference specific evidence
- Confidence bands: probabilistic outputs include confidence intervals, not just point estimates
- Evidence links: each reason code links back to the specific data element that drove it
- Drill-down UI: recruiters and managers can trace any score to its source signals in one click
- Counterfactual queries: "what would change this decision?" — available in the audit interface
Human-in-the-Loop Design
- Approve · Edit · Decline workflow captured on every recommendation
- Override rationale stored with correlation ID and actor identity
- Override patterns feed back into bias monitoring and model recalibration
- No fully automated adverse decisions — human confirmation gate enforced by architecture
Audit & Compliance Artifacts
- Immutable decision logs with timestamp, actor, model version, prompt version, and output hash
- Exportable audit packs: one-click generation of evidence bundles for regulatory requests
- DSAR-ready: data subject access requests fulfilled with full decision history for any individual
- Fairness reports: demographic parity, equalized odds, and intersectional analysis exported on demand
Regulatory Alignment
- EU AI Act (Annex III): transparency, human oversight, accuracy, and robustness requirements addressed by design
- GDPR Article 22: no fully automated decisions with legal effect — human gate enforced at architecture level
- PDPA (PH, TH, SG, NZ): purpose limitation, consent management, and data subject rights baked into the platform
- SOC 2 Type II: security, availability, and confidentiality controls on the readiness roadmap
Competitive Intelligence
Competitive Benchmarking
Intelletto.ai competes in a landscape of point solutions and platform-embedded features. The key differentiator is architectural: we are the only vendor building an attach-layer, cross-platform, explainability-first control plane that works across ATS, HCM, and CRM with a unified governance model. Platform-embedded AI is locked to a single vendor's stack. Point solutions solve one problem without a shared governance layer. Intelletto solves both.
| Vendor | Approach | Platform Lock | Explainability | Governance Layer | Write-back | Time-to-Value | Rating |
|---|---|---|---|---|---|---|---|
| ★ Intelletto.ai | Embedded sidecar | Platform-agnostic | Reason codes + evidence | Unified control plane | Any ATS/HCM/CRM | Weeks | 5/5 |
| HireVue | Video assessment | Own portal | Score only | Limited | Selected ATS | Weeks | 3/5 |
| Eightfold.ai | Talent intelligence | Own platform | Skills-based | Some controls | Integrations | Months | 3/5 |
| Beamery | Talent CRM/OS | Own platform | Basic | Workflow-level | Integrations | Months | 2/5 |
| Workday AI | Platform-embedded | Workday-only | Suite explainers | Within Workday | Native | Months | 2/5 |
| SAP SuccessFactors AI | Platform-embedded | SAP-only | Suite explainers | Within SAP | Native | Months | 2/5 |
| Paradox (Olivia) | Automation / Chat | Partial | Basic | Workflow-level | Integrations | Weeks | 3/5 |
| Phenom People | Talent experience | Own platform | Basic | Limited | Integrations | Months | 2/5 |
Rating key: 5 = standout market leader in this dimension; 4 = strong; 3 = competitive; 2 = limited; 1 = nascent. Assessment is directional and based on publicly available product documentation and analyst coverage as of 2025.
Domain Detail
Parsing & Scoring — Technical Detail
Résumé parsing is Intelletto.ai's first production workload and the foundation of the talent intelligence stack. The pipeline is designed for lossless extraction — every field extracted carries a confidence score, every transformation is logged, and every output is reproducible from the raw input. Cost metering is production-validated.
