# Accide

*/Startups/Accide*

## Startup Overview

Software reliability teams lose critical hours during post-mortems manually scraping Slack threads and correlating disparate system alerts. This system parses scattered telemetry logs into verified, second-by-second incident timelines. Responders receive an immediate, factual account of system failures without digging through raw infrastructure data.

Legacy incident management frameworks like PagerDuty and Jira Service Management function as simple routing layers that still rely on manual human investigation. This approach operates entirely autonomously in data extraction, pulling exact error states directly from the environment. The service applies a strict outcome-pricing model, billing exclusively per resolved incident rather than by user seat or log volume.

## Startup Founding Hypothesis

**Approach**: that parses scattered telemetry logs into verified incident timelines
**Competitors**:
- [PagerDuty](/Competitors/PagerDuty)
- [Jira Service Management](/Competitors/Jira_Service_Management)
- [manual Slack thread scraping](/Competitors/manual_Slack_thread_scraping)
**Differentiator2x2**: fully autonomous in data extraction and strictly outcome-priced per resolved incident

## Startup Solution Coordinate

**Solution**: [Accide Timeline Service](/Services/Accide_Timeline_Service)

## Startup Position2x2

```mermaid
quadrantChart
title Incident Timeline Automation vs Pricing Model
x-axis "Manual Log Scraping" --> "Autonomous Telemetry Parsing"
y-axis "Seat-based Licensing" --> "Outcome-Priced per Resolution"
quadrant-1 "Outcome-driven & Autonomous"
quadrant-2 "Outcome-driven & Manual"
quadrant-3 "Seat-based & Manual"
quadrant-4 "Seat-based & Automated Alerting"
"manual Slack thread scraping": [0.10, 0.15]
"Jira Service Management": [0.30, 0.25]
"PagerDuty": [0.55, 0.20]
"Accide": [0.85, 0.90]
```

## Startup Brand

**Voice**: Clinical register defined by strict, unembellished factual precision.
**Tagline**: Verified incident timelines extracted from scattered telemetry logs.
**Icon Concept**: pager
**Palette Intent**: electric-signal
**Visual Identity**: High-contrast neon greens and terminal blacks evoke server monitoring environments, paired with monospaced typography to reflect code-level data extraction.
**Archetype Reference**: the-sage

## Startup Customer Journey

```mermaid
flowchart LR; A[AWS Marketplace] --> B[MCP Registry Agent]; B --> C[Chronological Timeline]; C --> D[Jira Service Management]; D --> E[Enterprise Prepaid Pool]; E --> F[Security Auditor Export];
```

## Startup Proof Points

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

**Pilot Goals**:
- Goal: A 30-day pilot with a cloud-native engineering team running custom JSON telemetry, aiming to successfully map and extract 10+ consecutive resolved incidents within the 10-minute SLA.
- Goal: A 14-day proof-of-concept with an enterprise IT operations team, targeting seamless bidirectional sync with Jira Service Management to prove automated post-mortem ticket updates.
**Target Metrics**:
- Target: 85% reduction in the time Site Reliability Engineers spend manually aligning timestamps across disparate APM platforms.
- Aim: 100% elimination of manual Slack thread scraping for post-mortem creation.
- Target: Under 10-minute generation time from the moment an incident is marked resolved to the delivery of a fully mapped, verified timeline.
- Aim: 100% source verifiability metric, ensuring every node in the timeline contains a raw log ID and source system tag to prevent hallucinations.
**Target Case Studies**:
- Target: A mid-market cloud-native B2B SaaS engineering team transitioning from manual Slack thread scraping to automated timeline generation, significantly reducing the labor hours required to build post-mortems.
- Target: A regulated FinTech engineering organization utilizing the timeline generation to produce compliance-ready incident logs that satisfy security auditors without requiring secondary engineering validation.
- Target: An enterprise Site Reliability Engineering (SRE) department implementing custom JSON schema mapping to unify disparate Application Performance Monitoring logs into a single, chronologically verifiable timeline.
**Testimonial Targets**:
- Target: An SRE Lead expressing relief that their team no longer spends hours manually matching Slack conversations to telemetry timestamps after late-night outages.
- Target: A VP of Engineering praising the cost-efficiency of the UsageMeter pricing, highlighting that they only pay for explicitly triggered post-mortems rather than flat monthly software licenses.
- Target: A Security Compliance Auditor confirming that the generated incident timelines provide instant, reliable source traceability back to the raw JSON logs.

