# Evequence

*/Startups/Evequence*

## Startup Overview

This platform ingests cross-system logs and autonomously sequences them into precise, causal incident timelines. Instead of requiring security analysts to manually piece together disparate alerts, the engine correlates events across network, application, and infrastructure layers to reconstruct the exact chain of actions during a security event.

Incident response teams face an overwhelming volume of fragmented data, forcing them to spend critical hours writing manual Splunk queries to understand the scope of a compromise. The software eliminates this forensic bottleneck by automatically producing a verified, end-to-end narrative of an attack.

Unlike traditional orchestration tools like Splunk SOAR or Cortex XSOAR that rely on rigid playbooks, this architecture is fully autonomous in its timeline reconstruction. The commercial model aligns directly with resolution, abandoning data ingestion limits to charge exclusively on an outcome-priced basis per verified incident.

## Startup Founding Hypothesis

**Approach**: that sequences cross-system logs into causal incident timelines
**Competitors**:
- [Splunk SOAR](/Competitors/Splunk_SOAR)
- [manual Splunk queries](/Competitors/manual_Splunk_queries)
- [Cortex XSOAR](/Competitors/Cortex_XSOAR)
**Differentiator2x2**: fully autonomous in timeline reconstruction and outcome-priced per verified incident

## Startup Solution Coordinate

**Solution**: [Causal Incident Sequencer](/Services/Causal_Incident_Sequencer)

## Startup Position2x2

```mermaid
quadrantChart
x-axis Volume Pricing --> Outcome Pricing
y-axis Manual Scripting --> Autonomous Reconstruction
quadrant-1 Autonomous & Aligned
quadrant-2 Legacy Automation
quadrant-3 Alert Fatigue
quadrant-4 Unscalable Services
Evequence: [0.85, 0.85]
Splunk SOAR: [0.20, 0.70]
Cortex XSOAR: [0.25, 0.75]
Manual Splunk Queries: [0.10, 0.20]
```

## Startup Offer

**Proof**:
- Targeting a reduction in manual log querying time from 4+ hours to under 5 minutes per incident.
- Aiming to successfully map lateral movement across identity, network, and endpoint logs with zero manual regex tuning.
- Designed to integrate seamlessly into existing incident response workflows via standard API webhooks.
**Tiers**:
- Name: On-Demand Timeline · Price: ~$50–$120 per verified incident · Inclusions: Automated reconstruction of a single incident timeline across up to 3 integrated log sources, billed only when a causal chain is successfully sequenced.
- Name: Active SOC Retainer · Price: ~$3,000–$7,500/mo · Inclusions: Up to 100 verified incident timelines per month, priority API processing, and designed to ingest from unlimited standard log sources.
- Name: Enterprise VPC · Price: ~$50k–$90k/yr · Inclusions: Intended for localized deployment within your infrastructure, flat-rate pricing for unlimited timeline sequencing to support high-volume, highly-sensitive environments.
**Guarantee**: If Evequence fails to link a triggered alert to its causal chain and generate a coherent timeline within 15 minutes of log ingestion, the sequence reconstruction is not billed.
**Business Function**: ProvideService
**Objection Handlers**:
- Objection: Our log formats are heavily customized and won't parse natively. Rebuttal: Evequence is designed to use schema inference to dynamically map custom log fields into the universal timeline without manual rule creation.
- Objection: We cannot transmit sensitive raw logs to an external SaaS provider. Rebuttal: The Enterprise VPC package is intended for local deployment, ensuring sensitive event data never leaves your environment.
- Objection: We already use a SOAR platform for automated response. Rebuttal: SOAR platforms execute pre-defined playbooks for known threats; Evequence dynamically reconstructs the unknown causal chains that playbooks miss.
- Objection: Pay-per-incident pricing could blow up our budget during a major attack. Rebuttal: Retainer tiers include configurable monthly volume caps and burst protections to ensure predictable billing.
**Pricing Architecture**: UsageMeter
**Agent Checkout Support**:
- agentic-commerce-protocol

## Startup Brand

**Voice**: A clinical register characterized by unembellished forensic precision.
**Tagline**: Reconstructs cross-system logs into exact causal incident timelines.
**Icon Concept**: logbook
**Palette Intent**: electric-signal
**Visual Identity**: A stark palette of terminal-black and forensic cyan, paired with monospaced typography to reflect the structure of raw server logs.
**Archetype Reference**: the-sage

