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Production-grade agentic AI for SMBs.

Knows when to act. Knows when to ask.

Agentic, orchestrated, evolving. The orchestration layer that turns AI pilots into governed production workflows. Multikor pairs a 6-tier SLM-first LayerCake with the Multikor Quality Gate, our proprietary validation layer that mathematically checks every output against the customer's actual data before it reaches the user. SMB and mid-market operators get production-grade agents in weeks, without an AI team, on AWS-native cloud or proven on-prem hybrid on NVIDIA GB10.

INVESTOR PRESENTATION

© 2026 Multikor AI, Inc. Confidential. For Prospective Investors. May 2026.

Problem: AI pilots don't become production workflows

The answer isn't bigger models. It's production-grade orchestration.

88%

of observed AI POCs do not reach widescale deployment. IDC research with Lenovo found that only 4 of every 33 AI POCs graduate to production.

No AI team

SMB and mid-market operators have zero AI engineers in-house. They need outcomes, not a multi-quarter platform build.

Fragmented systems

Data trapped across SaaS, EHR/PIMS, documents, email. Pilots fail on integration, not on intelligence.

Trust at the edge

Healthcare, financial services, insurance, and government require validation, audit, and policy controls before any agent ships.

Source: CIO, "88% of AI pilots fail to reach production," March 25, 2025, citing IDC research with Lenovo.

Why Now: three forces converging in 2026

SLMs hit production viability, the deployment gap is widening, and a $6T services economy is shifting to software-delivered work.

01   TECHNOLOGY

SLMs reached production

Domain-specific small language models now deliver task-level accuracy with ~80% lower inference cost and ~60% faster latency than general-purpose LLMs. Edge-deployable on 128GB unified memory.

02   MARKET

Deployment gap widens

The "AI demo to AI in production" gap is the bottleneck, not model capability. Forrester predicts fewer than 15% of firms will activate agentic features in 2026, despite executive interest. The failing pilots are operational failures: integration, governance, drift, ownership.

03   ECONOMICS

Services become software

$6T global services labor versus $1.44T enterprise software (Gartner IT Spending Forecast, 4Q25). AI agents collapse that gap by delivering work, not tools. Gartner forecasts total AI spending at $2.52T in 2026, +44% YoY. SMBs without AI teams are the largest underserved buyer.

Convergence. For the first time, the technology, the market pain, and the buyer are all ready at the same time for an SMB-first, governed orchestration platform.

Sources: Sequoia (Services-as-Software) · Gartner IT Spending Forecast 4Q25 · Gartner AI Spending Forecast 2024-2029 (4Q25) · Forrester Predictions 2026 (RES184998) · Multikor company materials.

Market & Wedge: primary wedge is healthcare operations

Disciplined. Healthcare operations is the primary wedge. The same orchestration platform expands to adjacent regulated, admin-heavy SMB verticals as the wedge proves.

PRIMARY

Healthcare operations

Intake, prior auth, billing & claims, audit.

Veterinary operations

Appointment intake, SOAP, recall, billing.

Insurance / claims

FNOL routing, adjudication support, SIU.

FS back office

KYC/AML, onboarding ops, exception handling.

Government SMB

Permits, eligibility, case routing, audits.

Selection criteria

· Urgency. Pain is hair-on-fire.
· Compliance fit. HIPAA / SOC 2 lines up.
· Repeatable workflow. Same steps across accounts.
· Sales cycle. Weeks, not quarters.
· Data access. Systems we can integrate.
· Willingness to pay. Budget exists today.
57%
of physicians cite reducing admin burden as the biggest AI opportunity
$500B+
TAM across regulated, admin-heavy SMB workflows. Sits within Gartner trajectory: $270B AI app software 2026 → $450B agentic AI by 2035.

Sources: AMA, "Physicians' greatest use for AI? Cutting administrative burdens" (Mar 20, 2025) · Gartner IT Symposium (Oct 2025) · Gartner press release (Aug 2025).

Solution: the orchestration layer for governed autonomous work

Three platform pillars deliver outcomes operators can ship in weeks, without an AI team.

01

Unified Data Layer

Connect SaaS, EHR/PIMS, documents, APIs, email. Entity graph, knowledge/RAG, and context memory turn fragmented systems into a single context surface.

02

Validated Intelligence

SLM-first routing with confidence-based escalation to LLM or human. Outputs validated through the Multikor Quality Gate, our proprietary mathematical validation framework that catches structurally invalid answers before they reach the user. Auditable by design.

03

Self-Healing Operations

Detect, diagnose, repair, escalate, learn. Workflows survive the production handoff where most pilots break.

Weeks
time to a governed workflow, not months or quarters
Zero
AI engineers required. Operators own the workflow.
On-prem · Cloud · Hybrid
data sovereignty for regulated and mid-market deployments

How It Works: Connect, Orchestrate, Execute, Learn

A four-stage operating model. Every workflow flows through it. Same loop in healthcare, veterinary, claims, or back-office ops.