## Startup Top Risks

**Risks**:
- Severity: existential · Description: Customers dispute the definition of a resolved incident to avoid paying under the outcome-based pricing model. · Mitigation Status: unmitigated
- Severity: high · Description: Major telemetry providers like Datadog or AWS CloudWatch restrict API access or alter log schemas, breaking the autonomous extraction engine. · Mitigation Status: in-progress
- Severity: high · Description: Incumbents like PagerDuty bundle native AI-driven log summarization into their existing enterprise tiers for free. · Mitigation Status: in-progress
- Severity: moderate · Description: The parsing engine misinterprets benign system noise as critical events, generating false timelines that erode engineering trust. · Mitigation Status: in-progress
- Severity: low · Description: Site reliability engineering teams resist adoption because the autonomous tool bypasses their established manual post-mortem rituals. · Mitigation Status: unmitigated

## Startup Competitors

- [PagerDuty](/Competitors/PagerDuty) — Incumbent
- [Jira Service Management](/Competitors/Jira_Service_Management) — Incumbent
- [Manual Slack Thread Scraping](/Competitors/Manual_Slack_Thread_Scraping) — Status Quo
- [Datadog Incident Management](/Competitors/Datadog_Incident_Management) — Incumbent
- [Incident.io](/Competitors/Incident.io) — Startup Alternative
- [Rootly](/Competitors/Rootly) — Startup Alternative

## Startup Story Brand V2

**Hero**:
- **Want**: a verified incident timeline ready before the review meeting, without hunting through Slack
- **Identity**: the site reliability engineer who owns post-mortems at a cloud-native company
**Plan**:
- Step: Resolve the incident · Detail: Close it in PagerDuty or Jira as usual; the resolved state is where extraction begins.
- Step: Review the timeline · Detail: A second-by-second draft arrives with every event linked to its source record for instant checking.
- Step: Export the report · Detail: Push the verified timeline into the Jira post-mortem or download the audit copy.
**Problem**:
- **Villain**: manual timestamp archaeology
- **External**: After every resolved PagerDuty incident, the timeline is rebuilt by hand: timestamps copy-pasted from Slack threads and CloudWatch logs into the Jira post-mortem.
- **Internal**: Hours of copy-paste make a senior engineer feel like a court stenographer, dreading the review where one wrong timestamp unravels the credibility of the report.
- **Philosophical**: An engineer's judgment belongs on why the system failed; reconstructing who-said-what-when is clerical work the machines already hold the raw data to do.
**Success**: Every resolved incident produces a source-linked timeline within minutes of closing. The review starts from facts, and the engineer walks in as the analyst instead of the stenographer.
**One Liner**: Post-mortem timelines assemble themselves: Accide parses PagerDuty events, Slack threads, and raw logs into one verified, source-linked incident timeline, so engineers analyze the failure instead of reconstructing it.
**Call To Action**:
- **Direct**: Extract a timeline
- **Transitional**: Walk through one incident
**Failure Stakes**:
- Post-mortems slip days while engineers replay Slack scrollback
- Audit findings stall on timestamps nobody can trace to a source
- Senior engineers burn recovery time on clerical assembly

## Startup Landing Hero Software V2

**Eyebrow**: Incident timeline API
**Subhead**: A verified, source-linked timeline for every resolved incident, assembled from PagerDuty, Slack, and raw JSON logs, ready for the SRE running the post-mortem.
**Headline**: Incident timelines that write themselves
**Supporting Proof**: Every timeline entry carries its raw record ID and source system

## Startup Landing Solution V2

**Section Heading**: From resolved incident to verified timeline in minutes
**Solution Statement**: Accide is an incident timeline extraction engine: an API that parses PagerDuty events, Slack threads, and raw JSON logs into one chronologically ordered record, with every entry mapped to the raw source it came from.

