Multikor.ai
Knows When to Act. Knows When to Ask.
Production-grade agentic AI orchestration for SMB back-office automation — AP, customer support, procurement — powered by a three-layer architecture: Autonomous Data Fabric, Delta Intelligence Engine, and Self-Healing CI/CD. Deploys in 20% of the time at 10% of the cost, with 95% auto-remediation. Zero AI engineers required. Knows when to act. Knows when to ask.
Confidential Investor Presentation | February 2026
What is Multikor?
Agentic automation that knows when to act—and when to ask
62% of organizations are stuck in AI pilots. Only 23% are scaling agentic AI.
Our production-grade orchestration platform delivers autonomous back-office workflows through a three-layer architecture: the Autonomous Data Fabric ingests and normalizes enterprise data, the Delta Intelligence Engine detects changes and routes decisions with confidence scoring, and the Self-Healing CI/CD pipeline deploys and maintains workflows with 95% auto-remediation.
Targeting a $500B+ back-office automation opportunity.
Why We Win: Three-Layer Architecture + Compounding Advantage
Autonomous Data Fabric: Schema inference, multi-source normalization • Delta Intelligence Engine: Change detection, confidence-based routing • Self-Healing CI/CD: 95% auto-remediation, auto-rollback, circuit breakers • 4-Tier SLM Hierarchy: 70% at ~$0 cost • Compounding Advantage: Every deployment makes the next one faster
The Opportunity: The Operationalization Gap
McKinsey 2025: 62% stuck in pilots, only 23% scaling agentic AI. 33M US companies lack AI implementation capability (5.4% adoption).
The Solution: Three-Layer Agentic Architecture
Production-grade orchestration with confidence-based human escalation
4-Tier Model Hierarchy
- Tier 1: Self-hosted SLMs (<1B params) → ~$0 cost
- Tier 2: Haiku → Fast, cheap for moderate tasks
- Tier 3: Sonnet → Complex reasoning
- Tier 4: Opus → Reserved for highest complexity
Three-Layer Architecture
- Autonomous Data Fabric: Schema inference, multi-source normalization, real-time ingestion
- Delta Intelligence Engine: Change detection, confidence scoring, decision routing
- Self-Healing CI/CD: 95% auto-remediation, circuit breakers, auto-rollback
- Confidence-Based Escalation: Knows when to act autonomously, knows when to ask humans
- Enterprise Security: Multi-tenancy, per-tenant encryption, Compliance-as-Code, PII redaction pre-inference
- Compliance: SOC2/HIPAA architecture (Q2-Q3 2026 certifications)
Product Strategy
Multikor MVP deployed and production-ready: Learn from real deployments, scale what works
Launch Phase (Q1-Q2 2026)
- Multikor MVP complete, platform operational — AP + CS
- Apexon: Strategic Partner — first customer pilot in negotiation
- JTBD analysis: prioritize highest-pain services
- Autonomous automation subscription model
Scale Phase (Q3-Q4 2026)
- Scale proven services to similar companies
- First domain SLMs trained on pilot workflow data
- Expand disciplines: Procurement, Kaizen, HR
- BPO channel concurrent via Apexon
2027+: SLM Fleet + Expansion
Domain-specific SLMs handling 70% of requests at ~$0 cost • Grow from SMB into mid-market and enterprise • Expand back-office disciplines based on demand
Market Opportunity
SMB-first beachhead — 33M US companies lack AI capability. Sub-$5K CAC, 2-4 hour onboarding. BPO concurrent.
SMB-First + Growth Ladder
- $500B+ TAM across back-office automation
- 33M US companies lack AI implementation capability (5.4% adoption)
- SMBs don't have AI budgets or AI engineers — need turnkey automation
- SMB beachhead: $50M-$250M revenue companies
- Mid-market growth: $250M-$1B; Enterprise: $1B+
- Wave 1: E-commerce/DTC, SaaS, Professional Services
BPO Channel (Concurrent)
- $245B TAM — BPO market for outsourced ops
- Negotiating first customer pilot with Apexon
- 10-100X customer multiplier effect
- Platform partnerships from day one
- Not deferred — concurrent execution
Competitive Moat: Compounding Advantage
Three-layer architecture + domain SLMs + compounding data advantage
vs. Anthropic / Claude for Enterprise
Claude 3.5/4.5 + enterprise deployment (Bedrock, Vertex), long-context, safety, multi-agent tools. Multikor advantage: They provide models requiring ML teams. We deliver turnkey production-grade orchestration for SMBs — zero AI engineers required.
vs. Google Vertex AI (Gemini)
Managed AI platform, Google Cloud integration, MLOps/pipeline tooling. Multikor advantage: Infrastructure for engineers. Multikor is a finished product for SMBs. Sub-$5K CAC vs enterprise sales cycles.
vs. StackAI
Enterprise AI agent platform, orchestration, agent SDLC, governance. Multikor advantage: Enterprise-focused, expensive. We're SMB-first: 2-4hr onboarding, sub-$5K CAC, domain SLMs vs generic orchestration.
vs. Vertical AI (e.g., Syllable)
Contact-center/telephony agents, SIP connectivity, outcome-based pricing. Multikor advantage: Single vertical. We're horizontal across all back-office with domain SLMs per discipline + cross-industry intelligence.
