Product Strategy: Pilot-Driven Roadmap
Prove value with design partners, then scale what works
Design Partner Pilots
Scale Proven Services
SLM Fleet & Market Expansion
Platform Architecture: Three-Layer Agentic Architecture
Production-grade 4-tier model hierarchy with enterprise methodology governance and 95% self-healing pipelines
4-Tier Model Hierarchy
SLMs handle 70% of requests at ~$0 cost
- Tier 1: Self-hosted SLMs (<1B params, DistilBERT, TinyBERT) — ~$0 inference via Lambda/SageMaker/ECS
- Tier 2: Haiku — Fast, cheap for moderate tasks
- Tier 3: Sonnet — Complex reasoning
- Tier 4: Opus — Reserved for highest complexity only
Result: 60-80% LLM cost reduction. Domain-specific intelligence that big tech can't replicate
Neptune RAG + Knowledge Graph
Proprietary retrieval with 1024d Titan embeddings
- Neptune Analytics with HNSW indexing for vector similarity
- 1024-dimensional Titan v2 embeddings
- Bedrock Knowledge Base as secondary retrieval
- 13-step pipeline with ≥70% similarity threshold
- Gelfand 5-Signal Confidence Scoring
Result: Eliminates $2-5K/month vector DB costs. Knowledge graph learns from each deployment
Hybrid DAG Orchestration + Self-Healing
Airflow (batch) + Step Functions (real-time)
- Apache Airflow (MWAA) for batch ETL workflows
- AWS Step Functions for real-time agentic workflows
- 40% cost reduction vs all-Airflow approach
- 24 feedback loops across 7 categories
- Circuit breakers, auto-rollback, Prompt Lifecycle Management (PLM)
Result: 95% self-healing pipelines with canary deployment and golden dataset validation. Eliminates 80-90% of manual data engineering.
Three-Layer Architecture: From Raw Data to Self-Healing Production
Layer 1: Autonomous Data Fabric
Schema inference, automated normalization, and connector management. Ingests any data source with zero manual configuration — the foundation for production-grade pipelines.
Layer 2: Delta Intelligence Engine
Change detection, confidence scoring, and decision routing. Knows when to act autonomously and when to escalate to humans based on confidence thresholds.
Layer 3: Self-Healing Agentic CI/CD
Canary deployment, auto-rollback, circuit breakers, and golden dataset validation. 95% auto-remediation rate — pipelines that fix themselves in production.
Two Agent Classes: Operational + Strategic Intelligence
Purpose-built agents that do work and surface insights, with methodology-driven human escalation
Built on High Performer Principles: Workflow Redesign + Human-in-the-Loop
McKinsey State of AI 2025: High performers are 2.8X more likely to redesign workflows (55% vs 20%), and human-in-the-loop validation is the #1 differentiating practice for scaling AI successfully.
Workflow Redesign at Core
Multikor doesn't just automate existing processes—we redesign workflows for maximum transformation. Domain-specific SLMs enable per-discipline optimization across industries.
Human-in-the-Loop Validation
Confidence-based scoring determines when to act autonomously and when to escalate. The system routes edge cases to humans based on confidence thresholds, not arbitrary rules.
Breaking the Pilot Trap
62% of organizations are stuck in AI pilots. Domain-specific SLMs + methodology guardrails break the pilot trap by delivering measurable results from day one with enterprise-grade governance.
Cloud-Agnostic Conversational AI Interface
Natural Language Data Exploration
Eliminates SQL query writing, democratizes data access for non-technical users across AWS, Azure, or GCP
Automated Troubleshooting
Replaces manual log analysis, faster incident resolution with root cause identification
Documentation Generation
Auto-generates technical docs, keeps documentation in sync with pipeline changes
Platform Flexibility: Deploy on AWS, Azure, or GCP with conversational interfaces adapted to each cloud provider's AI services. RAG indexing for knowledge retrieval, Stage 3 artifact API exposure, custom plugins for platform-specific workflows
Competitive Moat: Anti-Commoditization by Design
Three-Layer Architecture + Compounding Advantage + 95% Auto-Remediation
vs. Anthropic / Claude for Enterprise
Claude 3.5/4.5 + enterprise deployment (Bedrock, Vertex), long-context, safety, multi-agent tools
Their Approach: Foundational models requiring ML teams to build, fine-tune, and maintain enterprise AI solutions
Our Advantage: They provide models requiring ML teams. We deliver turnkey production-grade orchestration for SMBs — zero AI engineers required. Production-grade from day one.
vs. Google Vertex AI (Gemini)
Managed AI platform, Google Cloud integration, MLOps/pipeline tooling
Their Approach: Infrastructure for engineers — requires data science teams and significant integration effort
Our Advantage: Infrastructure for engineers. Multikor is a finished product for SMBs. Sub-$5K CAC vs enterprise sales cycles. 2-4 hour onboarding, not months.
vs. StackAI
Enterprise AI agent platform, orchestration, agent SDLC, governance
Their Approach: Enterprise-focused agent orchestration with complex governance and high price points
Our Advantage: Enterprise-focused, expensive. We're SMB-first: 2-4 hour onboarding, sub-$5K CAC, domain SLMs vs generic orchestration. Eliminates 80-90% of manual data engineering.
vs. Vertical Platforms (e.g., Syllable)
Contact-center/telephony agents, SIP connectivity, outcome-based pricing
Their Limitation: Single vertical focus — limited to one use case like contact center or telephony
Our Advantage: Single vertical. We're horizontal across all back-office with domain SLMs per discipline + cross-industry intelligence. Production-grade platform with 95% self-healing pipelines.