Pipeline Stages
| Stage | Function | Technology | Output |
|---|---|---|---|
| 01 · Intake | Accept PDF, DOCX, HTML, image formats; route to appropriate extractor | GCS upload trigger, MIME detection | Canonical artifact ID, raw binary |
| 02 · Deduplication | Hash + semantic fingerprint to detect duplicate candidates across sources | SHA-256 + embedding cosine similarity | Dedup flag, canonical candidate ID |
| 03 · OCR / Extraction | Text and structured data extraction from scanned or complex PDFs | Google Document AI | Extracted text blocks with bounding boxes |
| 04 · LLM Extraction | Skills, roles, certifications, dates, locations extracted with structured output | Gemini 2.5 Flash (thinking mode), Pydantic schemas | Structured JSON candidate profile |
| 05 · Normalization | Job title standardization, skills taxonomy mapping, location geocoding | Custom taxonomy + geo resolver | Normalized profile with confidence scores |
| 06 · Scoring | Role Compatibility Score across 6 deterministic buckets + AI soft-signal overlay | Deterministic engine (Standard) / Gemini inference (Premium) | Scored profile with reason codes + evidence links |
| 07 · Write-back | Enriched profile and scores written back to ATS/HCM with correlation ID | Connector API + event bus | ATS record updated; audit log entry created |
Scoring Dimensions (6 Buckets)
| Bucket | Signal Type | Weight | Explainability |
|---|---|---|---|
| Skills Match | Hard skills, certifications, tools | High | Matched skills listed with source evidence |
| Experience Relevance | Role proximity, industry, seniority | High | Role delta and tenure patterns cited |
| Education Alignment | Degree level, field, institution tier | Medium | JD requirement vs. candidate comparison shown |
| Trajectory Signal | Career progression rate, promotion cadence | Medium | Progression curve annotated |
| Recency & Activity | Recent roles, skill freshness, tenure gaps | Low-Medium | Last active date and gap flags surfaced |
| Soft-Signal Inference | Communication style, framing, presentation | Low (Premium only) | AI inference confidence band shown; human-editable |
Unit Economics
Costing — Parsing & Scoring (Per Résumé)
Costs below are based on production metering data — not estimates. Pipeline runs against real résumé volumes using Gemini 2.5 Flash with thinking mode. Standard scoring is fully deterministic (zero LLM calls, ~38ms per score). COGS levers — policy-driven model routing, adaptive batching, cache windows, volume commitments — create a clear path to margin expansion as volume scales.
Parsing
| Component | Standard ($) | Premium ($) |
|---|---|---|
| Resume Deduplication | 0.0001 | 0.0001 |
| Resume Upload + OCR | 0.0004 | 0.0004 |
| Data Extraction (LLM) | 0.0081 | 0.0081 |
| Data Cleaning & Structuring | 0.0013 | 0.0013 |
| AI Data Wrangling | — | 0.0053 |
| Infrastructure (Cloud SQL + GCS) | 0.0007 | 0.0007 |
| Total Parsing | $0.0106 | $0.0159 |
Scoring
| Component | Standard ($) | Premium ($) |
|---|---|---|
| Deterministic Score (6 buckets) | 0.0004 | 0.0004 |
| AI Soft-Signal Inference | — | 0.0024 |
| Total Scoring | $0.0004 | $0.0028 |
| Bundle | Grand Total ($) |
|---|---|
| Standard Parse + Standard Score | $0.0110 |
| Premium Parse + Premium Score | $0.0187 |
Notes: Per-résumé COGS (USD) based on production metering data. Gemini 2.5 Flash with thinking. Standard scoring is fully deterministic (zero LLM calls, ~38ms). Excludes compute hosting baseline and network egress. Subject to model/provider choice and volume tier commitments. At 100K résumés/month, volume discounts and batch processing are estimated to reduce COGS by ~30–40%.
Commercial Strategy
Go-To-Market & Pricing
At $250K seed stage, focus is the strategy. One beachhead motion, one ICP, founder-led sales, and a pilot model designed so the first customer proves everything the next customer needs to see.
Ideal Customer Profile
200–3,000 employees. Running a mainstream ATS (Greenhouse, Workday, BambooHR, SAP) already. Actively using AI in recruiting but lacking explainability or audit infrastructure.
CHRO, CPO, VP Talent Acquisition, or CTO. Motivated by one of three triggers: EU AI Act compliance deadline, a board question about AI fairness, or a failed internal AI audit.
Philippines BPO sector (beachhead), Australia & New Zealand (founder network), and EU-headquartered multinationals with APAC hiring operations (regulatory urgency).
EU AI Act Aug 2026 deadline approaching · Board / regulator AI audit · Failed explainability review · Hiring volume spike · New CHRO/CPO re-evaluating tech stack.
