One engine. Modular actions. Your domain.
Momor Orchestration understands intent, executes parallel actions against multiple data sources, and synthesizes grounded answers. Consumer search and enterprise deployments run the same engine — the only thing that changes is which actions are on the board.
Momor Orchestration
Every query enters the same pipeline. Intent interprets the task, actions execute against data sources, and synthesis decides what happens next — finish, continue with more work, or pause for human input. The pipeline is recursive. Synthesis can spawn follow-on actions or re-enter the full pipeline from intent when it needs more data.
Intent
A larger, more capable model reads the query and determines what the user is actually seeking — not just the keywords, but the intent, implicit context, and what kind of answer would resolve this question. It produces a structured plan of which actions to fire, in what order, against which data sources.
Parses ambiguity and natural language into a structured plan of intent actions. It determines which tools the query needs, produces a per-action optimized query for each one, and detects when information is missing or the task is ambiguous.
A query about 'contingency deadline on the Park Avenue deal' fires the ContractReview action on the active contract, extracts deadline clauses, and cross-references with calendar data.
The intent model is the most expensive call in the pipeline and the most critical. The system automatically selects the best available model — Claude, GPT, Gemini, Kimi, DeepSeek, or others — based on what the query demands.
Action
Domain-specific actions fire in parallel against the data sources the intent model identified. Each action is modular — it talks to one data source and returns structured results. Actions run independently, and if any individual action fails, the system recovers automatically without affecting the others.
Synthesis
The synthesis model takes the accumulated results from all actions and produces a grounded response with source attribution. It writes from the retrieved data, with every claim traceable to a source. This is the two-model cost split — spend on understanding at the intent stage, optimize for grounded output at synthesis.
If the synthesis model identifies a gap — a question that needs one more data point — the pipeline recurses. It can fire a follow-on action directly, or re-enter the full pipeline from intent through action and back to synthesis. No restart, no lost context. A mid-stream course correction. If the system encounters ambiguity or a judgment call that belongs to a person, it stops and presents the context. Findings that are material but non-blocking travel alongside the result as advisories.
Threaded, cited responses with source attribution. Every claim traceable to the data that generated it. Results build in real time, with citations the reader can trace back to the original source.
Results are cached at every layer — intent analysis, individual action results, and synthesized answers. Cache durations are tuned per data type: weather expires in minutes, encyclopedia results last for days. Subsequent queries that hit cached data skip the external call entirely.
Same board. Different pegs.
Adding a new vertical to Momor means adding new actions to the board. The intent model learns what to trigger. Actions run in parallel alongside everything else. Synthesis handles whatever comes back. The core engine never changes.
| Consumer pegs Live today | Enterprise pegs Per-client deployment | ||
|---|---|---|---|
| SearchWeb | Web search via SERP | DocumentReview | Parse and query documents |
| SearchNews | News source aggregation | CompanyAnalysis | Company information |
| GetWeather | Location-aware weather data | CaseResearch | Case record synthesis |
| GetStock | Stock and market data | DataExtraction | Structured record extraction |
| GetPlaces | Location and directions | ComplianceCheck | Regulatory cross-reference |
| Calculate | Computation and conversion | InvestigationSearch | Investigation record search |
| SearchImages | Image search and retrieval | ||
| SearchAcademic | Academic paper search | ||
Adding a new action to the architecture does not require changing the core system. New actions are defined, the intent model learns when to surface them, and they run alongside everything else. That is how a single architecture serves legal, healthcare, procurement, and investigative workloads.
Four layers. Zero single points of failure.
Enterprise means your operations depend on this running. Momor's resilience is not a single failover — it is four independent layers, each catching what the one above it misses.
Parameter resilience
Every external call has built-in retry logic. If a service hangs or a network blip occurs, the system retries with increasing wait times and knows when to stop trying a service that is clearly down. Transient failures are absorbed before they reach anyone.
Provider failover
Every external dependency — AI models, search APIs, weather services — has multiple providers behind it. If one provider fails completely after all retries are exhausted, the system switches to the next one automatically.
AI model failover
If an AI model cannot handle a query or its provider is unreachable, the system routes to a different model or provider entirely. The failover is capability-aware — if the query includes images, it skips text-only providers.
Architecture degradation
Even if multiple systems degrade simultaneously, the architecture returns something useful. If deep analytics times out, return search results. If everything is slower than normal, the system still delivers a grounded answer from whatever completed.
No single vendor owns your pipeline.
Momor Orchestration runs across multiple AI providers simultaneously. The system selects models based on query complexity, available capacity, and cost — then fails over automatically if anything goes wrong. You are never locked in.
The provider list grows as the market evolves. Adding a new provider requires no architectural changes — the same failover pattern that manages AI models also manages search APIs, weather services, and every other external dependency.
See it in action.
The consumer product at momor.ai runs Momor Orchestration in production every day. The enterprise version runs the same engine with different actions for your domain. Tell us what you are building and we will show you how it fits.
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