From 45-Minute Paper-Chase to 15-Minute Smart Claims

How ExtractQ turned a manual bottleneck into an AI-powered fast lane.
1. The Reality Check – Life Before ExtractQ
Every claim followed a five-hand relay:
- Intake – PDFs arrived by email; a coordinator downloaded, renamed, and filed them.
- Re-typing – A data-entry clerk keyed 30-40 fields into the claims system.
- Verification – Another employee cross-checked policy numbers and coverages in a separate portal.
- Chasing missing data – Gaps were kicked back to the clerk; adjusters waited.
- Audit & compliance – Screenshots and spreadsheets were stored for regulators.
Result? Slow resolutions, mounting backlogs, frustrated customers, and an audit trail nobody trusted.
2. The Game-Plan – What We Deployed
We introduced an AI-driven automation stack anchored by ExtractQ (for “zero-training” document intelligence) and a light-weight orchestration layer we call ProcessQ for validations and routing.
3. The New Groove – Life After ExtractQ
One straight-through, lights-out flow:
- Auto-capture – Claim docs drop into an S3 bucket or SharePoint folder; a serverless trigger picks them up.
- Smart extraction – ExtractQ reads the file, pulls exactly the fields we tell it to (no template training), and returns clean JSON.
- Instant validation – ProcessQ pings internal policy DBs plus third-party APIs (DMV, fraud-check, VIN lookup) to confirm accuracy.
- Seamless ingestion – Verified data lands in the core claims app via REST API; adjusters see a ready-to-adjudicate record.
- Built-in compliance – Every extraction, validation, and field-level change is time-stamped for auditors; dashboards refresh in real time.
4. Why It Worked
Traditional OCR | ExtractQ Advantage |
---|---|
Needs model training & upkeep | “Just specify the fields” – zero training |
Struggles with mixed layouts & handwriting | Vision-plus-LLM stack handles tables, photos, scribbles |
Text only | Links data points for richer insights |
Limited language support | 25+ languages out-of-the-box |
5. Business Impact
- Speed: Average processing time cut from 45 min to 19 min—customers get answers the same day.
- Scale: When a hailstorm spiked claims by 150%, the system kept pace without extra hires.
- Savings: Fewer manual touches translated to US $1.5 M annual OPEX reduction.
- Decisions: Claims managers now approve or reject in one screen, backed by real-time accuracy scores.
- Compliance Confidence: Auditors receive click-through evidence instead of CSV exports.
6. Takeaway
ExtractQ didn’t just digitize paperwork—it re-engineered the claims experience. By removing manual choke points and baking validation into the flow, the insurer turned a cost center into a customer-delight engine.
Ready to see your own “before and after” story?
Let’s run a pilot on five claim types and benchmark the results in under 30 days.
(Contact Scalong to schedule your discovery session.)
More Success Stories
From Product Photo to Scroll-Stopping Post in 90 Seconds — A Generative-AI Platform for Effortless Social Media Marketing
Scalong’s builds a generative AI platform to automate and bring social-media creatives to life—blending AI image generation, background removal and caption optimization for lightning-fast, multi-language product marketing automation.
From Chaos to Clarity: How AI Transformed Accounting Efficiency
A global manufacturing conglomerate faced challenges with manual invoice processing, leading to delays, errors, and security risks. By implementing AccountingQ, an AI-powered accounting automation solution, the company achieved significant improvements. The solution automated invoice processing, enhanced accuracy, reduced operational burdens, and improved financial visibility. This case study highlights the power of AI in transforming complex accounting processes and driving business efficiency.
View All Case Studies
Explore more success stories and digital transformation journeys