MARKETING & ADVERTISING INTELLIGENCE, REDEFINED.
The Soundwave Intelligence Engine™ is a fully-automated, multi-source analytics stack that ingests, normalizes, and interprets signals from ads, socials, CRM, POS, ticketing, loyalty, and accounting. It doesn’t just show charts — it explains performance, flags what changed, and drafts the client-ready summary.
FROM DASHBOARDS TO DECISIONS
The future of analytics is not another login, dashboard, or CSV export. It’s a living intelligence layer that quietly watches the entire system, connects the dots, and talks to humans in plain language.
A NEW CATEGORY: AUTOMATED INSIGHT SYSTEMS
Traditional tools describe what happened. The Soundwave Intelligence Engine takes the next step: it notices the change, calculates the impact, compares it to history, and spells out what to do about it.
- No more screenshot collages and manual “what changed” write-ups.
- Every reporting cycle starts from a draft that already knows the story.
- Analysts move from copy-pasting charts to editing recommendations.
EVERY SIGNAL MATTERS
If it has an API, an export, or a semi-sane spreadsheet, it can be pulled into the Engine. Paid media, organic social, email, SMS, CRM, point-of-sale, loyalty, accounting, ticketing — they all become rows in one shared truth table.
- Ads & socials: platform performance, pacing, budgets, experiments.
- Revenue: ticketing, Shopify/commerce, Stripe, accounting systems.
- Audience: CRM, loyalty, email, SMS, CDPs, offline imports.
- Custom: casino host logs, show-level P&Ls, merch, anything with a schema.
THE INTELLIGENCE PIPELINE
Under the hood, the Engine is a modular pipeline — from ingestion to narrative — designed to be extended for any client, vertical, or weird internal data source.
LAYERS OF THE STACK
- Data Layer
- APIs, SFTP, exports, and webhooks are ingested, normalized, validated, and stitched into unified entities.
- Model Layer
- Metric expectations, anomaly bands, pacing curves, and attribution models live here.
- Orchestration
- Triggers, schedules, queues, and runbooks decide when to pull and compute.
- Automation
- Report builds, exports, alerts, and file routing (Drive, S3, email, Slack).
- Insight Layer
- The AI / LLM layer that reads the outputs, interprets them, and drafts narratives.
GUARDRAILS BY DESIGN
Every step is logged, versioned, and recoverable. Metrics are never hallucinated — the AI layer only interprets numbers the Engine has already computed.
- Strict schemas and data contracts for every source.
- Validation checks before anything hits client-facing reports.
- Run logs per brand, per report, per day for auditability.
ANALYSIS, EXPLANATION, AND ACTIONABLE INSIGHTS
The AI layer doesn’t replace analysts — it gives them a head start. It reads PDFs, compares metrics to expectations, and drafts the summary you’d normally burn a weekend writing.
PDF INTELLIGENCE PIPELINE
- Watch a Drive folder for new or updated report PDFs.
- Extract tables and key metrics; map them into known KPIs.
- Compare against trailing history, forecast curves, and risk thresholds.
- Flag anomalies, pacing issues, and opportunity pockets.
- Draft a client-ready write-up with plain-language explanations.
Every draft sticks to numbers the Engine has already computed — no hallucinated KPIs, no invented revenue lines.
HUMAN-IN-THE-LOOP BY DEFAULT
For each reporting cycle, the Engine emails the internal team with a link to the generated PDFs, a draft client summary, and a short list of flags worth digging into.
Analysts edit, reorder, and add nuance, then send the final version to the client. The edits flow back into the system so the next draft is closer to how humans actually talk.
A SELF-CORRECTING, OUTCOME-DRIVEN SYSTEM
The system is not static. Every report, every approval, every “this was actually wrong” comment becomes fuel to improve the next cycle.
HOW THE LOOP WORKS
- Editor feedback – Changes to tone, order, and emphasis are logged and used to tune prompt templates.
- Client behavior – Which insights get actioned or ignored feeds a signal into what the Engine surfaces first.
- Attribution back-pressure – Changes in revenue vs. forecast tune the anomaly bands and expectations.
- Source reliability – Data sources that routinely break or arrive late get special handling and more defensive logic.
WHERE IT LIVES IN THE REAL WORLD
The Intelligence Engine is not a theoretical deck. It runs on real schedules, with real clients, and real “oh shit” moments baked into the design.
SCHEDULING & TRIGGERS
- Nightly ingests keep data fresh for daily pacing checks.
- Weekly and month-end cycles for full attribution and revenue tie-outs.
- On-demand runs for “WTF just happened?” moments.
DELIVERY SURFACES
- Google Drive: PDF archives, client folders, and internal “to be checked” holding areas.
- Email: internal pre-flight digests and client-ready summaries.
- Slack / Teams: quick alerts for critical anomalies and pacing problems.