Solutions

What You Can Build with Grove

Production AI workflows for research, analysis, and automation — orchestrated as DAGs, executed in your cloud.

01

Agentic Research Systems

Build multi-step research workflows where LLM agents iteratively query knowledge bases, follow entity relationships, and synthesize findings into comprehensive reports.

Grove's DAG execution model lets you chain research steps with automatic dependency resolution. Each node can use different tools — semantic search, entity graph traversal, web search — and the engine handles parallelism where steps are independent.

When a draft needs a second pass, a refine node wraps a generator and a critic: the critic inspects each attempt and the generator retries with that feedback until the critic approves or the iteration budget is spent — all inside the workflow, with no client-side retry loop.

External tool execution means your agents can access internal databases and APIs without exposing credentials to the orchestration layer.

Key Capabilities

Multi-step LLM chainsRefinement loopsKnowledge graph traversalExternal tool executionStructured outputSession memory
Contact Sales → 
Input (Company Name)
      |
      v
+--------------+
|  Researcher  | <-- search_entities
|   (Claude)   | <-- traverse_graph
|              | <-- semantic_search
+------+-------+
       |
       v
+--------------+
|  Synthesizer |
|    (GPT)     |
+------+-------+
       |
       v
   Output (Report)
02

Document Processing Pipelines

Extract, enrich, and analyze documents with parallel specialization. Grove's DAG model naturally expresses multi-stage pipelines where each stage can use the best model for the job.

Fan-out to domain-specific extractors — financial terms, legal clauses, technical specifications — then merge results into a unified analysis. Each extractor runs in parallel automatically.

Crash recovery ensures long-running pipelines resume from the last checkpoint if anything fails midway through processing.

Key Capabilities

Parallel fan-out/fan-inMulti-provider routingCrash recoveryStructured JSON outputMerge strategies
Contact Sales → 
Input (Document)
      |
      v
+--------------+
|  Extractor   |
|   (Claude)   |
+------+-------+
       |
  +----+----+
  v    v    v
+---++---++---+
|Fin||Leg||Tec|  <-- Parallel
|   ||   ||   |      Specialists
+-+-++-+-++-+-+
  +----+----+
       v
+--------------+
|    Merge     |
+------+-------+
       v
   Output (Analysis)
03

Multi-Agent Collaboration

Deploy parallel specialist agents that each analyze a different dimension of a problem, then merge their findings with an LLM synthesizer for comprehensive output.

Each agent can use a different model — Claude for nuanced analysis, GPT for structured data extraction, Gemini for summarization — all in a single workflow. Grove routes each node to the right provider automatically.

Real-time SSE streaming lets your UI show progress as each agent completes, giving users visibility into the multi-agent process.

Key Capabilities

Parallel agent executionConditional routingMulti-provider routingLLM-powered mergeReal-time SSE streamingPer-node model selection
Contact Sales → 
Input (Brief)
       |
  +----+----+
  v    v    v
+---++---++---+
|Fin||Mkt||Tec|  <-- Parallel
| C ||GPT||Gem|      Agents
+-+-++-+-++-+-+
  +----+----+
       v
+--------------+
|    Merge     |
+------+-------+
       v
+--------------+
| Synthesizer  |
|   (Claude)   |
+------+-------+
       v
   Output (Memo)

Ready to deploy AI workflows in your cloud?

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