In-app extraction
Agents open the app, navigate to the data, and extract it the same way a person would: screen by screen, on a real device.
Three pains every sales and BD team hits weekly. Each one is what your reps actually complain about, not what a feature page would call them.
The data you need lives inside mobile apps that have no API, and web scraping does not reach it.
Every pipeline below is a shape you wire on the canvas using the crew and tools further down. Not a feature we ship for you, a pattern you configure.
Agents open the app, navigate to the data, and extract it the same way a person would: screen by screen, on a real device.
Scoped to the apps you allow, on a schedule you set, with every run logged and reviewable.
Extracted screens become validated, structured records your pipeline can consume.
Real personas from the research_team crew. Each ships with a tuned system prompt and a default tool allowlist. Swap models per persona on the canvas.
Turns screen-by-screen extraction into clean, validated datasets.
Decides which in-app sources matter and what to pull from them.
Documents each source and keeps extractions reproducible.
Every tool below is a real shared tool from the Melaya bundle. Allowlist per agent; HITL-gate the writes; revoke any of them in one click.
Navigates the app and reads the data from the screen on a real device.
Validates and profiles what came off the screens.
Lands the results where your pipeline expects them.
Builds a searchable record of every extraction run.
Every pipeline ships with three layers of knowledge access. Mix and match per agent on the canvas. No shared vector space with another tenant, no surprise reads, no opaque retrieval.
includeContextPer-pipeline documents appended to specific agents' input on every run. The ICP brief, playbook, pricing sheet, or won-deal email corpus. Whatever needs to be there before the agent thinks. You pick which personas get which docs.
rag_retrieveA scoped tool granted per-agent. When the agent decides it needs more depth, it queries the workflow's vector store on demand. Same knowledge base as Static context, accessed only when the model asks for it.
pipeline_memoryPipeline-level state that carries from one run to the next. Yesterday's research is in scope for today's follow-up. The crew remembers what it already prospected, what got approved, what was sent. The audit log is the second-order knowledge base.
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