Comparison

Continua vs Gemini
TB-scale evidence reasoning vs multimodal AI

Gemini is a powerful multimodal AI tightly integrated with Google's ecosystem. Continua is built to process enormous evidence sets into an explicit ontology and reason across the full decision context.

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Capability matrix

CapabilityContinuaGoogle Gemini
TB-level ontology processing✅ Core◐ Promptable
Up to 1B-token working context
100% page / corpus coverage✅ Core promise❌ No equivalent guarantee
Web search / Deep Research✅ Major strength
Private + web synthesis
Entity linking◐ Promptable
Policy / context / state reconstruction◐ Promptable
Contradictions / gaps / root cause◐ Promptable
Whitebox reasoning / steering
Multiple LLM vendors❌ Google model stack
Self-improving skills / RSI / evolution❌ Not a public product capability
Persistent Projects✅/◐
Data rooms
Translation✅ Major strength
Deal Q&A◐ Promptable
Sharing + permissions
Viewer analytics
BI / dashboards / visualization✅/◐ Strong Google data ecosystem
Zero data retention◐ Depends on product/deployment

Where Continua is stronger

  • TB-level ontology processing and 1B-token effective context.
  • Explicit 100% page/corpus coverage.
  • Entity linking and state reconstruction as core evidence objects.
  • Vendor-neutral model orchestration.
  • Whitebox decision reasoning and zero-retention mode.

Where Gemini is stronger

  • Native multimodality.
  • Google Search grounding.
  • Google Workspace ecosystem.
  • Broad consumer and enterprise AI capabilities.

Why Continua is different

01

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.

02

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.

03

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.

04

Whitebox reasoning and steering

Users can steer what the investigation looks for, how evidence is compared, what criteria matter and how conclusions are composed.

05

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.

06

Multi-model self-improvement

Continua can orchestrate multiple LLM vendors and evolve investigation skills through self-improvement, RSI and evolution strategies.

Gemini is a powerful multimodal model ecosystem. Continua is the evidence operating system around the decision.

Best-fit use cases

High-stakes due diligenceLegal and contract reviewPolicy and compliance analysisMedical or claims chronologyRFP and requirements reviewEnterprise knowledge / ontology processingCross-document contradictions and missing informationRoot-cause analysisExecutive decision supportBI and dashboarding where the numbers require documentary context

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.

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