The KPI-Gated Pilot Motion
Every engagement begins as a paid pilot. Success criteria are locked before day one. This protects conversion rates, demonstrates genuine product confidence, and generates the reference data every subsequent sale needs.
2–4 week scoped engagement. Connector setup, data mapping, KPI dossier baseline. Fixed fee: $5–10K.
6–8 weeks. Intelletto runs beside existing workflow in shadow mode. KPI gates measured against baseline. 40% prepay.
KPI gates passed → convert to annual contract. Gates missed → remediation SLA triggered before any cancellation option.
Production cutover. Compliance Pack attached. Outcome loops activated. Domain expansion scoped for Year 2.
Sales Channels — Seed Stage Priority
Scott's network across APAC CHRO/CPO/CTO buyers. C-suite AI dinner circuit (The Ortus Club, BGC). Zero CAC on warm introductions.
1.5M+ workers, high-volume hiring, BIR/DOLE compliance pressure, and existing relationships from Emapta days. Natural beachhead.
Auckland base, strong founder network, and mature AI governance awareness. EU Act-adjacent regulatory environment via NZ Privacy Act reforms.
HR transformation practices at Big 4 and regional SI firms. Marketplace listings (Greenhouse, Workday). Activated post follow-on raise.
Packaging & Pricing (Directional)
| Module | Base / mo | Usage | Governance Add-on |
|---|---|---|---|
| Parsing | $1,500–3,000 | +$8–15 per 1K résumés | Audit bundle, DSAR exports |
| Scoring | $1,000–2,500 | +$5 per shortlist batch | Reason-code exports, fairness pack |
| AVAR | $2,000–4,000 | +$12 per video hour | Rubric library, reviewer analytics |
| Perfect Match | $1,500–3,000 | +$3 per outcome cycle | Fairness reports, counterfactual exports |
| Compliance Pack | $800–1,500 | — | EU AI Act artifacts, DPIA templates, DSAR automation |
Pricing is directional and shared under NDA. Full stack (Parsing + Scoring + Compliance Pack) targets $5–8K/month per tenant ($60–96K ACV), scaling to $100–150K+ with AVAR and Perfect Match added.
Expansion Flywheel
Each domain add-on is adopted on the governance layer already approved. ACV grows; CAC stays near zero. This is how NRR exceeds 115% without price increases.
Implementation
Delivery & Implementation Model
Time-to-First-Value (TTFV) ≤ 14 days. The implementation model is designed to be low-risk, low-friction, and fully reversible at every stage.
| Phase | Timeline | Activities | Exit Criteria |
|---|---|---|---|
| 01 · Connect | Week 0–2 | Connector setup, API credential scoping, data mapping, shadow mode activation | TTFV ≤ 14 days; first parsed résumés in system |
| 02 · Baseline | Week 3–6 | KPI dossier baseline, recruiter onboarding, governance receipts, first shortlists reviewed | KPI baseline locked; ≥ 10 recruiter sessions logged; override rate captured |
| 03 · Validate | Week 6–10 | Pilot acceptance review against KPI gates, compliance pack attached, production cutover decision | KPI gates passed; Compliance Pack attached; contract conversion or remediation SLA triggered |
| 04 · Expand | Month 3+ | 30-day outcome loop activated, additional domain modules added, executive review cadence established | First outcome data cycle complete; expansion modules scoped; NRR growth path confirmed |
Rollout Model: Shadow → Gated → Automate
- Shadow mode: Intelletto runs in parallel, making no changes — risk-free visibility into recommendations before any commitment
- Gated mode: recommendations surface to recruiters for Approve · Edit · Decline; human stays in loop; overrides captured
- Automate: low-risk, high-confidence actions automated with circuit-breaker rollback always available
Key Commitments
- TTFV ≤ 14 days from signed SOW
- P99 latency < 800ms for scoring pipeline in production
- Error budget < 1% per connector
- One-click rollback at any deployment stage
- Dedicated CSM for first 90 days post-launch
- Monthly board-ready KPI report generated automatically
Projections
Financial Plan & Unit Economics
The $250K seed round is a prove-the-model raise, not a scale raise. The objective is clear: land 2–3 paying pilots, validate time-to-first-value, and generate enough ARR signal to support a meaningful follow-on raise at stronger leverage. Financial targets are calibrated to this scope — conservative on customer count, honest on runway, and focused on the unit economics that matter most at this stage: ACV, CAC payback, and gross margin per résumé.