Technology Highlights
Production-grade AI/ML architecture built for enterprise scale
4-Tier SLM Hierarchy
Domain-specific Small Language Models for each back-office discipline. SLMs handle 70% of requests at ~$0 cost. Eliminates 80-90% of manual data engineering. LLMs (Haiku, Sonnet, Opus) reserved for complex tasks only.
Neptune RAG + Knowledge Graph
Neptune Analytics with 1024-dimensional Titan v2 embeddings and HNSW indexing. Eliminates $2-5K/month vector DB costs. Proprietary knowledge graph that learns from each deployment.
Delta Intelligence Engine
Change detection, confidence scoring, decision routing. Ensures every automated decision meets quality standards. Confidence-based escalation routes edge cases to humans.
Enterprise Security
Production-grade security with 95% self-healing pipelines, 24 feedback loops, circuit breakers, auto-rollback. Gelfand Validation Framework with 3-phase threshold progression. AWS-native with VPC isolation and encryption.
Enterprise Security & Compliance Architecture
RBAC at API Gateway (JWT/Cognito)
Role-Based Access Control enforced at the API Gateway layer using JWT tokens and AWS Cognito, ensuring least-privilege access across all tenant operations
Immutable Audit Logs (CloudWatch → S3 Glacier)
Every agent action, decision, and escalation logged immutably via CloudWatch with long-term archival to S3 Glacier for compliance and forensic analysis
Data Sovereignty & Tenant Isolation
Per-tenant data isolation with region-pinned processing. Customer data never leaves designated geographic boundaries. Full multi-tenant separation at infrastructure level
PII Redaction Pre-Inference
Personally identifiable information automatically detected and redacted before any data reaches LLM/SLM inference layers, ensuring sensitive data never enters model context
SOC 2 / HIPAA Architecture: Platform architected for SOC 2 Type II and HIPAA compliance from the ground up. Certification target: Q2-Q3 2026.
Pending Patent: Dynamic Table Processing to DAG
Proprietary Innovation: MultiKor builds a dynamic data model based on customer needs, then generates tables and references that can be built with a DAG (Directed Acyclic Graph).
AI Data Ingestion → Dynamic Tables → DAG → RAG: This patent-pending process enables automated, intelligent data transformation and retrieval-augmented generation, making MultiKor uniquely capable of adapting to complex enterprise data environments without manual schema design.
Cloud-Native Multi-Account Architecture
Account 1: AI Services + LLM Infrastructure for orchestration, guardrails, and inference (AWS Bedrock, Azure OpenAI, or GCP Vertex AI)
Account 2: Custom frontend with API Gateway, serverless functions, authentication, and WAF protection
Multi-Modal AI Pipeline: Ingestion (Routing, Intent) → Retrieval (Knowledge Base, Sentiment) → Generation (Responses, Summaries) → Orchestration (SLA, QA, Analytics)
Product Roadmap & Technical Risk Mitigation
Q1-Q2 2026: Pilot Phase
Apexon — Strategic Partner: First customer pilot in negotiation — agentic automation for outsourced operations (5,500 engineers). MVP deployed by Multikor.
Methodology: JTBD analysis to identify highest-value services. LLMs handle all tasks while SLM training begins.
Q3-Q4 2026: Scale + First SLMs
Scale: Scale proven services to additional customers
SLMs: First domain SLMs trained on pilot workflow data
Expand disciplines: Procurement, Kaizen, HR. BPO channel concurrent via Apexon.
2027: SLM Fleet + Series A
Technology: Domain-specific SLMs handling 70% of requests at ~$0 cost
Market: Grow from SMB into mid-market and enterprise. $12M-$15M ARR target.
Funding: Series A ready Q3-Q4 2027.
2028+: Market Leadership
Technology: Full SLM fleet across all disciplines
Channels: BPO channel scaling via multiple partners
Expansion: Geographic expansion. Regulated industries entry.
Key Technical Risks & Mitigation Strategies
Risk: Big Tech Commoditization
Risk: Anthropic, Google Vertex AI, or StackAI ship competitive enterprise solutions. Mitigation: They provide models and infrastructure requiring ML teams. We deliver turnkey production-grade orchestration for SMBs. Domain-specific SLMs + three-layer architecture provide enterprise governance that generic platforms lack.
Risk: SLM Development Timeline
Risk: SLM training takes longer than projected. Mitigation: 4-tier hierarchy means LLMs handle all tasks initially. SLMs progressively take over as they're trained and validated on real workflow data.
Risk: Pilot Failure Risk
Risk: Design partner pilots don't deliver results. Mitigation: JTBD methodology identifies real pain. 24 feedback loops adapt in real-time. Methodology guardrails ensure quality from day one.
Risk: Neptune Infrastructure Costs
Risk: Neptune at $184/day (80.9% of infra). Mitigation: Breakeven at 5 clients. NVIDIA credits offset training. SLM inference cheaper than LLM. Cost structure improves with scale.
Risk: LLM Dependency During Transition
Risk: Reliance on third-party LLMs before SLMs ready. Mitigation: Cloud-agnostic. LLM costs declining 10X/year. SLMs progressively reduce dependency. 4-tier hierarchy ensures graceful transition.