The operational data layer for AI-driven organizations

A controlled and auditable operating model for data pipelines feeding BI and AI across clouds and enterprise systems.

PROBLEM

AI fails when data operations are not controlled

• Data is fragmented across clouds, SaaS applications, and core enterprise systems
• Pipelines are assembled from disconnected tools, scripts, and tribal knowledge
• When an incident happens, teams cannot quickly answer: what changed, who owns it, what was delivered
• Result: unreliable AI context, slow decisions, and audit exposure

WHY NOW

The bottleneck shifted from models to data readiness

• GenAI and agents require fresh, trusted context continuously
• Multi-cloud and SaaS are the enterprise default
• Governance, regulatory, and internal audit pressure is increasing
• “We can prove what happened” is becoming a buying requirement

SOLUTION

One operational layer instead of five

integration, execution, quality, delivery, evidence

advanexus connects sources, runs versioned jobs, enforces rules, produces governed datasets, and generates delivery evidence for BI and AI.

• No full stack rebuild
• No disruptive platform migration
• One operating model between sources and consumers

HOW IT WORKS

From fragmented pipelines to AI-ready context

POSITIONING

We complement winners, we do not replace them

• Confluent: data in motion
• Databricks / Snowflake: storage and analytics
• advanexus: control, accountability, auditability
• We sit between sources and consumers, where failures become business risk

VALUE

Control reduces cost and risk

• Deterministic execution and reproducibility
• Traceability from source to delivery, including context and enforced rules
• Explicit ownership for datasets, rules, and deliveries
• Audit readiness by default, evidence produced during execution
• Fewer tools, lower complexity, faster time to value

MARKET

A wedge into a large and expanding spend category

• Buyers: data platform teams, heavily influenced by risk, compliance, and internal audit
• Targets: regulated and complex enterprises where auditability is non-negotiable
• Entry wedge: a small set of critical flows where failures have measurable cost
• Expansion: more flows, higher criticality, broader delivery obligations, enterprise rollout

BUSINESS MODEL

Scope-based annual contracts with clear expansion dynamics

• Annual subscription priced by operational scope and criticality, not users or features
• Typical ACV ranges:
  • Controlled entry scope: €20k to €50k
  • Business-critical scope: €60k to €150k
  • Audit-critical enterprise scope: €180k+
• Expansion drivers:
  • More flows, higher frequency, stricter controls
  • External deliveries and accountability obligations
  • SLAs, governance alignment, premium support

COMPETITION MAP

Operational control vs analytics focus

MOAT

Why it is hard to copy

1. Core runtime, not a feature
   advanexus is not an add-on to an existing stack. It is an operational layer that defines how a run behaves: execution identity, context, rules, delivery, and evidence are one lifecycle.
   To copy this, competitors must change the core execution model, not add a module.

2. Evidence plus deterministic replay by default
   Evidence is produced during the run, and deterministic replay enables independent verification (same input → same output). This requires a disciplined runtime: versioning, dependency control, standardized context, and repeatable execution.
   Retrofitting this into mature platforms is slow, risky, and expensive because it changes the core, not the UI.

3. Lock-in through critical flows and operational accountability
   Once evidence, replay, and ownership become the standard for business-critical and audit-critical flows, switching costs increase sharply: revalidation of controls, rebuilding evidence, migration risk, and renewed audit alignment.
   This is not a tool swap. It is an operational change with measurable risk.

GO-TO-MARKET

Start where pain is highest and budgets exist

• Regulated verticals first: banking, insurance, telecom, healthcare
• Land: 3 to 10 critical flows, governed delivery to BI and AI consumers
• Expand: lineage, evidence, cross-domain ownership, enterprise governance
• Channels: direct enterprise sales, integrator partnerships, focused thought leadership

TRACTION

Early validation with clear proof points

• Working platform and demo environment exist
• Validated through real enterprise scenarios and feedback loops
• Differentiation confirmed: operational control and auditability, not another analytics tool

6-MONTH PLAN

Milestones and KPIs

• Milestones
  • 3 design partners
  • 1 to 2 paid pilots
  • Production v1 (security, evidence, replay, scoped onboarding)
• KPIs
  • Signed design partners: 3
  • Paid pilots launched: 1 to 2
  • Time to first critical flow: ≤ 4 weeks
  • Controlled critical flows per customer: 3 to 10
  • Evidence coverage: 100% governed deliveries
  • Replay success rate: ≥ 95% on target flows
  • Conversion: at least 1 pilot → annual contract

TEAM

Founder-led execution with deep domain advantage

• Founder with 10+ years in complex enterprise and regulated data operations
• Built and operated systems in banking environments with real audit constraints
• Hands-on product and engineering delivery
• Supported by business development and go-to-market advisory

ASK

Funding to productize and scale go-to-market

• We are raising capital to:
  • Build a focused product team (core, governance, reliability)
  • Harden security, audit evidence, and operational guarantees
  • Productize onboarding and scoping into a repeatable deployment model
  • Execute design partners and paid pilots through focused go-to-market
• Target outcomes:
  • 3 design partners
  • 1 to 2 paid pilots
  • 1 pilot converted to an annual contract
  • Production version ready for broader enterprise rollout