12
Formally modelled bias types
12
Tier-1 public data sources
100%
AWS-native, no data leaves
0
PII in durable storage
Solution Overview
See Public Sentiment Without the Blind Spots
The platform monitors public discourse across twelve tier-1 sources, surfaces sentiment and risk signals, and makes every bias visible, switchable, and auditable — so teams decide on data they understand.
Sentiment monitoring
Track public opinion across twelve tier-1 sources.
Bias transparency
See every bias assumption, never silently corrected.
Risk early-warning
Catch distribution shifts before they become recalls.
The Problem
Why Sentiment Data Misleads Decision-Makers
Public opinion data is biased by who posts, what platforms amplify, and how sentiment is measured. Most tools hide that — leaving teams confident in numbers they cannot trust.
Invisible Bias
Existing tools surface raw signals without making bias visible or quantifiable, so decisions rest on skewed data no one can actually see.
Unstable Trends
Topic models lose cluster identity between runs, so trend lines break and the same emerging issue looks brand new again each week.
Unverifiable Reports
Numbers rarely trace back to a source, so analysts cannot defend findings when leadership or regulators ask where a figure came from.
Compliance Risk
Sending public data to third-party AI vendors or storing personal data creates GDPR exposure that regulated manufacturers simply cannot accept.
The Adastra Offer
What the Platform Delivers
A 100% AWS-native, serverless platform that monitors public discourse, quantifies bias on every view, and generates cited, audit-ready reports your teams and regulators can trust.
Bias-Awareness Engine
Transparency · Auditable · Switchable
- Twelve formally modelled bias types on every view
- Self-selection, extremity, negativity, and bandwagon effects
- Coordination-pattern and demographic-skew detection
- Assumptions surfaced, never silently corrected
Risk Early-Warning
Distribution Shift · Backtested · Alerts
- Distribution-shift alerts with full statistics
- Backtested against historical recall events
- Skew measured against official baselines
- Slice by region, ownership, source, and time
Stable Topic Discovery
BERTopic · Lineage · Trends
- Emergent sub-topic discovery via BERTopic clustering
- Cross-run lineage layer keeps trend lines stable
- Segmentation by region, source, and demographic
- Proprietary Adastra IP, not off-the-shelf
Cited, Compliant Reports
Citations · GDPR · Audit-Ready
- Every sentence carries citation IDs
- Every number matches its source
- Audit-ready PDF output on demand
- PII gated at ingest, never stored
Expected Outcomes
Built to Earn Your Team’s Trust
The platform is engineered so teams act sooner, trust their numbers, and defend every finding. These targets reflect the locked design — validate them against your data in a pilot.
12
Bias types made visible on every view
Platform capability — Spec v0.4
12
Tier-1 public sources across DE and ES
Design scope
100%
Of inference runs inside your AWS account
By design — Amazon Bedrock only
0
Personal data written to durable storage
AWS Comprehend gate at ingest
How We Work
Phased Delivery Model
We deliver in short, outcome-focused phases — standing up reproducible infrastructure from week zero, then building, validating, and rolling out with your markets and governance in place.
Discovery & Data Assessment
We agree target markets, sources, and the bias types that matter, then map your data and success metrics. Infrastructure-as-code stands up dev, stage, and prod environments from week zero, so the build starts on solid, reproducible ground.
Platform Build
We stand up the serverless AWS pipeline — ingestion, PII gating, clustering, and the bias engine — on Bedrock, Glue, and Fargate. Every AI feature is versioned in an Iceberg lakehouse so results stay reproducible and auditable from day one.
Validation & Backtesting
We backtest risk alerts against historical recall events, tune sentiment and clustering accuracy, and verify every report traces to its source. You see real signals on live data until the outputs earn your analysts’ full trust.
Demo & Rollout
We package cited reports, hand over the dashboards, and roll out to your analysts with governance and monitoring in place. From there we expand sources, markets, and use cases as adoption grows across the business.
Why Adastra
Why Adastra Builds This Better
You could stitch together a generic listening tool or a research vendor, but Adastra brings a formal bias methodology, production-grade AWS engineering, and proprietary IP proven on hard problems.
