Enterprise AI Solutions Built for Verifiable Outcomes
RCT Ecosystem solves the architectural failures prompt engineering cannot: hallucination, intent drift, context loss, and single-model bottlenecks through constitutional AI infrastructure with auditability built in.
Why Current AI Systems Fall Short
Enterprise AI deployments face fundamental architectural limitations that prompt engineering alone cannot solve.
AI Hallucination (15% error rate)
Intent Misalignment
Context Window Limits
Single-LLM Bottleneck
AI Hallucination Prevention
Reduce hallucination from 15% to 0.3% with Multi-LLM consensus verification via SignedAI — achieving 99.7% accuracy through cryptographic audit trails.
Enterprise AI Memory
Overcome context window limitations with RCTDB v2.0 — a 3-layer hybrid database (Vector + Graph + SQL) with 8D Schema for complete contextual memory.
Dynamic AI Routing
Intelligent Multi-LLM routing across 9 tiers of algorithms — ensuring optimal model selection for every task with cost optimization.
For Enterprise
Deploy AI with confidence — full audit trails, compliance frameworks, and enterprise-grade security.
- 0.3% hallucination rate with SignedAI
- ED25519 + JWT RS256 + RBAC
- 99.98% uptime SLA
- 62 microservices
Enterprise Features
import { RCT } from '@rctlabs/sdk';
const client = new RCT({
apiKey: process.env.RCT_API_KEY
});
const result = await client.execute({
I: "analyze", D: documentData,
A: "summarize", verify: true
});
// { algo: "ED25519", verified: true,
// consensus: ["GPT-4", "Claude", "Gemini"] }For Developers
Build intent-driven applications with our SDK. TypeScript-first, fully typed.
- TypeScript SDK + OpenAPI 3.1.0
- JITNA Protocol RFC-001 v2.0
- 6 Kernel RFCs + docs
- 41 algorithms across 9 tiers
For SMEs
Enterprise-grade AI without enterprise costs — 3.74x cost reduction through RCTDB compression.
View PricingCost Savings
3.74x cost reduction through RCTDB compression and intelligent caching.
Questions Enterprise Evaluators Usually Ask
Which solution should a team evaluate first?
Start with AI Hallucination Prevention if trust, compliance, or accuracy risk is the main blocker. Start with Enterprise AI Memory if context loss and repeated workflow memory are the bottleneck. Start with Dynamic AI Routing if model selection, speed, and cost efficiency are the main concerns.
Do these solutions work together?
Yes. They are designed to work as a system: SignedAI handles verification, RCTDB handles persistent memory, and the routing layer selects the right model and policy path for each task.
What kinds of use cases fit these solutions?
They fit enterprise copilots, regulated workflows, multilingual support operations, retrieval-heavy document intelligence, and any environment where AI outputs need to be auditable and repeatable.