Forge Orbital vs Cogni
A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.
Forge Orbital compared with Cogni
| Detail |
Forge Orbital | C
Cogni |
| What it does | AI accountability that grows into governance
| Memory that reasons
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| Stage | Not given
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| Founded | Not given
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| Based in | US
| US
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| Pricing model | Not given
| Freemium
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| Pricing | Not given
| Free plan at $0 USD; Pro plan at $5 USD per month.
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| Key features | - Governance: checks proposed AI actions against defined rules before execution
- Recorded decision records that can be reviewed, replayed and verified later
- Evaluation: compares AI agent performance using representative test cases and expected outcomes
- Optimization: recommends model and cache candidates based on usage, prices and evaluation evidence
- Regress detection identifies changed action paths and quality regressions
- On-premises and private cloud deployment
- Integration with existing AI tools via connectors and wrappers
- No model training on customer data; separate data boundaries from external AI providers
- Cost comparison and optimization across model and cache configurations
| - Entity-graph spreading activation for cross-vocabulary retrieval
- Works with any LLM model
- No LLM or GPU in retrieval path
- Point-in-time and windowed recall
- Timestamped memories
- Fixed recall cost as memory grows
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| What makes it different | - Independent accountability: checks run inside your systems, not asking another AI to audit AI
- Your people keep authority: Forge produces decision evidence, your business enforces results
- Recorded decision evidence that auditors and regulators can examine
- Designed for consequential work in regulated industries like financial services
- Not another dashboard: integrates into your workflow without replacing your AI tools
| - Reaches answers across unrelated documents that share no keywords, where vector search scores zero
- Deterministic retrieval with no model in the path, so results never vary between queries
- Constant per-query cost regardless of memory size, even at 32,000 words
- No infrastructure to run—no vector database, cluster, or GPU needed
- Works with any model, so small local models gain the most from better retrieval
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| Free plan | Not given
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| Open source | Not given
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| Platforms | Not given
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| Public API | Not given
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| Community votes | 0 | 0 |
| DR (Domain Rating by Ahrefs) | Not checked yet ● no change since last check | 4 ● no change since last check |
| Trust Flow (Majestic) | 0 | 2 |
| Citation Flow (Majestic) | 7 | 21 |
| Referring domains (Majestic) | 22 | 34 |
4 details filled in for both companies.
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