[ SYS.LOG ]
crease_02 · search · 105k/day crease_04 · reviews · 22k/day

Access to Continuous Consumer Intelligence.

Outllyr is the intelligence layer behind your biggest decisions. Our proprietary AI reads every conversation that shapes your market and finds the insights others miss: causal, traceable, and ready to act on.

signals / day
0+
across 6 source families
rejected at audit
0%
bots, dupes, low-trust
claims traceable
0%
every verdict links to evidence
verdict latency
0h
not a 12-week wave

Traditional research: a survey panel and a deck that's stale on arrival. Social listening: mention counts dressed up as insight. Outllyr: the whole conversation, every source, turned into a verdict you can act on today.

The intake.

any brief format

Presentations, documents, emails, chats, meeting transcripts, spreadsheets, or plain English.

clarifies missing context

Spots gaps in objectives, scope, stakeholders, constraints, and success criteria through intelligent follow-up.

decomposes the problem

Breaks a complex business challenge into research objectives, hypotheses, information needs, and reasoning paths.

recommends the investigation

Picks the right methodology, evidence sources, and analytical framework for the question.

generates an intelligence blueprint

Outputs a structured, decision-ready blueprint that drives every downstream step.

brief_session · 14:22:08 SYS.TERMINAL.01
me
Why is our Gen-Z share dropping? They seem to like the product but drop off mid-way.
ai
> identifying gaps Need timeframe context. Are we looking at Q3 drop or year-over-year?
me
Let's focus on Q3 specifically.
ai
> parsing intent Extracted core problem: Gen-Z demographic drop-off (Q3). Identifying necessary data streams: session replays, conversion funnels, demographic cohorts.
brief_readiness 0%
threshold: 75% ▲ above threshold
confirm_parameters →

Hypotheses, not guesses.

competing hypotheses

Multiple plausible explanations for the question, not one convenient answer.

surfaces assumptions

Makes the assumption behind each hypothesis explicit, so nothing hides.

defines the evidence test

Specifies exactly what would prove or kill each one before the search begins.

pairs contrarian views

Builds opposing hypotheses in pairs, so the winner earns it on evidence, not bias.

validates the set is MECE

Mutually exclusive and collectively exhaustive: full coverage, no overlap.

H_001 Active

Payment Friction

Lack of alternative payment methods (BNPL, Apple Pay) causes friction for younger cohorts.

conf 0.83 ▲ 0.04
H_002 Pending Data

Hidden Costs

Shipping costs revealed too late in the flow cause immediate abandonment.

conf 0.45
H_003 Discarded

Mobile UX

Form fields are not optimized for mobile input.

conf 0.14 ✕ invalidated
H_004 Active

Brand Trust

Lack of social proof signals during checkout reduces trust among new users.

conf 0.65 ▲ 0.05
H_005 Discarded

Price Sensitivity

Gen-Z cohort is highly price sensitive compared to Millennials.

conf 0.22 ✕ invalidated
H_006 Pending Data

Session Length

Checkout flow takes too long, causing attention drop-off.

conf 0.33

Listen to everything. Trust selectively.

discovers relevant sources

Finds the internal systems, market intelligence, and public sources that actually bear on the question.

collects multi-source evidence

Structured and unstructured data from documents, conversations, reports, news, reviews, and enterprise systems.

filters signal from noise

Drops duplicate, irrelevant, and low-confidence data so only meaningful evidence survives.

maps evidence to hypotheses

Links every signal to the hypothesis it supports, contradicts, or strengthens.

builds a knowledge graph

Organizes evidence into a connected, traceable graph that powers downstream reasoning.

the_open_web · scanning
social · reddit.com/r/*45k
social · x.com12k
social · tiktok8k
social · linkedin3k
reviews · trustpilot1.2k
reviews · amazon15k
reviews · g2400
reviews · appstore6k
forums · stackex2k
forums · discord18k
video · youtube22k
search · g_search105k
news · 240 pubs1.5k
community · substack3k
source_router
rule_01
hypothesis_match
if (vector_sim > 0.82) { route }
rule_02
cultural_relevance
cohort == 'GEN_Z' && intent == 'HIGH'
rule_03
trust_audit
drop_if(bot_probability > 0.15)
rule_04
recency_decay
weight *= exp(-λ * days_old)
tagged_signals · structured
payment_friction 12k
trust_decay 4.5k
mobile_ux_friction 800
price_sensitivity_gen_z 18k
brand_advocacy_decline 6.2k
session_abandonment 2.1k
checkout_complexity 9.8k
cultural_drift_signal 1.5k

A report that updates itself.

refreshes insights continuously

New evidence updates the verdict automatically; nothing waits for a rerun.

highlights what's changed

Surfaces every shift since you last looked, and why it moved.

maintains traceability

Every claim links back to the signals behind it. Nothing is unsourced.

supports collaboration

Your team works from the same live report, not five stale copies.

recommends next actions

Turns the current verdict into the moves worth making now.

live · q1 2026 · last verdict shift 14m ago brand_health · gen_z · north_america 0 signals
H_001 · 0.83
Payment Friction
▲ 0.79→0.83
H_004 · 0.65
Brand Trust
H_002 · 0.45
Hidden Costs
H_005 · 0.22
Price Sensitivity
H_003 · 0.14
Mobile UX
H_006 · 0.33
Session Length
GEN_Z
recent verdict shifts
14m ago
H_001 ▲ 0.79 → 0.83
2h 15m ago
H_004 ▲ 0.60 → 0.65
1d ago
H_003 invalidated · 0.14
incoming signals
reddit · r/pf · 'fees are insane'→ H_001
x.com · @user492 · 'cart bug'→ discard
tiktok · #finance · 'bnpl missing'→ H_001
trustpilot · 1★ · 'scam vibes'→ H_004
g2 · review · 'support slow'→ discard
reddit · r/genz · 'apple pay plz'→ H_001
amazon · review · 'shipping high'→ H_002
youtube · comment · 'overpriced'→ H_002
stability monitor
H_001 Robust
H_004 Pending
H_002 Fragile

Plug in. We do the rest.

Marketing, product, and insights teams plug into Outllyr's infrastructure layer to get continuous, causal, traceable intelligence that powers decisions.

Teams
Marketing
CMO · growth · brand
Product & innovation
roadmap · pricing
Consumer insights
research · strategy
Outllyr.
insights-as-infrastructure

causal ai engine
always-on signals
source-linked outputs
on-demand access
Decisions
Launch & positioning
hours, not weeks
Campaign decisions
defensible to leadership
Product & pricing
causal, not correlation
what you plug into
end-to-end framework
causal forces, not metrics
on-demand access
no project cycles
infrastructure layer
sits inside your workflow
integrations
slack· notion· claude_mcp· chatgpt· looker· tableau· google_sheets· webhooks· rest_api· graphql· slack· notion· claude_mcp· chatgpt· looker· tableau· google_sheets· webhooks· rest_api· graphql
onboard

Ready to explore the future of consumer intelligence?

Request a demo to see the platform map your specific market.

sys_onboard · secure