DATA-AI

Category: AI/ML

DATA-AI (HoursBack v2) A no-code platform that automates recurring business intelligence work using coordinated teams of AI agents instead of single prompts.

You define a workflow a pipeline of specialized agents (data ingestor, analyst, critic, evaluator, delivery) wire them together with plain-English instructions, connect a data source, and set a trigger. The platform runs it end-to-end: pulling data, reasoning over it, self-checking for quality, and delivering the output as a report, email, or webhook.

Every agent decision is logged and visible in real time. Bad outputs get caught by a critic agent and revised automatically. High-stakes decisions can pause and wait for human approval before continuing.

Example 1: Monthly P&L Statement The problem: At the end of every month, your finance team spends 2–3 hours pulling revenue from Stripe, expenses from a spreadsheet, and manually building a Profit & Loss statement. They categorise line items, calculate gross margin, EBITDA, and net income then format a summary for founders and investors. It's the same structure every month but still takes half a day because someone has to reconcile the numbers and write the narrative.

The DATA-AI workflow: Two Data Ingestor agents run in parallel one pulls the month's Stripe charges, refunds, and subscription revenue via API, the other fetches expense data from a Google Sheet or CSV (payroll, hosting, SaaS tools, marketing spend). An Analyst agent then builds the full 5-line income statement: Revenue, COGS, Gross Profit, Operating Expenses, Net Income. It calculates gross margin %, operating margin %, and month-over-month deltas. Any line item that moved more than 15% from the prior month gets flagged. Three Critic agents independently verify the P&L in consensus mode — checking that revenue and expenses balance, margins are calculated correctly, and the narrative matches the numbers. The system picks the highest-confidence review. If any line item swung more than 25% or net income went negative, the workflow pauses and sends the draft to a human for review via the Escalation agent before anything ships. An Eval agent scores the final statement on completeness, specificity, and internal consistency. A Delivery agent generates a formatted PDF, emails it to the founders, and posts a summary to the finance Slack channel.

Trigger: 1st of every month at 9 AM.

Result: A board-ready P&L statement lands in the founders' inbox automatically. Three independent critic agents verified the math. If something looks off say hosting costs doubled or revenue dipped the workflow stops and asks a human before sending. The finance team goes from spending half a day on this to spending five minutes reviewing an escalation, if there even is one.

Example 2: Weekly Revenue Report The problem: Every Monday morning, someone on your team pulls Stripe data, calculates MRR/churn/expansion, writes a summary, and sends it to leadership. It takes 45 minutes. If they're on vacation, it doesn't happen.

The DATA-AI workflow: First, a Data Ingestor agent pulls 13 months of Stripe subscription data via API. Then an Analyst agent calculates MRR, net revenue retention, churn rate, and expansion revenue — producing a structured report with month-over-month comparisons. A Critic agent reviews the output, checking that all metrics are present, figures are internally consistent, and commentary is specific rather than generic. If the critic flags issues, the analyst revises automatically. An Eval agent scores the final report on completeness, specificity, actionability, and tone — if it scores below 0.75, it sends the report back for another pass. Finally, a Delivery agent emails the finished report and stores it in the archive.

Trigger: Every Monday at 8 AM. Result: Leadership gets a board-ready revenue summary every Monday at 8:05 AM. No one has to remember to do it.

In short: DATA-AI turns "someone on the team does this manually every week/month" into "the system does this automatically, checks its own work, and only bothers a human when something actually needs attention."

Key features:

• No-Code Workflow Builder: Define and configure complex, multi-agent AI pipelines using plain English, and trigger them manually, on a schedule, or via webhook.
• Multi-Agent Orchestration: Executes tasks using a coordinated team of specialized AI agents, such as analysts, critics, and evaluators, to ensure high-quality, structured output.
• Live Observability: Monitor workflows in real-time with a detailed event feed that shows what each agent is doing, its reasoning, and the status of the entire pipeline.
• Automated Quality Control: Implements consensus voting and critic agents to review outputs, score quality, and automatically trigger revisions to reduce errors and hallucinations.
• Human-in-the-Loop Gates: Integrate mandatory human approval steps into any workflow, ensuring critical decisions or outputs are reviewed and signed off before proceeding.
• Dynamic Report Rendering: Automatically transforms raw structured JSON output into polished reports with charts, tables, and stat cards, eliminating the need for manual templating.
• Codebase Analysis: Ingest and analyze code directly from sources like GitHub repositories to understand a project's purpose, functionality, and technology stack for automated documentation.

Tech stack: AI, Large Language Model, Next.js, LangChain, Tailwind CSS