1 · DATA FABRIC

Connect

Ingest from SaaS, EHR/PIMS, documents, email, and APIs. Build the unified entity graph and context memory in days, not quarters.

2 · AGENTIC PLAN

Orchestrate

Decompose work into a DAG of agents and tools. Route each step to the right tier: SLM, LLM, deterministic query, or human review.

3 · GOVERNED RUN

Execute

Run governed workflows with output validation, methodology guardrails, and a review queue for low-confidence cases. Audit-ready by default.

4 · CLOSED LOOP

Learn

24 feedback loops update prompts, routing thresholds, and embeddings on the customer's own data. Self-healing on drift, regression, or outcome miss.

Learn feeds back into Connect, Orchestrate, and Execute. The platform improves on the customer's own data. No human in the loop for routine optimization.

Platform Economics: 6-tier LayerCake with the Multikor Quality Gate

80%+ of enterprise queries never touch a frontier model. Every tier is validated against the live schema graph before output.

Costlier · slower← Routing starts at tier 1
6Cloud LLM. Bedrock Haiku, Sonnet, Opus. Last resort.$0.03 – $0.10 / query
5Empathetic rewriter. Query reformulation.~$0.01 / query
4Sentiment. Tone & intent shading.~$0.01 / query
3Embeddings. BGE-Large + Bedrock Titan v2, 1024-dim.~$0.01 / query
2Schema linker. Qwen2.5-1.5B. 94% action accuracy.< $0.003 / query
1Intent classifier. Route at the edge.< $0.003 / query
Cheaper · faster

Multikor Quality Gate. Transverse guardrail.

Our proprietary validation framework checks every output against the live schema graph, with calibrated thresholds at the 95th percentile across production embedding nodes. Catches answers that look semantically right but are structurally invalid, the failure mode that kills RAG in production.

85%
of production queries resolve at the bottom two tiers
90%
inference cost reduction vs LLM-first architectures
single-digit ms
latency on most operations
128GB
unified-memory edge box runs the full stack

Pricing & Unit Economics

Per-agent tier multiplied by discipline multiplier. Per-agent autonomous pricing is the SMB wedge. LLM-first competitors lose money at these price points.

Platform tiers, per agent / month

TierPer agentBedrock model · Target customer
Automate$150Haiku. SMB land, 1-2 disciplines, light-touch CSM.
Optimize$250Haiku/Sonnet. SMB and mid-market, 2-4 disciplines, dedicated CSM, SSO/SCIM, RBAC.
Transform$400Sonnet/Opus. Mid-market/enterprise, all disciplines, custom dev, SLA-backed.

Discipline multiplier (value-based)

1.0×
Cost-saving. Finance, CS, HR.
1.5×
Product/Ops. Mfg, Dev, CAD.
2.0×
Revenue. Sales, Marketing.

Gross margin by tier

29 – 58%¹
Automate
78 – 88%
Optimize
90 – 95%
Transform

On-prem. Per-agent license at SaaS price plus Annual Support Contract ($25K – $150K by tier). 3-year minimum. Captures ~30% ARR per enterprise customer. Blended GM ~83%.

Unit economics. 85% inference on SLMs at <$0.003/query. 15% Bedrock escalation. 95% autonomous / 5% HITL. Total AI infra <$500 / customer / yr.

¹ Automate GM range varies with on-prem vs Bedrock Haiku mix at small agent counts.

Self-Optimizing Platform: closed-loop engine, not deploy-and-pray

The platform autonomously learns, adapts, and heals using the customer's own data. No human in the loop for routine optimization.

01

Prompt Lifecycle Management

Prompts as living artifacts.

  • Versioned and A/B tested on live traffic
  • Scored against quality thresholds
  • Auto-rollback on regression
02

24 feedback loops, 7 categories

Every signal feeds the producing system.

  • Data quality and model accuracy
  • User signals and outcome metrics
  • Cost, latency, and compliance
03

Self-Healing Pipeline

Detect. Diagnose. Remediate drift.

  • Auto-rollback on regression
  • Auto-escalation to LLM or human
  • Auto-retraining triggers

Closed-loop verified in production. Self-healing detects regression and writes remediation back to the feedback row. Workflows survive production and improve as they run.

Data Integrity & Precision: the Multikor Quality Gate

Schema drift is the silent killer of enterprise AI. Customer data evolves; the AI's understanding doesn't, unless the platform watches for it.

Spectral coherence

Flags when query, retrieved context, and answer fall outside the learned manifold.

Calibrated thresholds at the 95th percentile.

Relevance scoring

Bedrock Titan v2 embeddings (1024-dim) on Neptune Analytics. Degradation triggers automatic recalibration.

