Intelletto.ai — Investor White Paper

Financials At‑A‑Glance

Executive Summary

Enterprises rely on ATS, HCM, and CRM to make hiring and customer decisions. These systems are slow, opaque, and don’t learn from outcomes. Managers can’t trust the “why,” regulators can’t audit it, and boards can’t defend it.

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, and evidence links.

The result is simple: time saved, trust earned, and learning that compounds on 30/90/180-day outcomes. We rebuilt familiar patterns—resume parsing, explainable scoring, and integrations—using a clean-room approach for defensibility.

What we will deliver

• Weeks-to-value: measurable ROI in weeks, not years.
• Explainability by default: every recommendation includes the “why.”
• Governance built-in: least-privilege, RBAC/ABAC, audit receipts.

Targets we will hold ourselves to

• ARR: $2.0–$2.2M at exit Year 1; ~$5.0M at exit Year 2.
• Margins: gross margin improving from ~45% (Y1) to ~62% at breakeven.
• Retention: NRR ≥ 120%; GRR ≥ 90%.
• CAC payback: ≤ 12 months.

The ask

Raising $1.5M seed to fund connectors, KPI-gated pilots, and compliance packs, extending runway 18–24 months. The sidecar is not another tool—it is an attach layer that makes existing systems auditable, defensible, and faster to value.

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.

Technology Stack & R&D Roadmap

Our technology stack is built to be durable, explainable, and cost-efficient. We use proven components, keep the architecture simple, and automate wherever possible. The principle is straightforward: fewer moving parts, lower risk, faster learning.

Stack

• Core Platform: Event-driven services running on Kubernetes, infrastructure as code with Terraform.
• Data Layer: S3 for storage, RDS for relational workloads, and OpenSearch for indexing and retrieval.
• Integration: Secure API gateway with contract-tested connectors for ATS, HCM, and CRM systems.
• MLOps: Versioned models, monitoring dashboards, retraining hooks, and guardrails for fairness.
• Inference: Policy-routed model calls with RAG for context and embeddings for search and deduplication.
• Observability: Golden signals, drift monitors, and fairness dashboards visible to customers and auditors.

R&D Roadmap

• Near Term: Strengthen connectors, accelerate Time-to-First-Value, and release fairness dashboards.
• Next 12 Months: Add confidential compute, expand classification capabilities, and automate compliance reports.
• Long Term: Counterfactual analysis and continuous learning systems that adapt in near real time.

We invest in R&D with a clear rule: each step must make the system more explainable, more governed, or more efficient. That discipline keeps us focused on customer outcomes, not feature bloat.

Architecture, Security & Governance

Our architecture is designed to be simple, resilient, and auditable. We do not add complexity for its own sake. Every layer has a clear purpose: process signals, protect data, and provide evidence that decisions are explainable.

Control Plane

• Policy-as-code directs model selection, cost, and latency targets.
• Every decision carries lineage for replay, rollback, and audit export.

Data Plane

• Durable storage combining relational, vector, and inverted indexes.
• Event bus for low-latency inference and streaming feedback loops.

Security & Privacy

• Least-privilege access by default; RBAC and ABAC enforced at every service.
• Encryption in transit and at rest, with regional residency options.
• Support for DSAR, deletion workflows, and retention controls.

Governance

• Immutable audit logs with reason codes and evidence links.
• Exportable compliance bundles for regulators and boards.
• Clean-room IP policy: contributor attestations, license scans, SBOMs.

Resilience

• Multi-AZ deployments, health-based routing, and circuit breakers.
• Regular chaos tests to validate failure recovery.

The outcome is straightforward: systems that customers can trust. Managers see why a recommendation was made. Regulators see defensible evidence. Boards see decisions they can stand behind. Trust is not an add-on; it is the architecture.

Security & Compliance

Security and compliance are not features we add later. They are part of the foundation. Customers, regulators, and boards expect systems they can trust. We design Intelletto.ai so that privacy, auditability, and governance are defaults.

Security Principles

• Least privilege by default: every service enforces RBAC and ABAC rules.
• Encryption end-to-end: data is encrypted in transit and at rest.
• Regional control: customers choose where their data resides to meet local laws.

