Autonomous Agent Infrastructure

We build the engine.
You train the voice.

Self-tuning decision systems. Trained brand voice. Continuous calibration from outcomes. One framework, any vertical. The architecture is production-proven. You bring the domain.

See the proof View the framework 
$ assembl init --vertical=finance --voice=ruthie
assembl Scoring engine configured (9 TA signals, 4 timeframes)
assembl Calibrator initialized (254 keys, auto-tuning hourly)
assembl Voice pipeline ready (label → baseline → compose)
assembl Deploy target: production
Ready. Agent will self-improve from here.

The framework

Infrastructure that learns. Every component feeds forward into the next. Outcomes tune decisions. Engagement trains voice. The system sharpens itself.

01

Decision Engine

Multi-signal scoring with continuous calibration. Evaluates, decides, and learns from outcomes. Domain-agnostic — configure the signals, the engine does the rest.

Multi-timeframe scoring Dynamic entry selection 250+ auto-tuned parameters
02

Voice Pipeline

Deterministic composition, not prompt roulette. Your team labels examples. The system builds a baseline. The LLM enters last — constrained by what proved it works.

Label-driven training 5-segment composition arc Tone/energy compatibility
03

Feedback Architecture

Every outcome feeds the calibrator. Every engagement teaches the voice. Observe, evaluate, adjust, feed forward. The agent gets sharper the longer it runs.

Closed-loop calibration Self-improving skill prompts Persistent learning across sessions

What stays. What's yours.

Framework (identical for every client)

  • ■ Scoring engine with multi-timeframe analysis
  • ■ Calibration engine (auto-tuning from outcomes)
  • ■ Composition engine (5-segment arc, RTC)
  • ■ Training pipeline (label → baseline → LLM)
  • ■ Social intelligence (source tracking, engagement)
  • ■ Content guard & safety layer
  • ■ Infrastructure (CI/CD, monitoring, backup)
  • ■ Self-improving feedback loops

Your configuration (what makes it yours)

  • ◆ Data sources (APIs, feeds, databases)
  • ◆ Signal definitions (your domain's indicators)
  • ◆ Content categories & intent system
  • ◆ Voice pools (brand words, tone, philosophy)
  • ◆ Source tracking (who it follows and learns from)
  • ◆ Risk parameters & guardrails
  • ◆ Deployment targets & scaling rules
  • ◆ Labeling criteria (what "good" looks like)

Any domain. Same architecture.

The framework doesn't know what industry it's in. You configure the sources, signals, and voice. Everything else is identical.

Finance

Trading agents, market analysis, portfolio management, risk assessment

News & Media

Story tracking, editorial voice, source credibility, content curation

Real Estate

Listing analysis, market commentary, lead qualification, client engagement

Sports

Game analysis, trade evaluation, fan engagement, betting intelligence

E-Commerce

Product discovery, review intelligence, brand voice, pricing strategy

Yours

If it has data, decisions, and a voice — the framework fits

The proof

Not a demo. Not a pitch deck. A live autonomous agent running in production since February 2026.

Ruthie

Client Zero — Autonomous Solana Trader

Meme coin discovery, multi-timeframe technical analysis, autonomous trade execution. Scores tokens across 4 timeframes. Enters on the timeframe that screams loudest. Adapts positions in real-time as the multi-TF picture evolves.

Composes in her own voice — southern, sharp, receipts ready. Every trade logged. Every loss published. The receipts are the pitch deck.

ruthie.trade →
4 Timeframes scored
254 Calibrated parameters
9 Analysis modules
24/7 Autonomous

The process

01

Configure

Point the framework at your domain. Data sources, signal definitions, content categories. All config, zero code required.

02

Label

Your team labels examples. "This is worth engaging. Use this tone. This approach works." The system learns what good looks like from your judgment.

03

Calibrate

The engine tunes itself from outcomes. Decision quality improves every cycle. Voice sharpens with every interaction. No manual tuning required.

04

Deploy

Your agent goes live. Autonomous. Self-improving. Composing in your voice. The LLM enters last — constrained by what the system proved works.

Build yours.

The hard part is done. The framework is production-proven.
You bring the domain. We assemble the agent.

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