Litepaper
Version 1.0
April 2025
1. Introduction
Doppelgänger.AI is a platform that allows individuals to train and deploy personal AI twins. These digital replicas are modeled on users' real intellectual output, such as text, correspondence, content, and social presence. Doppelgänger.AI offers a transformative way to extend personal productivity, outsource repetitive tasks, and even monetize access to one’s own cognitive model.
2. Vision
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You are not
replaceable
You are
replicable
In an age of hyper-connected, overworked digital labor, Doppelgänger.AI envisions a future where your intelligence becomes an asset that works alongside you. The AI twin is your shadow brain—trained, contained, and ethically controlled by you.
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3. Core Concept
Doppelgänger.AI is a personal AI twin platform. It allows users to:
01
Upload their intellectual work (texts, emails, posts, documents)
02
Connect digital behavior patterns (socials, chats, logs)
03
Train a unique, non-transferable AI instance
04
Delegate work (writing, research, response, ideation)
04
Tokenize access to the twin for third parties
This is not a generalized AI service. It is a you-based model, trained exclusively on your digital footprint.
4. How It Works
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Data Ingestion:
Files and content can be uploaded through a secure, encrypted frontend interface.
Supported formats: .txt, .md, .pdf, .docx, .json, .html, and .eml.
Optional API hooks allow streaming data from Google Docs, Notion, Slack, and GitHub.
Data is chunked, fingerprinted, and versioned before processing.
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Preprocessing & Embedding:
Ingested content is tokenized and normalized using spaCy, NLTK, or custom preprocessors.
Embeddings generated using sentence transformers (OpenAI ADA, Cohere, or in-house BERT variants).
Document embeddings stored in Faiss/Weaviate with metadata tagging (origin, timestamp, semantic topic).
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Model Training:
Each twin is isolated using Docker and scheduled with Kubernetes. Twin workloads are runtime-bound via Firecracker.
Core architecture is fine-tuned LLaMA 3 or Mistral 7B using PEFT methods like LoRA or QLoRA.
For most users, we offer embedding-only customization (no full fine-tune), reducing cost and energy footprint.
Optionally, LoRA adapters can be trained asynchronously with on-demand GPU access.
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Twin Generation:
Twin API endpoints are built with FastAPI, containerized, and proxied via Traefik with service mesh discovery.
Inference APIs support context-aware or stateless calls, with session memory powered by Redis streams.
Logs include request timestamps, prompt fingerprints, and audit trails for compliance and rollback.
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Delegation Interface:
Web-based terminal UI (xterm.js) and CLI tools are bundled via WebAssembly and packaged as native binaries.
Supports prompt chaining, conditional task branching, and dynamic content routing via LangChain workflows.
Queueing and retries handled by Celery with RabbitMQ or Redis backends. Each twin has a worker pool cap.
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Access Control & Tokenization:
ERC-721 tokens represent access keys with JSON schema for scopes, TTLs, and max usage limits.
On-chain logs track delegation, invocation history, and revocations. zk-proofs optional for privacy-preserving ops.
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Caching & Rate-Limiting:
Recurrent prompts are cached at embedding level with TTL using Redis LRU cache.
Rate-limits (token/user/IP) enforced with Redis token bucket algorithm and JWT claims.
Cold starts are minimized using pre-warmed container pools and lazy vector loading.
5. Use Cases
Freelancers:
Automate responses, create drafts, manage gigs.
Founders:
Offload internal communications, outreach, and documentation.
Developers:
Generate commit messages, respond to issues, explain code.
Creators:
Maintain a consistent tone, manage fan interactions, scale content.
Researchers:
Generate citations, review literature, and maintain journals.
Product Teams:
Train internal knowledge twins that align with team voice and protocols.
6. Tokenization Model
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Each AI twin includes programmable access rights. Using web3 infrastructure:
Access to your twin can be tokenized (ERC-721 or custom format)
Rights can include: read-only, limited command sets, temporary delegation
Twin owners can license, rent, or gift usage through secure smart contracts
Smart contracts are deployed via Ethereum-compatible chains (Polygon, Arbitrum), with optional support for zk-based privacy guards.
Interaction logs and usage credits are tied to wallet interactions, ensuring transparent and verifiable access tracking.
A token lifecycle includes creation, activation, access, and expiration. Tokens can optionally expire after inactivity or number of invocations.
7. Privacy & Ethics
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No Central Harvesting: Models are personal and non-aggregated
Full Revocation Rights: Data and model can be deleted anytime
Encrypted Training: Data is AES-256 encrypted at rest and TLS-encrypted in transit
Auditability: Full audit logs available via REST and optional IPFS pinning
Differential Privacy: Embeddings optionally masked with noise layers for shared twin use
Zero-Trust Containers: Each twin is spun up in isolated sandboxes with strict I/O limits
Future enhancements may include Intel SGX enclaves or Nitro confidential compute for trusted inference.
7. Privacy & Ethics
Frontend:
TailwindCSS + React (Next.js), animated terminal interface (xterm.js)
Pipeline Orchestration:
LangChain / custom DAG orchestrator for twin workflows
Backend:
FastAPI microservices with gRPC, RabbitMQ for tasks, and Celery
AI Layer:
Foundation models: LLaMA 3, Mistral 7B, Falcon
Finetuning: LoRA/QLoRA via HuggingFace Accelerate + bitsandbytes
RAG: Faiss/Weaviate indexes with hybrid vector+keyword search
Storage:
IPFS and Arweave for decentralized archiving
Postgres for persistent metadata + Redis for hot-path cache/session/state
S3-compatible blob for transient document storage
Security:
OAuth2 + wallet auth (Sign-In With Ethereum)
TLS everywhere, AES-256 at rest
Firecracker VMs with runtime memory limits, gVisor fallback
Monitoring & DevOps:
Prometheus + Grafana for system metrics
Loki for log aggregation, Sentry for error tracking
GitHub Actions + ArgoCD for CI/CD
All components are modular, API-first, and designed for horizontal scaling and eventual open-source transition.
9. Closing Thoughts
Doppelgänger.AI represents a new model of digital autonomy. It is not about replacing humans with AI—it's about replicating yourself to scale your potential. You own your time, your knowledge, and now, your twin. We believe in open models, accountable design, and permissioned delegation. Doppelgänger.AI is not just infrastructure—it's a philosophy of digitally extending the self.