OmniQ Labs builds ThoughtsUPP — a platform that captures group and solo ideas, orchestrates 17 AI agents, and drives them from raw thought to real-world execution.
WhatsApp and Telegram capture conversation, not decisions. Ideas drown in message noise. Decisions made in one thread vanish in another.
MakeMyTrip for flights, Zomato for food, Eventbrite for events. Each solves one domain; none handles the full group decision journey.
Notion and Todoist are built for individuals. No native group consensus, simulation, or cultural awareness for real-life planning.
ChatGPT responds to isolated prompts. It doesn’t persist context, orchestrate specialised agents, or drive multi-step execution.
Desired future state. Personal, Professional, Financial, Health, Social, Spiritual. Short / Medium / Long horizon.
Recurring or composite pattern. Travel, Learning, Fitness, Creative, Worship, Leisure, Shopping, Socialising.
Time-bounded happening. Religious, Lifecycle, Seasonal, Civic, Social, Commercial. From Diwali puja to product launches.
Concrete actionable unit. Done or not done. Always linked to a parent Goal, Activity, or Event. The atomic building block.
Searches flights, compares prices across airlines, optimises multi-city routes, tracks PNR status, and handles baggage and visa transit checks.
Searches hotels, Airbnb, and hostels. Analyses review sentiment, handles group room allocation, compares amenities, and coordinates check-in logistics.
Loose-coupled communication bus. Agents never import each other — new agents are added without modifying existing code.
Purpose-built binary protocol that reduces A2A message size by 85%. Makes multi-agent orchestration fast enough for real-time group planning.
19 external API servers across 7 categories. Each handles auth, pagination, rate limiting, and response normalisation with automatic fallback chains.
Same Event primitive. Different meta: type=Religious, region=India, tradition=Hindu, host_type=Family, scale=Community.
Same Event primitive. Different meta: type=Seasonal, region=North America, tradition=Secular-US, host_type=Self, scale=Family.
Same Event primitive. Different meta: type=Lifecycle, region=Global, tradition varies, host_type=Family, scale=Community. Nests: Reception → Bachelor Party → Bookings → Budget.
OpenAI, Anthropic, Gemini, Local. Automatic fallback chains. Per-task model routing. Token budgets and cost caps.
PII detection & redaction. Toxicity filtering (5 categories). Output hallucination grounding. Custom regex patterns.
4-level audit logging. GDPR/HIPAA/SOX flags. Data retention policies (30-365 days). Consent tracking. Cost alerts.
Prompt injection defense (12+ patterns). Rate limiting (RPM/TPM/daily). RBAC with tier gating. Input sanitisation.
omniq-Graph (free) → omniq-Chain (standard) → LangGraph (pro) → Custom (enterprise). Automatic fallback.
Typed, mode-aware idea objects with bidirectional graph relationships and lifecycle state machine (Thought → Idea → Plan → Execute).
Structured consensus-building (voting, preference graphs, constraint satisfaction) overlaid on existing messaging platforms.
17 parallel agents generate, score, and iteratively refine candidate plans via A2A protocol before real-world commitment.
Binary compression with 16-byte header, agent ID registry, per-pair negotiation, 85% size reduction, and 4 communication patterns.
17-agent coordination with SLM tiers (~8B to rule-based), MCP fallback chains across 19 servers, LangGraph state machines.
Culture as attribute values within a universal ontology of 4 primitives — not separate class hierarchies per culture.
Configuration-only SDK: multi-provider LLM fallback, injection defense registry, tiered engine routing, unified guardrails/compliance/RBAC.