Threshold 0.90, top-K 30.

Structural validation

Cross-references results against the live schema graph. Catches answers that score well semantically but are structurally invalid.

96.7% end-to-end integration pass.

Drift response. Quarantine affected queries. Trigger recalibration via the PLM closed loop. Escalate to human review when confidence stays low after recalibration.

Confidence-based routing: deterministic vs semantic

QUANTITATIVE
Deterministic questions

Routed to exact, schema-linked queries. No LLM call needed.

QUALITATIVE
Semantic questions

Validated semantic retrieval with confidence thresholds.

QUANT + QUAL
Hybrid questions

Decomposed, routed in parallel, reassembled via orchestration.

Trust & Enterprise Readiness: governed workflows, with Trustwise oversight

Multikor executes governed workflows. Trustwise is a complementary, independent control layer that can oversee them in regulated enterprise deployments. Multikor remains the orchestration and execution layer.

Multikor

Executes governed workflows.

Orchestration · agents · tools. Validation · self-healing · audit. SMB and mid-market operator focus.

Trustwise. Independent AI control tower.

Oversees agents in production.

AI Control Tower across models / clouds. Shields against tool misuse and data leaks. Large enterprise: healthcare, finance, industrials.

Runtime policy enforcement

Tenant- and regulation-specific policies enforced over agentic workflows.

Compliance roadmap

HIPAA and SOC 2 in flight. FCA, PCI-DSS, ISO 42001, NIST AI RMF, EU AI Act on roadmap as Trustwise integration deepens.

Anomaly & risk signals

Continuous trust signals feed Multikor's self-healing loop with provenance.

Regulated-buyer credibility

Enterprise-grade trust posture accelerates upmarket motion as the wedge expands.

Source: Trustwise public materials.

Go-To-Market: two-motion wedge, direct SMB and channel

A repeatable, outcome-led sales motion in the wedge, paired with a high-leverage channel through implementation and services partners that already own the buyer.

MOTION 1

Direct SMB & mid-market

  • ·ICP: 50-500-employee operators in healthcare, veterinary, claims, insurance back-office
  • ·Outcome-led discovery. One painful, measurable workflow first.
  • ·Pilot to production in 2-4 week sales cycles
  • ·Outcome-based or seat + workflow pricing
MOTION 2

Channel & services partners

  • ·Partners with 10-100 mid-market clients each
  • ·Multikor embedded in the partner's delivery stack
  • ·Co-sell across healthcare, financial services, insurance
  • ·Lower CAC, faster expansion through partner footprints

Land · Expand · Multiply

Land

One painful workflow. Governance built in from day 1.

Expand

Adjacent workflows in the same operator. Same data fabric.

Multiply

Repeat across the partner's book of similar SMBs.

GTM Detail: direct validates, channel scales

Direct sales builds the reference base, PMF, and case studies. Channel partners multiply that base across their existing client portfolios. Direct scales linearly, channel scales geometrically.

Three ICP tiers, entry deal · Year 1 ACV

TIER 1 · LAND

SMB. 50-500 employees.

$9 – $15K / mo. 5-10 agents.

$50 – $80K Year 1 ACV

TIER 2 · EXPAND

Mid-market. 500-2,500 employees.

$30 – $60K / mo. 20-40 agents.

$200 – $500K Year 1 ACV

TIER 3 · MULTIPLY

White-label channel partner. BPO / consultancy / SI.

$40 – $50K+ / mo wholesale. Embedded in delivery.

$480 – $600K Y1 → $1.2M+ Y3

Direct / channel mix by stage

Seed · Q3-Q4 2026
50 / 50
Pre-Series A · Q1-Q2 2027
40 / 60
Series B · 2028
30 / 70

Customer journey (months)

Land
M0-3
Expand
M3-12
Renew
M12+
Reference
M6+

Pilot discipline. 90-day proof-of-value at full retail pricing. 30-day exit clause. No free POCs, no loss leaders.

Competitive Landscape: different category, orchestration for governed SMB work

We don't compete on model size, on enterprise search, or on screen-recording bots. We compete on whether AI work survives the production handoff in an SMB without an AI team.

"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)

CategoryRepresentative playersWhere Multikor differs
LLM / foundation platformsAnthropic, OpenAI, Google Vertex AIThey sell models and infra. We orchestrate routing across SLM tiers and the LLM. Most queries never touch a frontier model.
Enterprise search & copilotsGlean, Microsoft CopilotThey surface answers inside knowledge workers' UIs at 100+ seats. We execute governed multi-step work for operators without an AI team.
RPA / automationUiPath, Automation AnywhereBrittle screen-recording bots that break on UI change. We execute over a unified data layer with self-healing and validation.
Agent builders & frameworksStackAI, LangChain, CrewAIToolkits to build agents. We deliver a production platform with governance, data fabric, and self-healing already in the box.
Vertical SaaS AI add-onsEHR / PIMS native AI featuresBound to a single system of record. We work across the operator's full stack: SaaS, EHR/PIMS, docs, email, APIs.
AI trust & controlTrustwise (complementary)Independent control tower oversees agents in production. We execute governed workflows that benefit from that oversight. Separate, complementary category.

