AI Capacity
Crucible's AI capacity layer doesn't replace human judgment — it multiplies human throughput. Every automation module has explicit boundaries: what the machine handles, what a human reviews, what gets logged. The result is faster iteration, lower cost per cycle, and decisions backed by more data than any manual process can produce.
Four automation modules
Each module includes what is automated, what is human-reviewed, and what artifacts come out.
Research-to-Product Translation
Converting technical material — papers, patents, lab notebooks — into product requirements, test plans, and commercial viability assessments.
Automated
- Extraction and structuring of technical claims
- Prior art scanning and patent landscape analysis
- Requirement generation from research documentation
- Commercial viability data gathering
Human-Reviewed
- Viability judgment and market fit assessment
- Prioritization of commercial opportunities
- Risk evaluation for technical feasibility
Artifacts
- Structured technical claims document
- Prior art landscape report
- Product requirements draft
- Commercial viability assessment
Customer Discovery & GTM Ops
Interview scheduling and synthesis, ICP iteration, messaging variant testing, competitive intelligence monitoring.
Automated
- Outreach sequencing and scheduling
- Transcript synthesis and pattern extraction
- Messaging A/B variant generation
- Competitive intelligence monitoring and alerts
Human-Reviewed
- Buyer insight interpretation and strategic implications
- Strategic pivot decisions based on discovery data
- Relationship management and partner negotiations
Artifacts
- Customer discovery synthesis report
- ICP refinement document
- Messaging framework with test results
- Competitive intelligence brief
Engineering Ops
Spec generation, QA scaffolding, evaluation harness construction, documentation, release notes, code review triage.
Automated
- Boilerplate generation and spec drafting
- Test scaffolding and QA automation setup
- Documentation drafting from code and specs
- Dependency scanning and security surface analysis
Human-Reviewed
- Architecture decisions and system design
- Security review and threat assessment
- Production deployment approval and rollback decisions
Artifacts
- Product specification document
- QA test suite scaffolding
- Technical documentation
- Release notes and changelog
Capital Workflows
Memo generation, diligence checklists, data room assembly, investor update drafting, financial model scaffolding.
Automated
- Document assembly and data room indexing
- Metric extraction from financial and operational data
- Draft memo and update generation
- Financial model scaffolding from templates
Human-Reviewed
- Investment thesis articulation and refinement
- Valuation analysis and terms negotiation strategy
- Investor relationship strategy and timing decisions
Artifacts
- Investor memo draft
- Diligence checklist
- Assembled data room
- Financial model
- Investor update template
What we don't automate — and why
These boundaries exist because judgment at these points is the value. Automation everywhere else is what creates the capacity for that judgment to be well-informed.
Investment committee decisions
Capital allocation requires judgment that compounds with experience and context.
Founder/client relationship management
Trust is built through human connection, not automated touchpoints.
Ethical and compliance review
Regulatory and ethical decisions require accountability that can't be delegated to machines.
Final gate pass/fail judgment
The decision to proceed, pivot, or terminate an engagement requires full-context human judgment.
Strategic pivots and termination decisions
Existential decisions about company direction are fundamentally human.
Controls and observability
AI operations are governed, not freewheeling. Every output is evaluated, reviewed, and logged.
Evaluation Harnesses
Every automated output is tested against evaluation criteria before it reaches a human reviewer.
Human Sign-off Checkpoints
No automated output ships without human review at each gate in the pipeline.
Decision & Action Logging
Every decision, automated or human, is logged for auditability and learning.
Confidence Scoring
Error rates and confidence scores on all automated outputs, visible to operators and clients.