| What it does | AI automation company delivering measurable ROI without enterprise prices
| Context infrastructure for AI agents
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| Stage | Startup
| Startup
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| Founded | 2025
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| Based in | US
| GB
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| Pricing model | Paid
| Freemium
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| Pricing | Pilots: $5,000–$15,000 fixed-price per workflow. Retainers: from $1,500/month for up to 15 people, $2,500–$3,000/month for 15–50 people, from $7,000/month for 50–100 people, custom pricing for 100+ people. All retainers include maintenance, model upgrades, new automations each sprint, and support at
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| Key features | - AI Agent Development with OpenAI, Anthropic, and LangGraph
- Workflow Automation connecting CRM, billing, reporting and ops stack
- Fixed-price pilots with ROI verification and documentation
- Voice AI Development for automated phone agents
- Healthcare AI with HIPAA compliance and self-hosted deployment
- AI App Development with RAG and multi-model routing
- On-premises and air-gap deployment options
- Direct access to founder and engineering team with priority response under 4 hours
- Free 30-minute consultation with no obligation or sales pitch
| - Memory system with self-managing advanced memory
- Isolated per-tenant instances with version rollback
- Portable context server with files, notes and links
- Sub-document multi-modal hybrid search
- Structured data extraction from PDFs, websites and images
- Autonomous agents for knowledge base automation
- 99.9% uptime SLA
- AES-256 encryption at rest and SSL in transit
- TypeScript SDK and REST API
- Model-agnostic and framework-agnostic
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| What makes it different | - Measurable ROI-first approach: every engagement defined by business outcomes, not hours delivered
- No lock-in with month-to-month retainers and IP that transfers on day one
- Founder-led technical scoping ensures the quoted architecture is the architecture delivered
- All maintenance, model upgrades, and fixes included in one flat monthly retainer
- Fraction of the cost of hiring an in-house AI team while delivering results from week one
| - Cut token spend by up to 81% through persistent agent memory instead of full context window stuffing
- State-of-the-art on two leading AI memory benchmarks
- Full-stack data layer eliminating need for multiple services
- Production-tested at scale powering Fabric platform
- Self-improving memory that tracks relationships and resolves contradictions
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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 |
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| DR (Domain Rating by Ahrefs) | 25 ▲ +1 (up) since last check | 29 ● no change since last check |
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| Trust Flow (Majestic) | 3 | 11 |
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| Citation Flow (Majestic) | 28 | 32 |
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| Referring domains (Majestic) | 105 | 122 |
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ValueStreamAI
Exabase