## Startup Buyer Chain

**Chain**: Evequence → Security Engineering Lead → Security Operations Center (SOC) Analyst
**Gtm Motion**: Acquires security engineering teams by offering a shadow-run proof of concept on historical SIEM data to demonstrate autonomous timeline reconstruction without upfront platform fees. Expands through an outcome-based pricing model that bills per verified incident, scaling naturally as the enterprise routes more complex cross-system investigations to the tool.
**Agent Channel**: Targeted for listing in autonomous SecOps agent directories and structured tool registries (such as LangChain toolkits for security) as a dedicated API endpoint where AI triage agents can request causal timelines rather than processing raw logs themselves.
**Primary Channel**: Search-driven discovery by security architects querying Google for 'automated incident timeline reconstruction' or 'Splunk SOAR alternatives', alongside intended capability listings in SIEM partner directories like Splunkbase.

## Startup Customer Journey

```mermaid
flowchart LR; A[Security Engineering Lead] --> B[Google Search]; B --> C[Historical SIEM Data]; C --> D[First Causal Timeline]; D --> E[SOC Analyst]; E --> F[Active SOC Retainer]; F --> G[Enterprise VPC]; G --> H[LangChain Toolkit];
```

## Startup Proof Points

_Illustrative — target and order-of-magnitude estimate figures, not an achieved track record (this Thing is concept-stage)._

**Pilot Goals**:
- 14-day shadow deployment alongside an existing SOAR platform: Aiming to prove that Evequence dynamically reconstructs the unknown causal chains that pre-defined playbooks miss
- 30-day proof-of-concept testing the on-demand API against 50 historical incident logs: Targeting validation of the schema inference accuracy and the 15-minute turnaround guarantee
**Target Metrics**:
- Target: Reduction in manual incident sequencing time from 4+ hours to under 5 minutes per alert
- Aim: 100 percent elimination of manual regex tuning for standard network, identity, and endpoint logs
- Target: 15-minute maximum SLA from log ingestion to a fully linked causal chain timeline
- Aim: Zero data leakage outside localized infrastructure when deploying the Enterprise VPC package
**Target Case Studies**:
- Targeting a mid-market Managed Security Service Provider: Demonstrate the ability to reconstruct complex alerts across multiple client environments without writing custom parser scripts for each new log source
- Targeting an enterprise SOC team in the financial sector: Validate the Enterprise VPC deployment by sequencing high-volume identity and endpoint logs to track lateral movement while keeping all raw data strictly localized
- Targeting a cloud-native SaaS engineering team: Prove the viability of the On-Demand Timeline tier by mapping causal chains for infrequent but critical security incidents, avoiding the overhead of expensive SOC retainers
**Testimonial Targets**:
- Targeting an Incident Response Lead: Seeking a sentiment of relief that lateral movement is mapped automatically across fragmented logs, allowing the team to focus on threat remediation instead of manual querying
- Targeting a CISO in a highly regulated industry: Seeking confirmation that the localized VPC deployment securely processes sensitive log data without transmitting it to an external SaaS provider
- Targeting a Tier 1 SOC Analyst: Seeking validation that the dynamic schema inference accurately maps custom log fields into universal timelines, removing the friction of constantly updating parsers

## Startup Top Risks

**Risks**:
- Severity: existential · Description: Customers dispute the definition of a verified incident under the outcome-based pricing model, leading to withheld payments and cash flow collapse. · Mitigation Status: unmitigated
- Severity: high · Description: Splunk or Palo Alto Networks bundles an autonomous timeline reconstruction feature into their existing enterprise SOAR platforms for free. · Mitigation Status: unmitigated
- Severity: high · Description: Major log source vendors change their schema formats without notice, breaking the causal sequencing engine and halting autonomous reconstruction. · Mitigation Status: in-progress
- Severity: moderate · Description: High false-positive rates in early deployments require manual intervention by engineers, temporarily eroding margins on the outcome-priced model. · Mitigation Status: in-progress

## Startup Competitors

- [Splunk SOAR](/Competitors/Splunk_SOAR) — Incumbent SOAR
- [Manual Splunk Queries](/Competitors/Manual_Splunk_Queries) — Status Quo
- [Cortex XSOAR](/Competitors/Cortex_XSOAR) — Incumbent SOAR
- [Datadog Incident Response](/Competitors/Datadog_Incident_Response) — Observability Suite
- [PagerDuty AIOps](/Competitors/PagerDuty_AIOps) — Event Management

## Startup Solution Stack

- [Incident Reconstruction Service](/Services/Incident_Reconstruction_Service) — Service-as-Software
- [Causal Correlation Agent](/Agents/Causal_Correlation_Agent) — Agent
- [Log Ingestion Worker](/Agents/Log_Ingestion_Worker) — Agent
- [Cross-System Query API](/Software/Cross-System_Query_API) — Software
- [Temporal Alignment Engine](/Software/Temporal_Alignment_Engine) — Software

