Melaya · Build high-trust AI agents for any workflow, and put them on your phone. The visual agent builder, with Device Control as its flagship: Claude Code, GPT, or Gemini operating your real phone apps, one verified action at a time, with your approval on every action.

Melaya Mobile · Data

The data lives in apps.So the agent goes there.

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. When the web route is closed, the device route is open.

See the pipelines ↓
01
// What breaks today

The status quo costs more than the agent does.

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.

  1. 01

    The data you need lives inside mobile apps that have no API, and web scraping does not reach it.

02
// Pipelines you can build

Compose. Approve. Replay.

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.

P01

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.

P02

Scheduled, scoped runs

Scoped to the apps you allow, on a schedule you set, with every run logged and reviewable.

03
// The crew

Data extraction crew

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.

Data Scientist

DataScientist

Turns screen-by-screen extraction into clean, validated datasets.

Market Analyst

MarketAnalyst

Decides which in-app sources matter and what to pull from them.

Literature Specialist

LiteratureSpecialist

Documents each source and keeps extractions reproducible.

04
// Scoped tools

Only the actions you grant.

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.

shared/tools/phone/

Navigates the app and reads the data from the screen on a real device.

phone_open_appphone_get_screen_treephone_scrollphone_screenshot
shared/tools/data_utils/

Validates and profiles what came off the screens.

csv_lintdf_describejson_validate
shared/tools/database/

Lands the results where your pipeline expects them.

sql_querysql_export_csv
shared/tools/knowledge/

Builds a searchable record of every extraction run.

build_knowledge_from_textbuild_knowledge_from_file
05
// Three knowledge layers

The crew reads what you give it.

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.

L1

Static context

includeContext

Per-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.

L2

RAG retrieval tool

rag_retrieve

A 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.

L3

Cross-run memory

pipeline_memory

Pipeline-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.

07
// FAQ

Questions we get every week.

What about apps with no API?

That is the point. The agent reads the data from the app screen on a real device, so no API is required.

Is every run auditable?

Yes. Runs are scoped to the apps you allow, scheduled by you, and every step is logged and reviewable.

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