## Startup Landing Features V2

**Benefits**:
- Detail: The engine orders scattered messages and alerts into one sequence within minutes of resolution. · Benefit: Reconstruct incidents without Slack archaeology · Feature: chronological parsing of Slack threads and PagerDuty event streams
- Detail: Reviewers jump from any event straight to the log line that produced it. · Benefit: Verify every entry at a glance · Feature: source mapping that pins each timeline node to a raw record ID
- Detail: Teams define field mappings once and nonstandard log formats parse cleanly afterward. · Benefit: Ingest logs in any shape · Feature: custom schema mappings for raw JSON payloads and OpenTelemetry traces
- Detail: A finished draft lands in the ticket the review meeting already uses. · Benefit: Publish straight into the post-mortem · Feature: timeline export into Jira Service Management issues and PDF audit records
- Detail: Nobody has to remember to start the report; the resolved state starts it. · Benefit: Trigger extraction from resolution itself · Feature: webhook triggers fired when a PagerDuty incident is marked resolved
- Detail: There are no seats to license and no charge for quiet weeks. · Benefit: Pay only for resolved incidents · Feature: metered billing counted per extracted incident timeline
**Section Heading**: The pipeline from resolved alert to finished post-mortem

## Startup Landing Pricing V2

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

**Tiers**:
- Name: Starter · Unit: per incident · Model: usage · Recommended: false · Audience Tagline: For on-call teams proving the workflow on live incidents
- Name: Growth · Model: flat · Cadence: mo · Recommended: true · Audience Tagline: For SRE teams running post-mortems every week
- Name: Scale · Model: flat · Cadence: mo · Recommended: false · Audience Tagline: For platform orgs standardizing audits across teams
**Group Hint**: 2
**Section Heading**: What does a finished post-mortem cost today?

## Startup Landing Faq V2

**Faqs**:
- Answer: Register a schema mapping once: point the parser at the fields that carry the timestamp, message, and severity, and payloads in that shape parse cleanly from then on. OpenTelemetry trace attributes map the same way, so most structured formats need one mapping, not custom code. · Question: What if our logs are custom JSON?
- Answer: Yes. Every timeline entry stores the source system, the raw record ID, and the original timestamp it was extracted from. Reviewers open the raw record behind any entry in one call, and entries with conflicting sources are flagged instead of silently merged. · Question: Can I verify what the engine extracted?
- Answer: No. Accide reads resolved incidents from PagerDuty and exports finished timelines into Jira Service Management, so both stay the systems of record. The extraction layer sits alongside them; remove it later and nothing in either tool breaks. · Question: Do we need to replace PagerDuty or Jira?
- Answer: Raw payloads are parsed into timeline entries and are not kept afterward; what remains is the ordered timeline with record IDs pointing back to the systems that hold the originals. Parsed timelines can be deleted at any time. · Question: What happens to our log data after parsing?
- Answer: On the Starter plan, one extraction of one resolved incident is one billable unit; re-running extraction on the same incident does not bill again. Growth and Scale are flat monthly plans, so extraction volume stops being a billing question entirely. · Question: How is a resolved incident counted for billing?
- Answer: Yes. Slack is one source, not a requirement. PagerDuty events, raw JSON logs, and OpenTelemetry traces are enough to build a verified timeline; a thread can be attached later if the discussion happened somewhere else. · Question: Will it work for incidents handled outside Slack?
**Section Heading**: The questions engineers ask first

## Startup Landing Final Cta V2

**Heading**: Your first verified timeline
**Subhead**: Point it at one resolved incident and the drafted timeline arrives in minutes; nothing writes back to source systems.
**Reassurance**: Read-only against source systems, and parsed timelines can be deleted at any time