Formal Bias Methodology
Twelve bias types with documented statistical modelling, not marketing labels. We make assumptions visible and switchable, so analysts trust the data instead of guessing at its blind spots.
Proprietary Topic IP
Our cross-run lineage layer keeps topic trend lines stable where off-the-shelf tools lose cluster identity, giving you dependable trends instead of noise that resets itself every single week.
GDPR-Native by Design
We gate PII at ingest and keep all inference inside your AWS account, so no data ever leaves and regulated manufacturers meet compliance without bolting on fixes later.
Production-Grade AWS
Full infrastructure as code, an immutable lakehouse, and audit trails from week zero mean the platform runs reliably in production, never as a fragile, one-off proof of concept.
Get It Now
See Bias-Aware Listening on Your Data
Talk to an Adastra AI specialist about your markets, sources, and risk priorities. We’ll show how the platform surfaces bias, forecasts risk, and produces cited reports your teams can defend.
- Schedule a 45-min discovery call
- Adastra reviews your sources and risks
- Launch a focused pilot in one market
- Receive an audit-ready cited report
FAQ
Questions Manufacturing Leaders Ask Us
Straight answers to the questions quality, product, and communications leaders raise when evaluating a bias-aware social listening partner — accuracy, compliance, speed, and how Adastra compares.
How does bias-aware social listening work?
Bias-aware social listening monitors public discourse across many sources, then measures and displays the bias in that data instead of hiding it. The platform surfaces twelve formally modelled bias types on every view, shows raw and robust-adjusted perspectives side by side, and lets analysts decide what to trust. Nothing is silently corrected, so every judgment stays transparent and defensible.
How is this different from Brandwatch or Sprinklr?
Unlike Brandwatch, Talkwalker, or Sprinklr, this platform makes bias visible and quantifiable rather than surfacing raw signals alone. It adds a formal twelve-type bias taxonomy, a proprietary cross-run lineage layer for stable trends, and citation IDs on every claim. All inference runs inside your AWS account on Amazon Bedrock, so no data leaves for a third-party AI vendor.
How do you keep our data GDPR-compliant?
GDPR compliance is built in, not bolted on. AWS Comprehend gates personal data at ingest, so PII never reaches durable storage, and only EU sources are in scope. All inference runs on Amazon Bedrock inside your own AWS account, meaning no data ever leaves for an external vendor. Every output also carries a model and prompt version for full auditability.
Can it warn us about product or recall risk early?
Yes. The risk early-warning engine raises distribution-shift alerts with full supporting statistics when public sentiment moves in ways that historically preceded problems. It is backtested against real recall events, and skew is measured against official baselines such as vehicle registration data. Analysts can slice signals by region, ownership, source, and time to confirm a genuine emerging risk quickly.
Which sources and markets does the platform cover?
The platform monitors twelve tier-1 public sources, including GDELT, Reddit, YouTube, German and Spanish news outlets, recall databases, Trustpilot, and official demographic data. Germany is the primary market with deep coverage, and Spain is the secondary market. Because the ingestion layer is modular, we can add sources and markets during delivery to match your regions and your specific risk priorities.
How long does it take to deploy?
A typical build runs eleven to twelve weeks from kickoff to a working demo. Because the design is already locked and infrastructure is defined as code, dev, stage, and prod environments stand up from week zero. A focused pilot in one market proves value early, then we scale sources, markets, and use cases at a pace that suits your team.
Do the reports stand up to audit and scrutiny?
Yes. Reports are audit-ready by design. Every sentence carries citation IDs, every number matches its underlying source, and each AI output records its model and prompt version. Feature rows are versioned in an immutable Iceberg lakehouse for full reproducibility. When leadership or regulators ask where a figure came from, analysts can trace it back to the exact source in seconds.
How do I get started with bias-aware social listening?
Start by talking to an Adastra AI specialist about your markets, sources, and risk priorities. We assess fit, agree the bias types and KPIs that matter, and propose a short pilot in one market to prove value. From there you scale to production at your own pace. Book a strategy session to map your path forward today.