Introduction
In industries where visual inspections are core to daily operations—such as insurance and manufacturing—accuracy, speed, and scalability often clash with human limitations. Claims assessors, quality assurance teams, and production-line supervisors routinely spend hours poring over images or physical components, making judgment calls based on experience and attention to detail. The process, though essential, is repetitive, time-consuming, and prone to variability.
To address this, we built an image-based assessment agent: an intelligent system capable of automating visual evaluations using deep learning, computer vision, and cloud-native services. Designed with flexibility in mind, the agent can be applied across a range of use-cases, from identifying vehicle damage in insurance claims to detecting production anomalies in electronic components.
What sets this agent apart is its accessibility to internal users. Rather than replacing human expertise, it augments it, helping claims settlers, QA teams, and other operations professionals process visual data faster, more consistently, and at scale. This is especially valuable in contexts where thousands of assessments must be performed each week and where visual cues determine critical downstream decisions.
Built on AWS technologies, this solution is a blend of automation, machine intelligence, and practical UX, shaped for real-world adoption. In this article, we’ll walk through how the agent works, explore the user journey, highlight the technology stack behind it, and share key lessons learned—including a case study from the automotive sector.
How the Agent Works
The image-based assessment agent is built to be modular, scalable, and simple to integrate into existing business processes. Its core functionality is driven by LLMs accessed through Amazon Bedrock, with orchestration and preprocessing handled entirely in Python.
This minimal yet powerful architecture allows the agent to perform complex visual assessments and generate structured reports with little infrastructure overhead, while taking full advantage of the elasticity and service abstraction that AWS provides.
1. Instruction Setup and Configuration
Each use-case begins with a simple configuration process where the agent is provided with
- a prompt template describing the assessment task (e.g., what to look for and how to evaluate);
- the output schema or report format; and
- any domain-specific metrics it should consider during judgment.
2. Image Intake and Visual Analysis (via Amazon Bedrock)
When a new assessment is initiated, a batch of images is uploaded to the system and processed by a visual LLM hosted on Amazon Bedrock. This model is responsible for:
- “viewing” the images;
- extracting relevant visual insights; and
- translating raw pixels into intermediate semantic understanding (e.g., “scratched bumper,” “loose connection,” “burn mark on PCB”).
The visual LLM is used in a model-agnostic way—meaning you can switch between supported Bedrock models like Claude, Titan, or Stability AI (depending on your need for multimodal capabilities).
3. Report-Composition with a Text-to-Text LLM
Once the visual analysis is complete, the findings are passed to a text-to-text LLM, also accessed via Amazon Bedrock. This second model is responsible for:
- synthesizing the image-based insights;
- formatting the response according to the pre-defined structure; and
- generating a human-readable report.
This report could be as simple as a bullet list of observations or as detailed as a multi-section professional-grade assessment with valuation, severity ratings, and part-by-part breakdowns.
4. Orchestration and Infrastructure
All logic outside the LLMs—file handling, prompt construction, status tracking, and user interaction—is implemented in Python. AWS services such as Amazon S3 (for storing input/output assets) can be used to build a fully serverless backend if desired.
By leveraging Amazon Bedrock, the entire system remains model-flexible, cost-controllable, and scalable without ops-heavy management—a major win for fast-moving solution teams.
The User Journey
To ensure the capabilities of the image-based assessment agent are matched by a frictionless user experience, we designed the end-user workflow to be intuitive, fast, and optimized for field use. Whether the user is a claims settlor, a QA inspector, or a technician in a manufacturing facility, the interaction model remains consistent and easy to adopt.
1. Accessing the System
Users interact with the agent through a companion mobile or web application that serves as the front-end for all assessments. Upon login, they are greeted by a dashboard that displays a chronological list of prior assessments, including their statuses and outcomes. This provides continuity—allowing users to reference historical reports, resume incomplete assessments, or verify previously identified issues.
2. Initiating a New Assessment
From the dashboard, users can start a new assessment by entering relevant metadata about the subject under inspection. This could include product identifiers, usage data, location information, or any context-specific parameters that help the AI make more informed judgments.
3. Uploading Visual Evidence
Next, the user is prompted to upload a set of images, captured via mobile camera or uploaded from storage. Multiple angles and zone-specific shots are encouraged to give the visual model the best possible input.
4. Real-Time Status Updates
Once submitted, the assessment enters a processing queue and is automatically tagged with a dynamic status: Pending, Estimating, and finally Completed. This feedback loop helps users stay informed without needing to monitor the process manually.
5. Receiving the Assessment Report
When processing is complete, the final report becomes available for viewing. It includes:
- the originally submitted metadata for traceability;
- a structured breakdown of findings based on visual analysis;
- quantitative or qualitative scores for each identified issue;
- a summary designed for quick consumption and decision-making.