Compliance by Design

• DSAR support: data subject requests are traceable and fulfilled on demand.
• Retention controls: customers set lifecycle rules; expired data is deleted automatically.
• Exportable evidence: audit packs are generated with logs, reason codes, and outcomes.

Governance & Assurance

• Immutable audit logs capture every decision, who made it, and why.
• Clean-room IP hygiene with contributor attestations, license scans, and SBOMs.
• Independent readiness for SOC 2, GDPR, and PDPA built into the roadmap.

The standard we hold ourselves to is clear: customers must be able to explain and defend every decision. Security and compliance are not cost centers. They are the reason customers adopt and stay.

Governance & Explainability

Customers and regulators expect more than outcomes. They expect evidence. Governance and explainability are not features in Intelletto.ai — they are the way the system works.

Explainability

• Every recommendation carries a reason code, confidence score, and evidence link.
• Managers see why a candidate or customer was ranked, not just the score.
• Boards and regulators get audit packs that can be read and defended without technical translation.

Governance

• Policy-as-code: rules for fairness, compliance, and data use are explicit and versioned.
• Immutable logs: every decision is recorded, time-stamped, and tied to a correlation ID.
• Regional control: data residency and retention policies are enforced at the platform level.

Outcome-Linked Learning

• Signals at 30/90/180 days are written back into the system.
• Bias is monitored, drift is flagged, and recalibration is automatic.
• Progress is measured by outcomes, not just model accuracy.

The principle is clear: if a decision cannot be explained, it should not be used. Intelletto.ai makes explainability and governance defaults. That is what builds durable trust.

Competitive Benchmarking

Directional ratings for quick scan. Claims to be finalized during diligence.

Costing — Parsing & Scoring (per résumé)

Parsing — Standard Tier

Parsing — Premium Tier

Scoring — Standard Tier

Scoring — Premium Tier

Notes: Estimated per‑résumé costing (USD), directional for planning; excludes storage/egress; subject to model/provider choice and volume tiers.

Go‑To‑Market & Pricing

Motion

• Paid pilot (6–10 weeks) with KPI gates; convert on pass; remediate on fail.
• Marketplace listings + SI partners; founder‑led enterprise motion.

Packaging & Pricing (directional)

Delivery & Implementation

1. Week 0–2: Connector setup; mapping; TTFV ≤ 14 days.
2. Week 3–6: KPI dossier baseline; recruiter training; governance receipts.
3. Week 6–10: Acceptance review; production cutover; attach Compliance Pack.

Financial Plan & Unit Economics

Multi‑Year Targets

• ARR ramp: Y1 exit ≈ $2.0–$2.2M; Y2 exit ≈ $5.0M; Y3 breakeven (GM ~62%).
• Efficiency: CAC payback ≤ 12 months; NRR ≥ 120%; GRR ≥ 90%.

COGS Levers

• Policy‑driven model routing; adaptive batching; cache windows; GPU reservations.
• Connector contract tests; rollback via feature flags; cost SLOs per pipeline.

The Ask & Use of Funds — Seed Round ($1.5M)

• Instrument: SAFE (valuation cap TBD) or priced round by lead preference.
• Use: Core platform & connectors; compliance readiness; marketplace launches; KPI‑gated pilots.
• Runway: 18–24 months with prudent spend and 40% prepay target on pilots.
• Close target: This quarter; initial commitments allocated to connector acceleration.

Hiring Plan (Milestone‑Gated)

KPIs & Milestones

• Speed: Time‑to‑Shortlist ↓ ≥ 50%; TTFV ≤ 14 days.
• Efficiency: Recruiter hours ↓ ≥ 40%; first‑round pass‑up ↑ ≥ 15%.
• Governance: DSAR path validated; fairness dashboards adopted.
• Commercial: ARR exit Y1 ≈ $2.0–$2.2M; exit Y2 ≈ $5.0M.

Risks & Mitigations

• Connector variability: Contract tests, feature flags, playbacks.
• Inference cost volatility: Policy routing, caching, reservations.
• Adoption friction: In‑tool UI, KPI dossier, CS‑led onboarding.
• Compliance & IP hygiene: Clean‑room policy, contributor attestations, license scans, audits.

Founders & Team

Resources