Business Model
Autonomous Automation: Customers buy operational outcomes, not software licenses
Revenue Streams
- Autonomous automation subscription per discipline
- Land with one discipline, expand to more
- Sub-$5K CAC for SMB direct (2-4 hour onboarding)
- BPO channel revenue (Apexon, others)
Cost Advantage: SLMs
- SLMs handle 70% of requests at ~$0 cost
- LLMs only for complex tasks (30% of requests)
- Neptune RAG eliminates $2-5K/mo vector DB costs
- Hybrid DAG orchestration: 40% cost reduction
Financial Projections
Path to $12M-$15M ARR by Q3-Q4 2027
Series A Readiness: Q3-Q4 2027
$12M-$15M ARR with strong customer retention • $120M-$180M post-money valuation • $30M pre-money seed → 3-4.5X return in 18 months
World-Class Leadership Team
Proven track record in enterprise AI and data intelligence
Suresh Nelakantam
CEO & Co-Founder
20+ years enterprise data • SVP Engineering at RxSense (6B+ transactions) • AI-first transformation leader • Patent-pending architecture inventor
Leigh Turner
CTO & Co-Founder
25+ years distributed systems • Multiple US & international patents • Hyperscale cloud-native architecture • LLM & RAG systems expert
Anthony Antonuccio
SVP, Business Operations
35+ years strategic leadership • 2 successful exits (Valent→Lycos, Vivo→RealNetworks) • Former Amazon AWS & Novell • 130+ countries experience
J. Scott Benson
Board Chair
$500K investor • Founder of software.com • Business development expertise • Strategic advisory and governance leadership
+ Senior AI Architect & Strategic Partnerships
13+ years enterprise AI/ML (SLM hierarchy, Neptune RAG, hybrid DAG) • NVIDIA Inception: GPU credits • AWS Activate: $150K credits • Apexon: Strategic Partner — pilot in negotiation (5,500 engineers, Goldman Sachs backed)
Investment Opportunity
$4.5M seed round at $30M pre-money valuation
Use of Funds
- SLM R&D & platform development (~25%)
- GTM execution & sales team
- Customer success & support
- Cloud infrastructure & AI/ML
- Strategic partnerships & BPO channel
Milestones
- Q1 2026: Multikor MVP deployed and operational; Apexon pilot in negotiation
- Q2 2026: Close $4.5M seed, scale proven services
- Q3-Q4 2026: First domain SLMs, 15-20 customers
- Q1-Q2 2027: 25-35 customers, expand disciplines
- Q3-Q4 2027: $12M-$15M ARR, Series A ready
Exit Scenarios
Multiple paths to exceptional returns
Strategic Acquisition (5-7 years)
Potential Acquirers: ServiceNow, SAP, Oracle, Microsoft, IBM, Salesforce
Valuation: $500M-$2B based on ARR multiples (8-12X)
Seed Return: 14-58X
Growth Equity / Series B-D (4-6 years)
Path: Continue scaling to $50M+ ARR
Valuation: $400M-$1.2B at Series C/D
Seed Return: 11-35X with partial liquidity
IPO (7-10 years)
Target: $100M+ ARR, established market leader
Valuation: $1.5B-$5B+ at IPO
Seed Return: 43-145X+
PE Buyout / Secondary (6-8 years)
Profile: Profitable, predictable SaaS business
Valuation: $800M-$2.5B based on EBITDA
Seed Return: 23-72X
Conservative: 11-23X | Base Case: 43-103X
Direct sales only = 11-23X (conservative) • Direct + BPO channel = 43-103X (base case) • Multiple exit paths de-risk investment
What Kills This?
Risk acknowledgment with clear mitigants
Risk: Anthropic, Google Vertex AI, StackAI ship enterprise agents
Mitigant: Domain-specific SLMs trained on real workflow data can't be replicated generically. Compounding advantage: schema inference, guardrail calibration, and auto-remediation patterns deepen with every deployment.
Risk: SLM training takes longer than projected
Mitigant: 4-tier hierarchy: LLMs handle all tasks initially. SLMs progressively take over. No cliff dependency.
Risk: Initial customer pilots don't deliver expected results
Mitigant: JTBD methodology identifies real pain. Self-Healing CI/CD adapts in real-time with 95% auto-remediation rate. Confidence-based escalation catches edge cases early.
Risk: Neptune Analytics at $184/day (80.9% of infra)
Mitigant: Breakeven at 5 clients. NVIDIA credits offset training. SLM inference cheaper than LLM once deployed.
Risk: Don't hit milestones for Series A
Mitigant: 24+ month runway. Cash flow positive at 5 clients. Multiple exit paths: M&A from cloud platforms, BPO providers.
Risk: Reliance on third-party LLMs before SLMs are ready
Mitigant: Cloud-agnostic architecture (AWS/Azure/GCP). LLM costs declining 10X/year. SLMs progressively reduce dependency.
Why Now?
18-24 month window before big tech commoditizes domain-specific AI
SLM Window: Build Domain Moat Before Big Tech Catches Up
Big tech ships generic agents daily (Anthropic Claude for Enterprise, Google Vertex AI). But domain-specific SLMs trained on real enterprise workflow data are defensible. 18-24 months to establish the moat.
McKinsey Validates: 62% Stuck in Pilots
Only 23% scaling agentic AI • Companies need purpose-built automation, not generic tools • Methodology governance is the missing piece that breaks the pilot trap
SLM Economics: Inference Costs Approaching Zero
Self-hosted SLMs (<1B params) run on Lambda/SageMaker at ~$0 per request • 70% of tasks handled locally • LLM costs declining 10X/year for remaining 30%
Production-Ready: Real Enterprise Validation
Multikor MVP deployed and operational • Negotiating first customer pilot with Apexon • Real workflow data to train first SLMs • JTBD analysis to prioritize highest-value services
Three-Layer Architecture. Compounding Advantage. Defensible by Design.
Knows When to Act. Knows When to Ask.
Ready to Invest?
$4.5M seed round • $30M pre-money • 13% equity
Full investor materials available at:
investors.multikor.ai
Knows When to Act. Knows When to Ask.
This presentation contains confidential and proprietary information intended solely for potential investors.
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