Multi-Year Revenue Targets
| Metric | Year 1 Exit End of $250K runway | Year 2 Exit Post follow-on raise | Year 3 Breakeven |
|---|---|---|---|
| ARR | $200K–$400K | ~$1.0–$1.5M | ~$2.5–$3.0M |
| Paying Customers | 2–3 pilot conversions | 6–10 enterprise | 15–20 enterprise |
| Avg ACV | $60–100K | $100–150K | $150–180K |
| Gross Margin | ~40–45% | ~52–56% | ~62% |
| CAC Payback | ≤ 12 months | ≤ 10 months | ≤ 9 months |
| NRR | Baseline (first renewals) | Target ≥ 115% | Target ≥ 125% |
| GRR | Target ≥ 85% | Target ≥ 90% | Target ≥ 92% |
Y1 ARR of $200–400K = 2–3 contracts at $60–100K ACV. Y2 onwards assumes a follow-on raise of $500K–$1M triggered by proof-of-model at end of Y1 runway.
COGS & Margin Levers — Early Stage
| Lever | When it applies | Mechanism | GM Impact |
|---|---|---|---|
| Deterministic Standard Tier | Day 1 | Zero LLM calls for Standard scoring — fully rule-based at ~38ms/score | +4–6 pts vs. Premium |
| Policy-driven model routing | First pilots | Route to lighter model when confidence ≥ threshold; reserve Flash thinking for edge cases | +3–5 pts |
| Cache windows | First pilots | Cache identical or near-identical résumé extractions within a campaign window | +1–2 pts |
| Adaptive batching | 3–5 active tenants | Batch non-urgent résumés during off-peak GCP windows | +2–3 pts |
| Volume commitments | Post follow-on raise | Negotiate token/GPU reservations once monthly volume justifies it | +3–4 pts |
Unit Economics
First 2 connectors built, shadow-mode pilots activated with 2–3 target accounts. Infrastructure live on GCP.
2 pilots converted to paying contracts ($60–100K ACV each). TTFV ≤ 14 days demonstrated. First 30-day outcome loop running.
$200–400K ARR in sight. NRR baseline established. Second connector set (Workday / BambooHR) scoped. Raise at materially stronger leverage — paying customers, live outcomes data, proven TTFV.
6–10 enterprise accounts, multi-domain expansion driving NRR ≥ 115%, and GM improvement trajectory to breakeven visible.
The Ask
Seed Round — $250K Ask & Use of Funds
Instrument
- Structure: SAFE (Post-Money, valuation cap TBD) or priced round by lead investor preference
- Close target: This quarter; initial commitments allocated to first connector build
- Runway: 6–10 months with lean spend and 40% prepay target on pilot contracts
- Board: Monthly board pack auto-generated; quarterly investor update with KPI dashboard
Milestone Gates
- ≥ 2 production connectors GA (Greenhouse + one ATS/HCM)
- ≥ 2 KPI-gated pilot conversions to paying contract
- TTFV ≤ 14 days validated in production
- DSAR path validated; foundational compliance posture established
- ARR trajectory confirms follow-on raise viability
| Category | % | Amount | What It Funds | Milestone Gates |
|---|---|---|---|---|
| Product & Engineering | 33% | $82,500 | Core platform build, first 2 ATS connectors, résumé parsing pipeline, model routing | ≥ 2 connectors GA; TTFV ≤ 14 days; P99 < 800ms |
| Cloud & Inference | 18% | $45,000 | GCP compute, Cloud SQL, GCS storage, n8n orchestration, model usage (Gemini 2.5 Flash) | No unplanned token spikes; cost SLOs met |
| GTM & Pilots | 14% | $35,000 | Pilot SOWs, founder-led outreach, partner introductions, basic ABM | ≥ 2 paid pilots signed; ≥ 1 converting to contract |
| Security & Compliance | 10% | $25,000 | DSAR path, DPIA templates, basic data residency controls, compliance documentation | DSAR path validated; data handling documented |
| Talent (gated) | 10% | $25,000 | Fractional/contract engineering support, gated behind first pilot conversion | ≥ 1 pilot conversion before any contract engagement |
| Customer Success | 6% | $15,000 | Onboarding playbooks, pilot tracking, NPS capture, founder-led success for first accounts | TTFV ≤ 30 days; NPS ≥ +60 |
| Ops, Legal & Finance | 5% | $12,500 | MSA/DPA templates, basic insurance, board reporting setup, fractional legal counsel | Monthly board pack live; contracts in place |
| Reserve | 4% | $10,000 | Contingency for unexpected compliance requirements or pilot extension costs | CEO discretion; board notification required |
| Total | 100% | $250,000 | ||
Risk Management
Risk & Mitigation
All enterprise software investments carry execution risk. We have identified the primary risk categories and maintain active mitigation strategies for each. Seed capital is deployed only on hitting milestone gates, reducing downside exposure.
