Production-grade agentic AI for SMBs.
Knows when to act. Knows when to ask.
AI workers, not AI tools. Each one returns 10× its cost in operating margin.
10× is the floor. The math is conservative.
Across typical multi-discipline SMB deployments, Multikor returns roughly 15× its cost as operating margin. We anchor public claims to 10× to absorb Year 1 implementation drag.
85% of queries run on our own edge hardware at under $0.003 each, not on cloud LLMs at $0.05. Every output is mathematically validated against the customer's actual data before delivery.
6-tier LayerCake routes queries through Intent classifier → Schema linker (Qwen2.5-1.5B, 94% action accuracy) → Embeddings (BGE-Large + Bedrock Titan v2, 1024-dim) → Sentiment (DeBERTa-v3) → Empathetic rewriter (Qwen3-4B + LoRA) → Cloud LLM (Bedrock Haiku/Sonnet/Opus).
The Multikor Quality Gate runs transversely at every tier, calibrated at the 95th percentile across production embedding nodes.
Margins LLM-first platforms can't match at SMB prices. Accuracy regulated buyers can sign off on.
At $150–$400 per autonomous agent per month (× 1.0–2.0 discipline multiplier), a 50-person SMB deploys production AI at a fraction of the cost of the labor it absorbs.
The Multikor Quality Gate is auditable by design — deterministic confidence thresholds, not prompt guardrails — so outputs survive DD scrutiny in healthcare, financial services, insurance, and government workflows.
Apexon (5,500+ engineers, Goldman Sachs-backed) is design-partner live. Healthcare design partners onboarding. Active discovery pipeline across healthcare, veterinary, claims, financial services back-office. Product stage: private beta (AWS · NVIDIA Inception · Bifrost edge · Tailscale mesh VPN).
Combined Intent classification, Schema linking, and Embedding retrieval handle 85% of typical workloads without invoking a cloud LLM.
The Qwen2.5-1.5B schema linker hits 94% action accuracy on Multikor's internal benchmark (evaluate_and_export_qwen.py, 2026-03-16).
Per-query SLM cost runs under $0.003 on owned edge nodes. Cloud LLM equivalents (Bedrock Sonnet/Opus, GPT-4 class) average $0.04–$0.05 per resolution.
At 85% SLM disposition the effective per-query cost is ~$0.005 versus ~$0.05 LLM-first — a ~90% reduction at the unit level. Total AI infra under $500 per customer per year.
2–4 weeks from kickoff to live workflow for typical scope: 3–6 agents, 2–3 system integrations, schema-anchored.
Apexon design-partner deployment cleared its first production workflow inside this window. E2E integration test reported 96.7% pass rate (MKOR-SLM-013/014).
Operators configure workflows through Multikor's UI. No model training, no platform build, no in-house ML team.
The per-tenant LoRA flywheel improves accuracy automatically as customer staff handle the ~5% of outputs routed to human review by the Multikor Quality Gate.
88% of AI pilots never reach production.
IDC and Lenovo found that only 4 of every 33 AI POCs graduate. For SMB and mid-market operators it's worse: no AI team, no multi-quarter platform budget, fragmented systems, regulators watching. Bigger models don't fix that. Production-grade orchestration does.
Why most pilots stall (IDC/Lenovo AI in Enterprise 2025; Gartner POC graduation taxonomy):
1. No validation layer. RAG produces confidently-wrong answers. Regulated buyers can't ship those.
2. Cost-at-scale. Cloud LLM unit economics break the budget once a workflow exceeds ~100 queries/day.
3. Skills gap. SMB and mid-market operators don't employ ML engineers to maintain a production stack.
4. Integration debt. Fragmented SaaS + legacy systems turn data unification into a multi-quarter project.
5. Governance. Compliance, audit, and explainability requirements that POCs ignore.
6. Change management. Workflows that look elegant in demo don't fit operator reality.
Multikor was designed against these six failure modes: Quality Gate validation, SLM-first economics, no-code operator UI, schema-first integration, auditable confidence thresholds, configurable per-discipline.
The market is moving
$6T services labor versus $1.44T enterprise software. AI spending hits $2.52T in 2026, +44% YoY (Gartner). Agentic AI captures a $450B+ share of enterprise app revenue by 2035. Multikor's $500B+ regulated-SMB workflows TAM sits at the upper end of that trajectory — not as a stretch.
Bottoms-up: ~30M US SMBs, ~10M with 10+ employees concentrated in regulated and admin-heavy verticals (healthcare, veterinary, claims, financial services back-office, professional services).
At 4–8 autonomous agents per business × discipline multipliers (1.0×–2.0×) × per-agent ARR of $1,800–$9,600, the addressable run-rate exceeds $500B.
Conservative against Gartner's $1.44T enterprise software base and $2.52T total AI spending (2026).
Gartner Worldwide IT Spending Forecast (April 2026): AI application software revenue reaches $270B in 2026, up from $158B in 2025.
Highest-growth segment within enterprise software. Total AI spending forecast: $2.52T (+44% YoY).
Gartner long-term outlook: agentic AI captures $450B+ of enterprise application revenue by 2035, redirecting share from traditional CRM, ERP, and BPM categories as autonomous workflows replace seat-based licensing.