## Startup Story Brand

**Hero**:
- **Need**: to be the forensic authority who stops breaches, not the investigator missing signals
- **Want**: to reconstruct complete attack timelines across fragmented system logs instantly
- **Identity**: the SOC Lead at a mid-market enterprise security team
**Plan**:
- Step: Submit logs · Detail: Ingest events from your identity, network, and endpoint sources via standard API webhooks.
- Step: Confirm sequence · Detail: Review the autonomously reconstructed causal chain mapped into a single, coherent forensic timeline.
- Step: Close incident · Detail: Export the verified evidence trail to your ticketing system and eliminate the threat.
**Guide**:
- **Empathy**: Detection windows are won in the first five minutes — but the reality is that log correlation usually takes hours of manual regex tuning.
**Problem**:
- **Villain**: manual Splunk queries
- **External**: Security analysts spend four hours copy-pasting timestamps across Splunk, Cortex XSOAR, and CrowdStrike to find one lateral movement.
- **Internal**: You feel like a low-level clerk hunting for needles in haystacks while an active threat persists.
- **Philosophical**: Forensic expertise belongs in threat mitigation, not in manual log-string correlation.
**Success**: Incident response time drops from hours to minutes with every causal link automatically mapped and verified.
**One Liner**: What if you could turn thousands of disjointed logs into a single causal timeline? Evequence autonomously reconstructs incident paths, reducing manual investigation time by 90%.
**Positioning**:
- **So That**: reconstruct attack timelines in five minutes instead of four hours
- **Unlike**: manual Splunk queries
- **For Whom**: SOC Leads at mid-market enterprises
- **Category**: Autonomous Incident Sequencing Platform
**Call To Action**:
- **Direct**: Verify an incident
- **Transitional**: View sample timeline
**Failure Stakes**:
- Missed lateral movement
- Extended dwell time
- Analyst burnout
**Transformation**:
- **To**: one of the few SOC Leads who masters the full attack surface
- **From**: a researcher buried in raw Splunk logs
**Controlling Idea**: Causal incident reconstruction must be autonomous and forensic.

## Startup Landing Hero

**Eyebrow**: Autonomous Incident Sequencing Platform
**Headline**: See the full attack timeline in minutes
**Supporting Proof**: Built on dynamic schema inference for cross-system causal log mapping

## Startup Landing Hero Services

**Eyebrow**: Autonomous incident sequencing
**Headline**: Exact attack timelines mapped from fragmented logs.
**Supporting Proof**: Powered by dynamic schema inference.

## Startup Landing Hero Headless Saa S

**Eyebrow**: Incident reconstruction API
**Headline**: Sequence logs into causal attack timelines
**Supporting Proof**: Uses dynamic schema inference instead of manual regex.

## Startup Landing Problem

**Cards**:
- Body: You manually align UTC timestamps from CrowdStrike and Okta into a spreadsheet to prove a lateral movement. One missing log row or a slight time-drift between servers breaks your entire forensic sequence, forcing a full restart of the investigation. · Heading: Copy-pasting timestamps into Excel
- Body: You spend critical incident minutes tuning regex strings to bridge data between Cortex XSOAR and legacy system logs. This technical friction delays the 'stop' command while the adversary continues to move through your network undetected. · Heading: Writing complex regex for fragmented logs
- Body: You flip between six open browser windows to follow a single user ID across different security tools. Without a unified causal chain, you lose the thread of the attack, resulting in incomplete remediation and dangerous hidden dwell time. · Heading: Toggling browser tabs to trace sessions
**Section Heading**: Manual Splunk queries shouldn't hold your incident response hostage

## Startup Landing Solution

**Section Heading**: Command your forensics with autonomous event timeline reconstruction
**Solution Statement**: Evequence is an Autonomous Incident Sequencing Platform designed to connect disjointed events from Splunk, CrowdStrike, and Cortex XSOAR into a single causal chain. The system is built to use dynamic schema inference to map lateral movement and credential access across disparate log sources automatically.