| Risk | Category | Likelihood | Impact | Mitigation |
|---|---|---|---|---|
| Platform API access changes (Workday, SAP) | Technical | Medium | High | Connector abstraction layer; contract-tested adapters; multi-platform diversification from day one |
| Enterprise sales cycle longer than modeled | Commercial | Medium | Medium | KPI-gated pilots with 40% prepay accelerate cash; SI partner co-sell compresses cycle |
| Model cost increases (LLM pricing) | Technical / COGS | Low | Medium | Policy-driven model routing; multi-provider fallback; deterministic Standard tier as cost floor |
| Regulatory change (EU AI Act interpretation) | Compliance | Medium | Medium | Explainability-first architecture is already aligned with highest-bar interpretation; legal retainer for monitoring |
| Enterprise IT security review delays | Commercial | Medium | Medium | SOC 2 readiness pack, DPIA templates, and security questionnaire library pre-built; dedicated security review track |
| Incumbent platform embeds competing feature | Competitive | Low | Medium | Platform-embedded AI is locked to one vendor; Intelletto's cross-platform, audit-grade approach is structurally differentiated |
| Key person dependency (founder technical) | Team | Low | High | Architecture documentation; advisor network; talent hiring gated to conversions de-risks dependency as team grows |
The Team
Team
Intelletto.ai is built by operators who have run the systems we are now making smarter. The founding team combines 40+ years of global financial and enterprise leadership, CTO-level AI platform experience, outsourced delivery expertise, and deep APAC C-suite commercial networks — the exact profile required to sell, build, and govern an explainable-AI sidecar at enterprise scale.
With 40 years of leadership in emerging-market financial services across the Middle East, Asia, Russia, Europe, and Africa, Gerrit has held pivotal roles at marquee global institutions including Troika Dialog, J.P. Morgan, Lehman Brothers, and Kleinwort Benson. His operational depth spans market strategy, trading infrastructure, and regulatory navigation across economic cycles. An International Finance master's and finance bachelor's provide both strategic insight and quantitative grounding.
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 — making him ideally suited to guide a mission that places explainability, governance, and trust at the centre of AI-powered workflows.
Scott 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 — including 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.
Scott combines strategic vision with execution: redesigning legacy systems into federated, AI-native architectures that reduced onboarding time by 40% and recruiter effort by 25%. He is personally hands-on in the Intelletto.ai architecture — FastAPI backend, PostgreSQL schema, GCP infrastructure, n8n orchestration, and the résumé parsing engine — while maintaining a high-proximity network across CHRO, CPO, and CTO buyers in ANZ and APAC.
Auckland, NZ · Manila, PH
Julio is a founder of technology-enabled professional services and offshoring businesses, with end-to-end delivery experience 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 and managed services around the sidecar, and aligning delivery playbooks to enterprise expectations on cost, quality, and speed.
Richard 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 and partner revenue. Richard also co-founded an executive-search firm serving multinationals across Southeast Asia.
For Intelletto.ai, Richard owns top-of-funnel creation: warm introductions to CHRO, CPO, and VP Talent buyers across the BPO sector, managed-services firms, and enterprise accounts in the Philippines and broader APAC — the precise channels where the $250K seed round's beachhead motion runs.