Forrester corroboration (Predictions 2026, RES184998): <15% of firms activate agentic AI by year-end 2026 — supply side leads demand for several years, creating window for early platform leaders.
Why Multikor wins, and why it's hard to copy.
Six axes of defensibility. Each is matchable by a single competitor on its own. The combination requires a multi-year platform rebuild.
SLM-first architecture
85% of queries resolve on our edge hardware at under $0.003 each. Total AI infra under $500 per customer per year.
Why it matters: LLM-first competitors lose money at our price points. Foundation model providers can't undercut us without competing with their own API revenue.
6-tier LayerCake: DistilBERT intent (7–10ms latency) → Qwen2.5-1.5B schema linker (94% action accuracy) → BGE-Large + Bedrock Titan v2 embeddings → DeBERTa-v3 sentiment → Qwen3-4B + LoRA empathetic rewriter → Bedrock Haiku/Sonnet/Opus cloud fallback.
Weighted unit cost ≈ $0.005 per query at 85% SLM disposition versus ~$0.05 LLM-first. Total AI infra under $500 per customer per year.
Multikor Quality Gate
Every output is mathematically validated against the customer's own data before delivery. ~95% pass autonomously; the 5% flagged route to the customer's own staff, whose corrections retrain the model.
Why it matters: Catches the failure mode that kills RAG in production — confidently-wrong answers in regulated workflows. No equivalent in the competitive set.
Calibrated at the 95th percentile across production embedding nodes. Confidence threshold 0.90, top-K = 30. Apexon production trace: ~95% PASS, ~5% HITL.
End-to-end integration test pass rate: 96.7% (MKOR-SLM-013/014). Closed-loop self-healing: 142 of 160 prompt versions generated by the regression-remediation loop.
Mathematical formalism is protected as a trade secret pending patent application.
Per-agent SMB pricing
$150-$400 per agent per month, multiplied by 1.0-2.0× by discipline value. Per-autonomous-agent pricing. Not per-seat. Not per-call.
Why it matters: A 50-person business deploys production AI at a fraction of the headcount cost it replaces. LLM-first competitors can't be profitable here.
Three tiers: Automate $150 · Optimize $250 · Transform $400 per agent per month.
Discipline multipliers (deck Slide 8): 1.0× cost-saving (Finance, CS, HR, Procurement, Kaizen) · 1.5× product/ops (future) · 2.0× revenue (Sales, Marketing, Phase 2 Q4 2026).
Typical 50-person SMB: 6 CS Optimize agents × $250 × 1.0× × 12 = $18K ACV on one discipline. Multi-discipline 100-person SMB customers land in the deck's $50-80K Y1 ACV band. License + Annual Support adds ~30% ARR on enterprise on-prem.
Multi-discipline span
Finance, Customer Support, Procurement, HR, and Kaizen at launch. Sales and Marketing in Phase 2. Cross-functional from day one.
Why it matters: Operators land in one workflow and expand into adjacent ones on the same data fabric. Each expansion is the same product, not a new sale.
Day-1 disciplines: Finance & Accounting, Customer Support, Procurement, HR, Kaizen.
Phase 2 (Q4 2026): Sales, Marketing.
Same data fabric and same Multikor Quality Gate across every discipline. Typical land-and-expand path: customer enters via CS or F&A, expands to a second discipline within 2 quarters on the same schema.
On-prem option
AWS-native cloud plus proven on-prem hybrid on NVIDIA GB10 (Bifrost). License + Annual Support captures ~30% additional ARR per enterprise customer, 3-year minimum.
Why it matters: Unlocks regulated buyers pure-SaaS competitors can't serve. Healthcare, financial services, insurance, government.
Reference architectures: AWS-native cloud and NVIDIA GB10 on-prem (Bifrost). Hybrid option: control plane in customer cloud, compute on-prem; outputs governed by the Multikor Quality Gate either way.
License + Annual Support Contract: 3-year minimum, ~30% ARR uplift per enterprise customer. Unlocks HIPAA, SOC 2, PCI-DSS workflows that pure-SaaS competitors can't serve. Broader compliance roadmap (FCA, ISO 42001, NIST AI RMF, EU AI Act) deepens as Trustwise integration matures.
Channel-led scale
Direct SMB sales builds the reference base. White-label channel partners (Apexon-class) multiply that base across their book. Mix evolves from 50/50 at Seed to 30/70 by Series B horizon.
Why it matters: Direct scales linearly with hires. Channel scales geometrically with partner relationships.
Apexon (5,500+ engineers, Goldman Sachs–backed) is design-partner live. Partner P&L: own implementation services + Multikor margin pass-through, white-labeled.
Channel mix evolution: 50/50 direct/channel at Seed → ~40/60 by Series A → ~30/70 by Series B horizon. Each new channel partner is non-linear leverage on the GTM line.
An analyst voice on this
"Individual automation markets like RPA, iPaaS, and BPM have all but converged. The challenge for 2026 will be to figure out how to combine adaptive intelligence with proven controls, balancing innovation with trust."
— Leslie Joseph, Principal Analyst, Forrester (Predictions 2026: Automation and Robotics, RES184998)
Series A is a growth round, not a survival round.
Operating capacity 18-24 months. Operational breakeven targeted near month 15. Series A engagement window M10-M18, from a cash-positive position.
Seed round in progress. Engagement and round detail provided on request via the portal contact form.