Traction & Status: built, deployed, AWS-native plus on-prem hybrid

Status as of May 2026, company-reported. MVP is in production, the first design partners are live, and pipeline is in active discovery.

PRODUCT

MVP complete

  • ·6-tier LayerCake SLM stack in production with the Multikor Quality Gate validating every tier
  • ·HITL feedback pipeline + Methodology Guardrail + Review Queue
  • ·24 feedback loops across 7 categories with verified closed-loop self-healing
  • ·AWS-native cloud, proven on-prem hybrid on NVIDIA GB10 (Bifrost), Tailscale mesh VPN
DESIGN PARTNERS

Pilots live & queued

  • ·Apexon. Pilot live, 5,500+ engineer BPO (Goldman-backed)
  • ·Healthcare design partners onboarding
  • ·Veterinary design partners onboarding
  • ·Channel partner enablement underway
PIPELINE

Active discovery pipeline

  • ·Healthcare, veterinary, financial services, insurance
  • ·Direct SMB + channel-partner mix
  • ·Outcome-led pilots. 2-4 week cycles.
  • ·Repeatable wedge pattern across verticals

AWS · NVIDIA Inception · Bifrost edge deployment · Tailscale mesh VPN. Product stage: private beta.

Path to Series A: 12-18 month plan from Seed to institutional A

Seed-plan target. Sequenced milestones from design-partner conversion through repeatable revenue and Series A readiness.

M1-M3

Design-partner conversion

  • ·Close 2-3 paying customers from design-partner pipeline
  • ·Lock first vertical reference in healthcare or veterinary
  • ·Stand up partner channel with first implementation / services partner
M4-M6

Land + expand

  • ·Target ~$80K MRR from land-and-expand motion
  • ·Second workflow live in initial customers
  • ·Repeatable 2-4 week pilot-to-production cycle
M7-M9

Operating leverage

  • ·Approach operating breakeven near ~$180K MRR
  • ·10-12 customers, multi-workflow expansion underway
  • ·Channel partners in active co-sell
M10-M18

Series A ready

  • ·Target $6.2 – $9M ARR run-rate
  • ·25-40 customers across wedge verticals
  • ·Institutional Series A syndicate engaged

MANAGEMENT PLAN. REVENUE ARC.

Year 1
10-12 customers
$2 – $4M ARR
Year 2
25-40 customers
$6.2 – $9M ARR
Year 3
75-100+ customers
$25 – $50M ARR

Use of Funds & Headcount Plan

Turn pilots into governed production workflows. Conservative seed operating plan with 18-24 month runway to Series A.

40%

Productization & platform hardening

Hardened SLM stack, prompt lifecycle, self-healing pipeline at production scale.

30%

Sales & GTM expansion

Operator-focused AEs, channel partner enablement, vertical playbooks.

20%

Customer success & deployment

Healthcare, veterinary, channel-led onboarding and retention.

10%

Compliance & operations

HIPAA, SOC 2, audit posture, finance/ops infrastructure.

Headcount plan & monthly burn (18-month operating plan)

FunctionHeadcountFTE addsMonthly burn
Founder leadership (CEO/CTO)2 foundersincluded$42 – $50K
Product & engineering8-10 eng. blended onshore/nearshore+5-7$140 – $180K
Sales & GTM1 SVP + 3-4 operator AEs+2-3$95 – $115K
Customer success & deploy2-3 deployment leads+2$45 – $60K
Ops, compliance & finance1-2 ops/legal + fractional CFO+1$24 – $31K

Ramped burn $346 – $436K / mo (full headcount, M12+). Operating capacity 18-24 months: average burn ~$285K/mo over 18 months (starts ~$175K, ramps as hiring lands), combined with Slide 16 revenue arc puts operational breakeven near M15. Series A at M10-M18 is a growth round, not survival.

Executive team

Suresh Nelakantam
Chief Executive Officer

20+ yrs enterprise tech. Former SVP, RxSense.

Leigh Turner
Chief Technology Officer

20+ yrs engineering leadership across enterprise platforms.

Kimberly Boydston
Chief Architect

15+ yrs enterprise AI, cloud, and data architecture. TOGAF and IL6 credentialed.

Anthony Antonuccio
SVP, Growth & Commercial

20+ yrs enterprise software growth, commercial strategy, and operator-led sales.

Multikor turns AI from pilots into governed production workflows.

Seed round in progress. AWS · NVIDIA Inception · Private beta.

Engagement detail on request via the portal contact form.

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