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Document Intelligence Case Study
Kenstin Technologies built a document intelligence UX: every extracted snippet carries precise page and coordinate metadata, so one click jumps the PDF viewer to the exact passage-highlighted in context for fast audit and review.
8
Weeks
100%
Completion
4
Tech Used
Why teams choose this build
Concrete scope signals from the engagement-structured for evaluation, not vanity metrics.
Precision target
Page + bbox accuracy
UX goal
Click-to-highlight parity
Scope
Extraction + viewer bridge
Project foundation
We start with scope clarity, challenge mapping, and execution guardrails before implementation begins.
Teams reviewing contracts, policies, or technical PDFs needed extraction for search and workflows-but also needed to verify AI or OCR output against the source. Kenstin delivered an experience where the list of extracted segments and the native PDF viewer stay in lockstep, turning “found text” into trustworthy visual proof.
Mapping extracted strings to PDF geometry is brittle: layout shifts, multi-column pages, hyphenation, and embedded fonts can break naive offsets. Users will not trust the product if highlights drift by even a line. The solution had to be accurate across pages and resilient to common PDF quirks.
Scope & timeline
Directional highlights for this anonymized portfolio entry-useful for understanding depth of work, sequencing, and ownership.
Delivery window
8 weeks
Precision target
Page + bbox accuracy
UX goal
Click-to-highlight parity
Scope
Extraction + viewer bridge
Engagement note
The team executed in tightly defined milestones with weekly validation loops, keeping scope, quality, and rollout confidence aligned throughout delivery.
Weeks 1–3
Extraction pipeline
Built parsing and text segmentation with coordinate capture across varied PDF layouts.
Weeks 4–5
Coordinate normalization
Aligned bounding boxes with viewer coordinates across zoom levels and page sizes.
Weeks 6–7
Viewer integration
Implemented navigation, scrolling, and highlight overlays wired to list selections.
Week 8
QA & edge cases
Hardened multi-column layouts, hyphenation, and font edge cases with visual regression checks.
Execution
Kenstin implemented a position-aware extraction pipeline that stores page indices and bounding boxes for each segment, normalized for the viewer’s coordinate space. List interactions dispatch navigation events with enough metadata to scroll, zoom, and render overlays without guesswork.
Accuracy benchmarks were incorporated early so coordinate drift could be detected before it impacted reviewer trust.
The implementation covers extraction, indexing for lookup, and click-through navigation. Selecting an item opens the correct page and draws a highlight over the matched region so reviewers can validate meaning and wording in seconds rather than hunting manually.
We also tuned viewer synchronization behavior across zoom and scroll states to keep highlights stable during repeated review actions.
Review throughput improved materially: less manual scanning, fewer mis-citations in downstream work, and higher confidence in extracted fields because users could always snap back to the authoritative PDF location. Teams reported faster validation cycles because discrepancies were easier to spot and resolve in context.
The stack is selected for reliability, maintainability, and production readiness.
Previous case study
Kenstin Technologies built a multi-bot lead platform where each assistant answers in real time, then captures contact details only after the conversation is complete-so quality and compliance stay aligned with the user journey.
Next case study
Kenstin Technologies refined a health AI pipeline: exercise plans respect physiology and profile context via retrieval-grounded generation, and meal plans honor dietary rules from first retrieval through final output-fixing the leaks that broke trust.
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