Carol — build the whole business, in chat

How it works

Four steps from idea to live service

Every project goes through the same flow. You're only ever in chat — the rest happens behind the scenes. Meet each agent →

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Tell Carol your idea

Open a chat and describe what you want — a blogging site, a fitness tracker, a niche tool. Carol logs the project and pulls in Leo, her strategist.

💬

Carol walks you through the questions

Leo has a short checklist — audience, scope, MVP, hosting. Carol asks them one at a time in plain language. You answer, she captures.

🗺️

Leo drafts the roadmap

When the questions are done, Leo turns the answers into a phased roadmap with cost + time estimates. The work gets filed as concrete tasks for the build team.

🚀

The build team ships it

Elrond's team — designer, developer, tester, compliance — picks up each task. Carol pings you live with every update so you always know what's happening.

Chat→ Requirements walk→ Roadmap→ Build pipeline→ Live service

What people build

An app, a sales engine, a whole back office, a new business

Carol doesn't just build apps. Any team or function you'd hire to run a business — strategy, sales, ops, compliance, security, support, data — she stands up as a team of agents that builds it to your specifications and keeps it running.

📝

A blog for tech leaders

A blogging site for tech leaders.

Who's your reader, and what's the smallest version you'd ship first?

CTOs at Series-A startups. One post a week to start.

Want Bilbo to write the drafts, or just publish what you hand him?

Have him draft. I'll edit.

Done. What angle pulls these readers — hot takes, postmortems, frameworks, or interviews?

Mostly postmortems. Real war stories.

Strong pick — postmortems travel on LinkedIn. Push each post to your newsletter + LinkedIn page automatically too?

Yes, both.

First topic, while it's fresh — what nearly broke at your last company?

🛡️

A security team for our SaaS

A team of agents that runs cybersecurity for our SaaS.

Let's start with the surface — what's hosted where, and who has access today?

AWS for prod, GitHub for code, Google Workspace for the team.

Three monitors then: cloud posture, code leaks, Workspace logins. Slack for criticals, daily digest for the rest?

Yes — and a weekly summary I can show the board.

Adding a board-ready report. SOC2 audit on the horizon, or defense-only for now?

SOC2 by Q4. No roadmap yet.

I'll wire an evidence agent in parallel — pulls audit-ready logs from each system as we go. Pen-test scope: external only, or internal too?

External for now.

First sweep runs the moment you say go. Anything you'd want flagged before that — known weak spots, recent incidents?

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A sales team that opens doors

A sales team that finds leads and reaches out about our product.

Who's the ideal buyer, and where should I source them — LinkedIn, conferences, or a list you already have?

Heads of data at mid-size banks. Pull from LinkedIn.

Sharp niche. Cold email, LinkedIn DM, or both? And what's the hook — case study, demo, free trial?

Both. Lead with the case study.

I'll have an outreach agent personalise per lead. Volume — 20 a week, 50, 200?

Start at 50. Quality over volume.

Right call. CTA: book a meeting, reply for the deck, or just a soft “interested?”

Soft interest. We close on the second touch.

I'll route warm replies straight to your inbox with a one-line summary so you don't read every thread.

🔎

A sanctions investigator

A team that investigates sanctions hits.

Screening new customers, auditing historical ones, or both? And which lists — OFAC, EU, UN?

Both. All three, plus UK HMT.

False-positive rate is the killer here. Tolerance — 5% manual review, 20%?

Aim for under 10%.

Then fuzzy match + a Themis-style reviewer for anything above 0.7 confidence. Audit trail for regulators?

Yes — every decision, who made it, why.

Tamper-evident log going in. PEPs and adverse media in scope, or sanctions only?

PEPs yes, adverse media later.

Want a dry run on 100 names first so we calibrate the thresholds before the real batch?

📑

A data-contracts team

Agents that broker data contracts between producers and consumers.

Internal teams or external partners? And does “contract” mean schema only, or also SLA and PII rules?

Internal for now. Schema + SLA + PII.

Where do contracts live today — Confluence, code, nowhere?

Mostly Slack threads, sadly.

Job one is a registry. An agent scrapes Slack history, proposes contracts, pings producers to confirm. Sound right?

Yes. What about breaking changes?

Every change goes through a diff review. Consumers get a heads-up; producers can't ship until ack'd. Penalties on violation, or just visibility?

Visibility for now. We'll add teeth once people trust the system.

Wise. Want a public scoreboard so producers can see who's hitting their SLAs?

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A ticket-resolution team

A team of agents to support ticket resolution for my app.

What does the queue look like today — Zendesk, Intercom, raw email?

Zendesk. About 200 tickets a week.

Of those, how many are repeats — password resets, billing questions, “where's my X?”

At least half. Easily 100.

Then job one is a triage agent that resolves repeats instantly and escalates the rest. Draft replies for humans to send, or send directly?

Draft first, until we trust it.

Smart. I'll attach a confidence score to every draft — anything below 0.8 stays in the human queue. SLA targets per priority?

1 hour first reply, 24 hours resolution.

Got it. I'll also have the agent spot patterns — same complaint from 5 customers in a week pings your product team automatically.

📊

A data-quality team

Agents that monitor, manage, and improve the quality of my data.

How big — one warehouse, a few tables, or the whole stack?

Snowflake warehouse, ~400 tables. Some critical, most noise.

Start with the critical ones. Which dimensions matter — freshness, completeness, uniqueness, drift?

All four, especially freshness.

Baseline checks per dimension, alert on breach. Where do downstream consumers feel pain first — dashboards, ML models, or finance reports?

Finance reports. CFO sees stale numbers and panics.

Then finance tables get the strictest SLAs. Should the agent auto-quarantine bad rows, or just flag and let humans decide?

Flag for finance, quarantine for ML.

Good split. I'll also have it propose fixes — “this column has 3% nulls, here's the source upstream” — so quality climbs over time, not just gets reported.

Transform your business to run autonomously using agents — by chatting.

Carol's the face. The team does the building.