Continua vs ChatGPT
Full-corpus AI for high-stakes decisions
ChatGPT is a broad general-purpose AI assistant. Continua is built for a different problem: turning massive private and public evidence into a structured, inspectable model for high-stakes decisions.
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Capability matrix
| Capability | Continua | ChatGPT |
|---|---|---|
| TB-level ontology processing | ✅ Core | ◐ Promptable, not a core product promise |
| Up to 1B-token working context | ✅ | ❌ |
| 100% page / corpus coverage | ✅ Core promise | ❌ No equivalent guarantee |
| Web search / deep research | ✅ | ✅ |
| Private + web synthesis | ✅ | ✅ |
| Entity linking | ✅ | ◐ Promptable |
| Policy / context / state reconstruction | ✅ | ◐ Promptable |
| Cross-document contradictions / gaps | ✅ | ◐ Promptable |
| Whitebox investigation steering | ✅ | ◐ Research-plan/source steering |
| Multiple LLM vendors | ✅ | ❌ OpenAI models |
| Self-improving skills / RSI / evolution | ✅ | ❌ Not a public product capability |
| Persistent Projects | ✅ | ✅ |
| Data rooms | ✅ | ◐ Shared Projects/files, not a purpose-built VDR |
| Translation | ✅ | ✅ |
| Deal Q&A | ✅ | ◐ Promptable |
| Sharing + permissions | ✅ | ✅ Shared Projects |
| Viewer analytics | ✅ | ❌/◐ Not core |
| BI / dashboards / visualization | ✅ | ✅/◐ Data analysis and charts |
| Zero data retention | ✅ | ◐ Different plan/workspace retention controls |
Where Continua is stronger
- TB-level ontology processing and 1B-token effective context.
- Explicit complete-corpus coverage.
- Entity, policy, context and state modeling as first-class investigation objects.
- Multi-vendor model orchestration.
- Zero-retention investigation mode plus persistent Projects/data rooms.
- High-stakes evidence workflows with contradictions, gaps, timelines and causal analysis.
Where ChatGPT is stronger
- General-purpose writing, coding, creative work and broad assistant workflows.
- Mature consumer ecosystem.
- Deep Research across web, files and connected apps.
- Strong general-purpose Projects.
Why Continua is different
Process ontology from TB-level big data
Continua is designed to process TB-level heterogeneous data into a structured ontology of entities, relationships, policies, rules, constraints, context, states, claims, contradictions, gaps, timelines and causal relationships.
Up to a 1B-token working context
Continua can reason over an effective working context of up to 1 billion tokens, enabling investigations across corpora that exceed the native context of a single frontier model.
Full-context coverage
Continua is designed around 100% page / corpus coverage for investigations where one buried clause, addendum, event, requirement or contradiction can change the outcome.
Whitebox reasoning and steering
Users can steer what the investigation looks for, how evidence is compared, what criteria matter and how conclusions are composed.
Projects, data rooms and analytics
Continua can support persistent Projects, data rooms, sharing, permissions, viewer analytics, translation, deal Q&A, BI, dashboards and data visualization.
Multi-model self-improvement
Continua can orchestrate multiple LLM vendors and evolve investigation skills through self-improvement, RSI and evolution strategies.
ChatGPT answers across many tasks. Continua reconstructs the evidence world and reasons over it.
Best-fit use cases
Frequently asked questions
Does Continua support web research?
Yes. Continua supports web source discovery and web research in addition to private-data analysis.
Does Continua support persistent Projects and data rooms?
Yes. Continua can support persistent Projects and data rooms, with sharing, permissions, viewer analytics, translation and deal Q&A.
Can Continua build dashboards?
Yes. Continua can support BI, dashboards and data visualization alongside document, structured-data and web evidence.
Does Continua always retain uploaded data?
No. Continua supports a zero-data-retention mode for sensitive investigations; persistence can be enabled intentionally for Projects and data-room workflows.
Make the complete evidence set computable
Process the files, web sources, structured data, policies, relationships and states — not just the passages a retrieval layer thinks are relevant.
Start with ContinuaRun Your First Investigation Free
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