6. Post-Assessment Actions
The report can be reviewed, exported, or shared with other stakeholders depending on the workflow. In some cases, users may also have the option to contest or refine certain findings, especially if the system is integrated into a larger claims or quality control pipeline.
By translating a complex, judgment-heavy task into a simple, guided interaction, the user journey empowers professionals to make better decisions, faster, without sacrificing accuracy or oversight.
Case Study: Automating Vehicle Damage Assessments for Car Insurance
In the world of automotive insurance, resale, and fleet management, assessing the cost of vehicle damage is often a bottleneck. Traditional processes depend on human inspectors, rigid cost tables, and time-consuming interactions, sometimes involving travel to remote locations, centralized inspection depots, or live video calls. This manual approach introduces subjectivity, delays, and limited scalability.
To address these challenges, we worked with an insurance client to develop a specialized image-based assessment agent tailored specifically for vehicle damage evaluation. The goal was to review car images, identify and classify visible damage, and generate a structured, real-time cost estimate report, all without requiring a human inspector on site.
Mobile-First Deployment: A Workflow Designed for the Field
To make the experience practical for field inspectors and business users, the agent was deployed through a custom-built Android application. Designed with usability in mind, the app served as the primary interface for submitting images, tracking assessments, and retrieving AI-generated reports.
Upon launching the app, users were presented with a dashboard listing previously submitted assessments. From here, they could initiate a new report, entering key vehicle metadata such as mileage, age, and original purchase price. Next, users were prompted to upload images of the vehicle—ideally from multiple angles—to give the AI sufficient visual input.
Once submitted, the app tracked the request in real time, displaying dynamic status labels such as Pending, Estimating, and Succeeded. The final report, once ready, included:
- a restatement of the vehicle metadata;
- a detailed, part-by-part breakdown of detected damage;
- severity classifications for each issue;
- a revised valuation of the vehicle; and
- a summary section formatted for quick review or export.
Under the Hood: A Two-Stage Intelligence Pipeline
The specialized vehicle agent followed the same two-step structure as the generic image assessment agent, but with additional domain-specific logic and report formatting.
Step 1: Visual Analysis via Amazon Bedrock
At the heart of the system was a visual LLM retrieved from Amazon Bedrock, which served as the perception layer. For each uploaded image, the model identified affected vehicle components and assigned a severity label using a three-point scale: Broken—Repair Required; Minor Repair Required; and Negligible.
The result was a structured, machine-readable list of damage events, each linked to a specific part and severity rating.
Step 2: Structured Report Generation
This intermediate output, along with the user-submitted metadata, was passed to a text-to-text LLM, also hosted on Amazon Bedrock. This model was guided by a strict template and tasked with composing a human-readable report. Instructions embedded in the prompt controlled:
- report structure and tone;
- the way damage was grouped and explained; and
- valuation adjustments based on regional cost data and prior pricing benchmarks.
The outcome was a document that balanced technical accuracy with stakeholder readability, ready to be shared with customers, repair partners, or internal claims teams.
The Customization Challenge: Fine-Tuning for Precision
While the base models provided by Amazon Bedrock offered a strong starting point, the client requested additional specialization to improve accuracy and alignment with their internal standards.
To meet this need, we conducted a fine-tuning process for the visual model:
- a labeled dataset of car images and expert assessments was curated and annotated;
- a high-compute AWS EC2 instance was used to run the training process; and
- usage of infrastructure was optimized using DeepSpeed and quantization techniques to make the training viable within resource constraints.
This fine-tuned version demonstrated improved part recognition and severity calibration, especially in edge cases like low-angle shots, poor lighting, or older vehicles.
Real-World Impact
The agent significantly reduced the time and effort involved in generating vehicle assessment reports. Inspections that once took hours or days could now be completed in under five minutes, end-to-end, from image capture to final report.
It also enabled remote inspections to be performed reliably, reducing the need for travel or synchronous video calls. Most importantly, the output became standardized, explainable, and traceable, helping the client align their valuation methodology across regions and agents.
Conclusion
The development of our image-based assessment agent illustrates how the strategic application of large language models—paired with cloud-native tools and thoughtful user experience design—can transform outdated, manual workflows into fast, scalable, and consistent processes.
Built on Amazon Bedrock and orchestrated entirely in Python, the agent delivers modularity and performance without operational overhead. Its architecture supports a wide range of use cases across industries like insurance, manufacturing, and logistics—anywhere visual inspection and structured reporting are core to decision-making.
Our work with a leading automotive insurer showed what’s possible when the agent is tailored to a specific domain. From optimizing the image analysis pipeline to fine-tuning models for better alignment with business expectations, we demonstrated that the path from general capability to industry specialization is not just achievable, but repeatable.
As foundation models and multimodal intelligence continue to evolve, so will the opportunities to unlock automation in visual-first processes. For organizations looking to modernize how they assess, document, and decide, this agent is both a practical starting point and a flexible framework, especially when supported by a team experienced in adapting AI to real-world complexity.