## Startup Landing Features

**Benefits**:
- Detail: The engine automatically parses and understands your unique event formats without requiring manual rule creation or constant maintenance. · Benefit: Eliminate manual regex tuning for logs · Feature: dynamic schema inference that maps custom log fields into a universal forensic timeline · Icon Name: Settings2
- Detail: Evequence links disjointed signals into a single causal chain to reveal exactly how an attacker moved between systems. · Benefit: See the full lateral movement path · Feature: causal correlation across CrowdStrike, Cortex XSOAR, and identity logs via API webhooks · Icon Name: GitBranch
- Detail: The system sequences every log into a chronological evidence trail, removing the need for manual spreadsheet coordination. · Benefit: Stop copy-pasting timestamps between tools · Feature: temporal alignment of events from Splunk and network sources into one view · Icon Name: Clock
- Detail: Run the entire reconstruction engine inside your own perimeter to ensure raw event data never leaves your control. · Benefit: Keep sensitive logs within your environment · Feature: local deployment through the Enterprise VPC package for high-volume and sensitive infrastructures · Icon Name: ShieldCheck
- Detail: Generate a complete, verified record of the attack path ready for immediate attachment to any security ticket. · Benefit: Accelerate forensic handoffs to ticketing systems · Feature: automated export of verified evidence trails to your existing incident response platforms · Icon Name: ExternalLink
**Section Heading**: Reconstruct attack timelines in minutes instead of four hours

## Startup Landing Social Proof

_Illustrative — target and order-of-magnitude estimate figures, not an achieved track record (this Thing is concept-stage)._

**Section Heading**: Engineered for autonomous incident timeline reconstruction
**Capability Claims**:
- Maps lateral movement across identity, network, and endpoint logs with zero manual regex tuning.
- Reconstructs incident paths from fragmented system logs into a single causal timeline within five minutes.
- Infers custom log schemas dynamically to map non-standard fields without manual rule creation.
- Integrates with existing incident response workflows via standard API webhooks from CrowdStrike and Splunk.
**Foundation Signals**:
- Built for secure VPC deployment within localized enterprise infrastructure
- Standardized API ingestion for Splunk, Cortex XSOAR, and CrowdStrike data

## Startup Landing Pricing

_Illustrative — target and order-of-magnitude estimate figures, not an achieved track record (this Thing is concept-stage)._

**Tiers**:
- Name: On-Demand Timeline · Price: ~$50–$120 per verified incident · Tagline: For lean security teams needing instant clarity on isolated alerts · Cta Label: Verify an incident · Highlighted: false
- Name: Active SOC Retainer · Price: ~$3,000–$7,500/mo · Tagline: For mid-market SOCs managing continuous incident response volume · Cta Label: Start the API · Highlighted: true
- Name: Enterprise VPC · Price: ~$50k–$90k/yr · Tagline: For high-volume sensitive environments requiring localized data sovereignty · Cta Label: Connect data · Highlighted: false
**Billing Note**: Usage-metered pricing; illustrative bands until live. No charge for unsequenced alerts.
**Section Heading**: Scale your forensic authority with predictable costs

## Startup Landing Faq

**Faqs**:
- Answer: Evequence uses dynamic schema inference to map custom log fields into a universal timeline. You do not have to write manual regex or create parsing rules, as the system identifies event relationships across non-standard data structures automatically. · Question: Our log formats are heavily customized and won't parse in a standard tool.
- Answer: The Enterprise VPC deployment installs Evequence directly within your private cloud or on-premise infrastructure. This architecture ensures that sensitive event data and reconstructed timelines never leave your controlled environment. · Question: We cannot transmit sensitive raw logs to an external SaaS provider for analysis.
- Answer: SOAR platforms execute pre-defined responses to known alert types. Evequence performs dynamic forensic reconstruction, identifying the specific causal links and lateral movements that occurred in a unique attack which your static playbooks cannot predict. · Question: How is this different from the playbooks we already have in our SOAR?
- Answer: The Active SOC Retainer includes configurable monthly volume caps and burst protection. You set the upper limit on processed incidents to ensure your billing remains predictable even during high-volume security events. · Question: Will pay-per-incident pricing blow up my budget during a major site-wide attack?
- Answer: Setup is minimal. Evequence ingests data through standard API webhooks from tools like Splunk, CrowdStrike, and Okta, requiring no proprietary agents or complex data engineering to begin sequencing timelines. · Question: How much time will my team spend configuring integrations for this to work?
- Answer: We do not bill for failed reconstructions. If the engine cannot link a triggered alert to its causal chain and generate a coherent timeline within 15 minutes of ingestion, that specific sequence carries no charge. · Question: What happens if the system fails to find a causal link?
**Section Heading**: Common questions about incident sequencing