## Startup Landing Objection

**Objection**: Will it invent timestamps or fill gaps when the logs disagree?
**Data Handling**:
- **What We Never**: Designed to never write to source systems, never keep raw payloads after parsing, never touch code.
- **What We Touch**: Reads PagerDuty incidents, connected Slack channels, and the log payloads sent to the extraction API.
**Honest Answer**: No entry is generated without a source: every timeline node carries the raw record ID and system it was extracted from, and conflicting timestamps are shown as conflicts rather than silently reconciled. Gaps in the record stay visible as gaps. The founding bet is that verifiable extraction beats fluent summary.
**Residual Risk**: Coverage of nonstandard log shapes still depends on the schema mappings a team defines.
**Mechanism Detail**: Each node stores the source system, record ID, and original timestamp; the review view renders disagreements side by side instead of averaging them.

## Startup Landing Surfaces

**Entities**:
- Icon: lucide:alert-triangle · Name: Incident · Description: A resolved outage or degradation, imported from PagerDuty or opened directly.
- Icon: lucide:list-ordered · Name: TimelineEvent · Description: One ordered entry: a timestamp, the event, and a pointer to its raw source.
- Icon: lucide:database · Name: SourceRecord · Description: The raw Slack message, PagerDuty event, or JSON log line behind an entry.
**Headless**: true
**Functions**:
- Signature: POST /incidents/{id}/extract · Description: Starts timeline extraction for a resolved incident and returns a job handle.
- Signature: GET /jobs/{id} · Description: Returns extraction job state and the finished timeline when complete.
- Signature: GET /incidents/{id}/timeline · Description: Returns the ordered, verified timeline with a source reference on every event.
- Signature: GET /events/{id}/source · Description: Fetches the raw log line, message, or event behind one timeline entry.
- Signature: POST /schemas · Description: Defines a field mapping that teaches the parser a custom JSON log shape.
- Signature: POST /webhooks/resolution · Description: Registers a resolution trigger so extraction starts without a manual call.
**Code Example**:
- **Code**: const job = await accide.incidents.extract("inc_4821", {
  sources: ["pagerduty", "slack", "cloudwatch"],
});

const timeline = await accide.incidents.timeline("inc_4821");

for (const event of timeline.events) {
  console.log(event.at, event.summary, event.source.recordId);
}
// 09:41:07Z  p99 latency breach on checkout-api  cw_88213
// 09:42:15Z  first responder acknowledged        pd_5521
- **Language**: typescript
**Surface List**:
- api
- sdk
- webhook
**Entities Heading**: What a timeline is made of

## Startup Landing Software Workflows

**Workflows**:
- Label: Extract · Title: Extract a timeline on resolution · Description: Start extraction the moment PagerDuty marks the incident resolved.
- Label: Verify · Title: Trace every event to its source · Description: Read the ordered timeline and open the raw record behind any entry.
- Label: Ingest · Title: Teach the parser custom logs · Description: Register a schema once and nonstandard JSON payloads parse cleanly.
**Workflows Heading**: From resolved to reported
**Workflows Description**: Three calls take an incident from resolved state to a verified, exportable timeline.

## Startup Landing Software Code Cards

**Cards**:
- Badge: Reactive · Heading: Extraction on resolution · Description: The resolved state itself triggers extraction, so nobody starts the report by hand.
- Badge: Composable · Heading: Timelines into Jira · Description: The verified timeline drops into the post-mortem ticket the review meeting already uses.
**Code Cards Heading**: More than a log store
**Code Cards Description**: Extraction runs off events and composes into the tools a response team already runs.

## Startup Token Hero

**Genre**: founding-hypothesis
**Rendered**: What if your post-mortem timelines wrote themselves from raw data? Accide parses scattered telemetry logs into verified incident timelines, delivering a factual account of system failures in minutes.
**Mechanism**: spine-derived-v1
**Template Id**: spine-founding-hypothesis
**Vocab Fingerprint**: 2bbb0683a7902546

## Startup Token Positioning

**Genre**: moore-positioning
**Rendered**: Autonomous incident timeline generation for SREs at cloud-native software companies. Unlike manual Slack thread scraping — eliminate manual timestamp correlation for post-mortem reports.
**Mechanism**: spine-derived-v1
**Template Id**: spine-moore-positioning
**Vocab Fingerprint**: 9b1bf475d1f9115e

