Saga — The analyst that never clocks out

Introducing Saga

The analyst that never
clocks out

Saga operates directly on the data lake, runs on a standing brief, and pushes finished intelligence to you — before you think to ask. It has a job, not a chat window.

Saga — active brief · running since 30 days

Standing brief received
Track brand sentiment shift in Gen Z segments across 4 markets, weekly
Briefed
Novel cluster detected
Emerging affinity group not in existing taxonomy — +23% volume this week
Escalated
Monday brief delivered
4-market competitive scan, 200 high-signal mentions, brand brief attached
Delivered
Watching_
Next escalation threshold: audience shift > 2 std. deviations
Running
AI Copilot

Has a chat window

Waits for a question. Answers it. Forgets the moment you close the tab. Produces nothing when nobody's asking.

Saga

Has a job

Briefed once, runs indefinitely. Watches the data, escalates when something matters, pushes finished work to you on its own clock.

How Saga works

Four things that make it different

Not a faster way to ask questions. A way to stop asking.

01

Brief-driven,
not prompt-driven

Brief Saga once and it runs on its own clock — watching, escalating, and pushing finished intelligence to you. You don't re-open it. You don't re-ask. It produces work while you're doing something else.

Prove it: brief Saga and a copilot the same way. Walk away for 30 days. One produces a stack of analyses tied to real data movements. The other produces nothing.

02

On the data lake,
not the dashboard

Every copilot in this category sits on top of pre-aggregated analytics. Saga works directly on the raw corpus — novel clustering, custom embeddings, statistical work no dashboard pre-defines. It does what the product doesn't ship.

Prove it: ask the same novel-clustering question. Saga returns emergent affinity clusters not in any taxonomy. A copilot returns a summary of an existing dashboard.

03

Finished work,
not summaries

A copilot narrates the dashboard it's been handed. Saga produces the deliverable — the brand brief, the competitive scan, the cultural read — multi-market, on one methodology, comparable by construction. It replaces the analyst's output.

Prove it: the Monday Brief lands in the inbox before standup. A 4-market scan on one prompt. 200 high-signal mentions, not 20,000 raw ones.

04

Methodology that
compounds

A copilot is stateless — every session starts from zero. Saga captures your team's approach as named, versioned, owned prompt libraries that survive attrition and scale across hires. Your method becomes infrastructure.

Prove it: a junior analyst producing senior-grade output from the captured house method — library named, owner credited.

The category vs Saga

Saga operates on the data lake, not the dashboard. It has a job, not a chat window.

AI Copilot (everyone else) Saga
Mode Waits for a question in a chat window
Briefed once, runs indefinitely on its own clock
Data access Chat layer over pre-aggregated analytics — capped at what the reporting product exposes
Operates directly on the raw corpus — novel clustering, custom embeddings, statistical work no dashboard pre-defines
Output Summary of an existing dashboard or report
Finished deliverables — brand briefs, competitive scans, cultural reads — multi-market, one methodology
Memory Stateless — every session starts from zero
Named, versioned prompt libraries that capture team methodology and survive attrition
Data corpus Open-web scrape or whatever the vendor's index covers
15 years of permissioned, audience-grade data — not open-web scrape

What it looks like in practice

Finished work, not prompts

Monday brief

30-day

Standing brief, zero re-prompts

Brief Saga once on a Monday. Every Monday after that, the brief lands in your inbox before standup — with last week's signal, not last year's taxonomy.

Multi-market scan

4 markets

One prompt, comparable by construction

One brief, four markets, one methodology. Not four separate outputs assembled by hand. Comparable because Saga runs them the same way.

Early warning

200 signals

The right 200 mentions, not 20,000

Saga doesn't surface everything. It escalates what crosses your threshold — the emerging cluster, the tone shift, the affinity group that isn't in any pre-built taxonomy.

The corpus underneath

Not a wrapper on the web

Fifteen years of permissioned, audience-grade data. The kind of depth that lets Saga surface clusters that don't exist in any pre-built taxonomy.

Every other tool in this category is a chat layer over whatever the vendor's index covers. Saga works directly on the raw corpus — which means it can do statistical work that no dashboard pre-defines.

Saga · Novel Clustering Analysis
Emerging affinity cluster — "slow beauty"
Not in taxonomy · +23% vol · 12k+ audiences
New
Audience overlap detected
High affinity with category Y · custom embedding
Signal
Tone shift — brand X, 3-market escalation
Crosses brief threshold · escalating now
Escalated
Cultural moment tagged — pre-taxonomy
Statistical anomaly · 15yr corpus cross-ref
Insight

Get early access

Give Saga a job.
Walk away.

Request access and we'll set up your first standing brief. No onboarding marathon — just a brief, a corpus, and an agent that works while you don't.

Brief once

Write it like you'd brief a senior analyst. Saga runs it indefinitely — no re-prompting, no re-opening.

Escalation on your terms

Set the threshold. Saga escalates when the data crosses it — not when you remember to check a dashboard.

Finished work delivered

Brand briefs, competitive scans, cultural reads — not summaries. In your inbox before you think to ask.