AI Search Pulse
Document AI / IDP Baseline Benchmark
Baseline Date: July 10, 2026
Methodology Version: 1.0
AI Assistant: ChatGPT
Model: GPT-5.5
Tracked Vendors: 10
Prompt Library: 49 Buyer Evaluation Scenarios
Executive Summary
This document presents the first baseline benchmark produced by AI Search Pulse for the Intelligent Document Processing (IDP) category.
Using the methodology described in the AI Search Pulse Research Methodology (Version 1.0), we submitted a standardized library of 49 buyer-representative prompts to ChatGPT and recorded which enterprise software vendors were recommended, how frequently they appeared, and where they appeared within each response.
This benchmark establishes the initial reference point for future monthly comparisons. Subsequent benchmark reports will evaluate changes in AI Visibility Score, Category Distribution, and Recommendation Order using the same methodology and prompt library.
Important: This benchmark measures AI recommendation visibility, not product quality, customer satisfaction, analyst rankings, or market share.
Key Findings
Before looking at the detailed metrics, four broad patterns emerged from the baseline benchmark.
1. Enterprise recommendations were highly concentrated.
Across enterprise platform-selection prompts, ChatGPT repeatedly recommended the same small group of vendors. Rather than producing a wide variety of shortlists, responses consistently favored four enterprise-focused platforms.
2. Visibility differed by buyer persona.
Several vendors appeared frequently when prompts reflected developer or API evaluation scenarios but appeared rarely—or not at all—in enterprise platform-selection prompts.
This suggests that AI visibility can vary significantly depending on the type of buyer asking the question.
3. Competitive prompts surfaced a broader vendor set.
Alternative-vendor and comparison prompts generated more diverse recommendations than enterprise buying prompts, introducing vendors that rarely appeared elsewhere in the benchmark.
4. Capability-focused prompts emphasized features over brands.
When ChatGPT was asked which capabilities buyers should evaluate in an IDP platform, responses focused primarily on functional criteria rather than recommending specific vendors.
AI Visibility Summary
| Vendor | Total Mentions | Enterprise | Use Case / Industry | API | Competitive | AI Visibility Score |
| ABBYY | 35 | 9 | 15 | 8 | 3 | 23.6% |
| UiPath | 35 | 9 | 15 | 8 | 3 | 23.6% |
| Hyperscience | 33 | 8 | 15 | 6 | 4 | 22.3% |
| Tungsten Automation | 32 | 8 | 15 | 5 | 4 | 21.6% |
| Rossum | 22 | 3 | 6 | 8 | 5 | 14.9% |
| Nanonets | 9 | 0 | 0 | 6 | 3 | 6.1% |
| Affinda | 5 | 0 | 0 | 5 | 0 | 3.4% |
| Klippa | 3 | 0 | 0 | 3 | 0 | 2.0% |
| Docsumo | 3 | 0 | 0 | 3 | 0 | 2.0% |
| Veryfi | 2 | 0 | 0 | 2 | 0 | 1.4% |
AI Visibility Score represents each vendor’s share of total tracked recommendations within the benchmark prompt library.
Benchmark Observations
A consistent enterprise shortlist emerged.
ABBYY, UiPath, Hyperscience, and Tungsten Automation appeared repeatedly across enterprise platform-selection prompts and remained closely grouped throughout the benchmark.
For these vendors, the primary challenge appears to be differentiation rather than visibility.
Rossum showed stronger visibility in technical evaluations.
Rossum appeared more frequently in developer/API and competitive comparison prompts than in enterprise platform-selection prompts.
This suggests that its visibility may currently be stronger among technical evaluation scenarios than executive buying scenarios.
Several vendors demonstrated limited enterprise visibility.
Nanonets, Affinda, Klippa, Docsumo, and Veryfi were recommended primarily within developer/API or competitive prompts and did not appear in enterprise platform-selection scenarios during this benchmark.
Additional benchmark runs will determine whether this represents a consistent visibility pattern or normal month-to-month variation.
Capability-focused questions rarely produced vendor recommendations.
One prompt asking buyers which capabilities they should evaluate in an IDP platform resulted in a feature-based response without vendor recommendations.
This suggests that informational evaluation prompts may behave differently from vendor-selection prompts.
Questions for the Next Benchmark
The July baseline establishes a starting point rather than a ranking.
Future benchmark reports will monitor questions such as:
- Does the enterprise shortlist remain stable?
- Do developer-focused vendors gain enterprise visibility?
- Does recommendation order change over time?
- Are changes associated with identifiable AI model updates?
Methodology & Limitations
This benchmark measures AI recommendation visibility within a standardized set of buyer evaluation scenarios. It does not evaluate product quality, analyst rankings, customer satisfaction, or market share.
All benchmark data is collected using the AI Search Pulse Research Methodology Version 1.0. Future reports will continue using the same methodology to support meaningful month-over-month comparisons. Any methodological changes will be documented through public version history.
AI Search Pulse Roadmap
AI Search Pulse is an ongoing research program. Each publication builds on the previous benchmark to create a longitudinal view of AI Search Visibility.
| Status | Milestone |
| ✅ Published | Research Methodology v1.0 |
| ✅ Published | Document AI / IDP Baseline Benchmark |
| 🔄 Scheduled | August 2026 Monthly Benchmark |
| 📅 Planned | September 2026 Monthly Benchmark |
| 📅 Planned | ChatGPT + Gemini Comparative Benchmark |
| 📅 Planned | Additional Enterprise Software Categories |
AI Search Pulse is designed as a continuous research program rather than a one-time study. Each benchmark uses the same methodology and standardized prompt library to enable meaningful comparison over time. Future reports will expand historical trend analysis as additional benchmark periods become available.