## Startup Token Pitch Deck

**Genre**: pitch-deck
**Rendered**: Problem: Post-mortems in Jira Service Management require hours of copy-pasting timestamps from disparate logs and unorganized Slack conversations
Solution: What if your post-mortem timelines wrote themselves from raw data? Accide parses scattered telemetry logs into verified incident timelines, delivering a factual account of system failures in minutes.
Customer: SREs at cloud-native software companies
Unlike: manual Slack thread scraping
**Mechanism**: spine-derived-v1
**Template Id**: spine-pitch-deck
**Vocab Fingerprint**: 3840d07d66242cff

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

**Pain**: Post-mortems in Jira Service Management require hours of copy-pasting timestamps from disparate logs and unorganized Slack conversations
**Metrics**: Target: You produce verified, chronologically perfect incident timelines in minutes with zero manual correlation or timestamp alignment.
**Rendered**: Pain: Post-mortems in Jira Service Management require hours of copy-pasting timestamps from disparate logs and unorganized Slack conversations
Economic buyer: SRE / DevOps Manager
Metrics: Target: You produce verified, chronologically perfect incident timelines in minutes with zero manual correlation or timestamp alignment.
Competition: manual Slack thread scraping
**Mechanism**: spine-derived-v1
**Competition**: manual Slack thread scraping
**Economic Buyer**: SRE / DevOps Manager
**Vocab Fingerprint**: 81feecd9d354477e

## Startup Token Cold Email

**Genre**: cold-email
**Rendered**: Subject: Autonomous incident timeline generation for SREs at cloud-native software companies

SREs at cloud-native software companies — Post-mortems in Jira Service Management require hours of copy-pasting timestamps from disparate logs and unorganized Slack conversations What if your post-mortem timelines wrote themselves from raw data? Accide parses scattered telemetry logs into verified incident timelines, delivering a factual account of system failures in minutes.
**Mechanism**: spine-derived-v1
**Template Id**: spine-cold-email
**Vocab Fingerprint**: 2fb67ae566e0b38b

## Startup Token Agent Spec

**Genre**: ai-agent-spec
**Rendered**: Autonomous incident timeline generation. What if your post-mortem timelines wrote themselves from raw data? Accide parses scattered telemetry logs into verified incident timelines, delivering a factual account of system failures in minutes. Serves SREs at cloud-native software companies.
**Mechanism**: spine-derived-v1
**Template Id**: spine-ai-agent-spec
**Vocab Fingerprint**: 8c4eccea5902b208

## Neighborhood

### Candidate solutions

- [Proprietary Deal Target Origination](/Problems/Proprietary_Deal_Target_Origination) — candidate solution for · Problems
- [Tax Season Capacity Bottlenecks](/Problems/Tax_Season_Capacity_Bottlenecks) — candidate solution for · Problems

### Composed of

- [Capacity Allocation Services](/Services/Capacity_Allocation_Services) — composes · Services
- [Task Assignment Worker](/Agents/Task_Assignment_Worker) — composes · Agents
- [Capacity Routing Service](/Services/Capacity_Routing_Service) — composes · Services
- [Document Profiling Agent](/Agents/Document_Profiling_Agent) — composes · Agents
- [Practice Integration API](/Software/Practice_Integration_API) — composes · Software
- [Multimodal Extraction Engine](/Software/Multimodal_Extraction_Engine) — composes · Software
- [Complexity Scoring Engine](/Software/Complexity_Scoring_Engine) — composes · Software
- [Staff Availability API](/Software/Staff_Availability_API) — composes · Software
- [Document Triage Agent](/Agents/Document_Triage_Agent) — composes · Agents
- [Workload Assignment Worker](/Agents/Workload_Assignment_Worker) — composes · Agents
- [Incident Timeline Service](/Services/Incident_Timeline_Service) — composes · Services
- [Telemetry Ingestion API](/Agents/Telemetry_Ingestion_API) — composes · Agents
- [Log Normalization Engine](/Agents/Log_Normalization_Engine) — composes · Agents
- [Thread Extraction Worker](/Agents/Thread_Extraction_Worker) — composes · Agents
- [Telemetry Parsing Agent](/Agents/Telemetry_Parsing_Agent) — composes · Agents