## Startup Token Hero

**Genre**: founding-hypothesis
**Rendered**: What if you could turn thousands of disjointed logs into a single causal timeline? Evequence autonomously reconstructs incident paths, reducing manual investigation time by 90%.
**Mechanism**: spine-derived-v1
**Template Id**: spine-founding-hypothesis
**Vocab Fingerprint**: 096730d5ab29f549

## Startup Token Positioning

**Genre**: moore-positioning
**Rendered**: Autonomous Incident Sequencing Platform for SOC Leads at mid-market enterprises. Unlike manual Splunk queries — reconstruct attack timelines in five minutes instead of four hours.
**Mechanism**: spine-derived-v1
**Template Id**: spine-moore-positioning
**Vocab Fingerprint**: 66f0221b1dc4704f

## Startup Token Pitch Deck

**Genre**: pitch-deck
**Rendered**: Problem: Security analysts spend four hours copy-pasting timestamps across Splunk, Cortex XSOAR, and CrowdStrike to find one lateral movement.
Solution: What if you could turn thousands of disjointed logs into a single causal timeline? Evequence autonomously reconstructs incident paths, reducing manual investigation time by 90%.
Customer: SOC Leads at mid-market enterprises
Unlike: manual Splunk queries
**Mechanism**: spine-derived-v1
**Template Id**: spine-pitch-deck
**Vocab Fingerprint**: 472cd2fe5d4d1b1e

## Startup Token M E D D P I C C

**Pain**: Security analysts spend four hours copy-pasting timestamps across Splunk, Cortex XSOAR, and CrowdStrike to find one lateral movement.
**Metrics**: Target: Incident response time drops from hours to minutes with every causal link automatically mapped and verified.
**Rendered**: Pain: Security analysts spend four hours copy-pasting timestamps across Splunk, Cortex XSOAR, and CrowdStrike to find one lateral movement.
Economic buyer: Security Engineering Lead
Metrics: Target: Incident response time drops from hours to minutes with every causal link automatically mapped and verified.
Competition: manual Splunk queries
**Mechanism**: spine-derived-v1
**Competition**: manual Splunk queries
**Economic Buyer**: Security Engineering Lead
**Vocab Fingerprint**: dbbae8c57b0520e1

## Startup Token Cold Email

**Genre**: cold-email
**Rendered**: Subject: Autonomous Incident Sequencing Platform for SOC Leads at mid-market enterprises

SOC Leads at mid-market enterprises — Security analysts spend four hours copy-pasting timestamps across Splunk, Cortex XSOAR, and CrowdStrike to find one lateral movement. What if you could turn thousands of disjointed logs into a single causal timeline? Evequence autonomously reconstructs incident paths, reducing manual investigation time by 90%.
**Mechanism**: spine-derived-v1
**Template Id**: spine-cold-email
**Vocab Fingerprint**: 219cb08e1dd77c05

## Startup Token Agent Spec

**Genre**: ai-agent-spec
**Rendered**: Autonomous Incident Sequencing Platform. What if you could turn thousands of disjointed logs into a single causal timeline? Evequence autonomously reconstructs incident paths, reducing manual investigation time by 90%. Serves SOC Leads at mid-market enterprises.
**Mechanism**: spine-derived-v1
**Template Id**: spine-ai-agent-spec
**Vocab Fingerprint**: 0f023580d5dde18b

## Neighborhood

### Candidate solutions

- [Bioinformatics Talent Sourcing](/Problems/Bioinformatics_Talent_Sourcing) — candidate solution for · Problems

### Composed of

- [Log Ingestion Worker](/Agents/Log_Ingestion_Worker) — composes · Agents
- [Incident Reconstruction Service](/Services/Incident_Reconstruction_Service) — composes · Services
- [Causal Correlation Agent](/Agents/Causal_Correlation_Agent) — composes · Agents
- [Cross-System Query API](/Software/Cross-System_Query_API) — composes · Software
- [Temporal Alignment Engine](/Software/Temporal_Alignment_Engine) — composes · Software

### Embodies

- [Service-as-Software](/Theses/Service-as-Software) — embodies · Theses

### What it offers

- [Causal Incident Sequencer](/Services/Causal_Incident_Sequencer) — offers · Services

### Competitors

- [Datadog Incident Response](/Competitors/Datadog_Incident_Response) — competes with · Competitors
- [Cortex XSOAR](/Competitors/Cortex_XSOAR) — competes with · Competitors
- [Manual Splunk Queries](/Competitors/Manual_Splunk_Queries) — competes with · Competitors
- [Splunk SOAR](/Competitors/Splunk_SOAR) — competes with · Competitors
- [PagerDuty AIOps](/Competitors/PagerDuty_AIOps) — competes with · Competitors

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