### What it offers

- [Dynamic Workload Router](/Software/Dynamic_Workload_Router) — offers · Software
- [Accide Timeline Service](/Services/Accide_Timeline_Service) — offers · Services
- [Accide Capacity Engine](/Agents/Accide_Capacity_Engine) — offers · Agents

### Embodies

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

### Competitors

- [Master Spreadsheets](/Competitors/Master_Spreadsheets) — competes with · Competitors
- [Offshore Seasonal Contractors](/Competitors/Offshore_Seasonal_Contractors) — competes with · Competitors
- [CCH Axcess Practice](/Competitors/CCH_Axcess_Practice) — competes with · Competitors
- [Thomson Reuters Practice CS](/Competitors/Thomson_Reuters_Practice_CS) — competes with · Competitors
- [Canopy Practice Management](/Competitors/Canopy_Practice_Management) — competes with · Competitors
- [Offshore Staffing Agencies](/Competitors/Offshore_Staffing_Agencies) — competes with · Competitors
- [seasonal offshore contractors](/Competitors/seasonal_offshore_contractors) — competes with · Competitors
- [Static Excel Schedules](/Competitors/Static_Excel_Schedules) — competes with · Competitors
- [Master Scheduling Spreadsheets](/Competitors/Master_Scheduling_Spreadsheets) — competes with · Competitors
- [Offshore Contractors](/Competitors/Offshore_Contractors) — competes with · Competitors
- [Master Excel Spreadsheets](/Competitors/Master_Excel_Spreadsheets) — competes with · Competitors
- [Offshore Temp Contractors](/Competitors/Offshore_Temp_Contractors) — competes with · Competitors
- [Spreadsheet Tracking](/Competitors/Spreadsheet_Tracking) — competes with · Competitors
- [Jira Service Management](/Competitors/Jira_Service_Management) — competes with · Competitors
- [PagerDuty](/Competitors/PagerDuty) — competes with · Competitors
- [Rootly](/Competitors/Rootly) — competes with · Competitors
- [Incident.io](/Competitors/Incident.io) — competes with · Competitors
- [Datadog Incident Management](/Competitors/Datadog_Incident_Management) — competes with · Competitors
- [Manual Slack Thread Scraping](/Competitors/Manual_Slack_Thread_Scraping) — competes with · Competitors

### Who it serves

- [Accounting Firm](/CompanyTypes/Accounting_Firm) — serves · CompanyTypes

### Similar Startups

- [Zoomline](/Startups/Zoomline) — similar · Startups
- [Action](/Startups/Action) — similar · Startups
- [Wholoblem](/Startups/Wholoblem) — similar · Startups
- [Flarekeep](/Startups/Flarekeep) — similar · Startups
- [Stabamber](/Startups/Stabamber) — similar · Startups
- [Outagyard](/Startups/Outagyard) — similar · Startups
- [Sentus](/Startups/Sentus) — similar · Startups
- [Hoppermanor](/Startups/Hoppermanor) — similar · Startups
- [Accit](/Startups/Accit) — similar · Startups
- [Crunchiage](/Startups/Crunchiage) — similar · Startups
- [Problequency](/Startups/Problequency) — similar · Startups
- [Autechanic](/Startups/Autechanic) — similar · Startups
- [Assoblem](/Startups/Assoblem) — similar · Startups
- [Autignal](/Startups/Autignal) — similar · Startups
- [Autoreman](/Startups/Autoreman) — similar · Startups
- [Evorrelate](/Startups/Evorrelate) — similar · Startups
- [Astralagent](/Startups/Astralagent) — similar · Startups
- [Autoturnaround](/Startups/Autoturnaround) — similar · Startups
- [Aftoutage](/Startups/Aftoutage) — similar · Startups
- [Astroblem](/Startups/Astroblem) — similar · Startups