<p>Our client is a leading global provider of technology-enabled risk, benefits, and integrated business solutions. They specialize in claims management, including workers' compensation, disability, and absence management, offering services that improve productivity and reduce costs for businesses.</p><p></p><h3><strong>Reduction in processing time: <span style="color: rgb(112, 0, 224)">40%</span></strong></h3><h3><strong>Document processing: <span style="color: rgb(112, 0, 224)">85% faster</span></strong></h3><h3><strong>Manual work reduced: <span style="color: rgb(112, 0, 224)">+9,500 hours</span></strong></h3><p style="text-align: start"></p><h3><strong>Client Situation (Problem/Opportunity):</strong></h3><p>Our client needed to enhance their claims management and compliance processes to handle high volumes efficiently and reduce the risk of errors and delays.</p><p></p><h3><strong>Our Solution:</strong></h3><ul><li><p><strong>Claims Management</strong>: Automated the creation of claim denial shells and streamlined the process for handling medical bills and lawsuit claim notes​​.</p></li><li><p><strong>Compliance and Data Management</strong>: Provided solutions for compliance monitoring and data operations, ensuring accurate and timely processing of compliance documents and data points.</p></li></ul><p></p><h3><strong>Results Achieved:</strong></h3><p>Claims Management:</p><ul><li><p><strong>Efficiency Gains</strong>: Reduced the time to process claim denial shells by 40%, improving turnaround times and increasing customer satisfaction.</p></li><li><p><strong>Volume Handling</strong>: Processed multiple batches of up to 1,500 claim denials and unstructured data files at a time, managing claim denials, flagging reports for errors, and handling inbound automated and human-in-the-loop email triage processes.</p></li></ul><p></p><p>Compliance Workflows:</p><ul><li><p><strong>Document Processing</strong>: Improved speed of compliance document processing average of 85% to going from processing for documents from 60+ minutes to less than 7 minutes.</p></li><li><p><strong>Labor Savings</strong>: Saved staff over 9,500 hours of manual work within the initial months of the program, helping to prevent millions in potential penalties.</p></li></ul><p></p><p>Data Operations:</p><ul><li><p><strong>Accuracy</strong>: Achieved 99.5% accuracy in data operations, completing 15,000 orders in 90 days, ensuring high-quality data management and operational efficiency.</p></li></ul>

Processing documents 85% faster and reducing processing time with a 99.5% accuracy rate.

Reducing 9,500 Hours of Manual Work for Integrated Business Solutions Company

<h1><strong>Getting High Quality Data</strong></h1><p>In 2020, Boosted.ai expanded its AI-powered financial analysis platform, Boosted Insights, by developing an AI portfolio assistant for asset managers using a large language model (LLM). The assistant processes data from 150,000 non-traditional sources to deliver macro insights and trend analysis on over 60,000 stocks across global equity markets overnight.</p><p></p><h3><strong>Actionable Insights &amp; Data Updates: <span style="color: rgb(112, 0, 224)">Real-Time</span></strong></h3><h3><strong>Cost Savings: <span style="color: rgb(112, 0, 224)">90%</span></strong></h3><p></p><h2><strong>Problem: Drawbacks of LLMs &amp; Getting High Quality Data</strong></h2><p>While Boosted.ai's customers were responding well to the initial product, they wanted more precise insights, broader industry coverage, and faster speed. To meet these demands, Boosted.ai would need to process up to 10 times more data while reducing processing time by 99.99%.</p><p></p><p>Between 2023 and 2024, Microsoft, Google, Meta, and Salesforce began building AI products with small models trained on specific concepts and processes for defined use cases. These Small Language Models (SLMs) are less than 0.003% the size of typical LLMs and drastically reduce the time, energy, and costs of developing AI products. Boosted.ai realized that powering their AI investment assistant with an SLM could help them achieve their goals and reached out to AWS for support.</p><p></p><p>With AWS support, Boosted.ai saw promising results fine-tuning an open-source SLM. However, without more advanced ground truth training data, the SLM couldn’t meet their standards. They attempted other solutions with limited success, and AWS connected Boosted.ai with Invisible for a solution.</p><p></p><h2><strong>Solution: Developing Data for High-Performance SLMs</strong></h2><p>Invisible’s team collaborated with Boosted.ai to produce precise data for the SLM to learn and apply financial expertise accurately. This extended beyond data labeling to include data guiding the model on when to apply financial concepts and execute multi-step reasoning.</p><p></p><p>Invisible's Trainer teams checked for common-sense errors and applied expert-level QA. The work was complex, with tasks taking up to two hours each.</p><p></p><h4><em>"Simply not possible to do without human oversight." - Jonathan Dorando, Co-Founder &amp; CTO, Boosted.ai</em></h4><p></p><h2><strong>Impact: Empowering Boosted.ai to Focus on Innovation</strong></h2><p>By the third data batch, Boosted.ai felt “unlocked.” Their product and tech teams could stop focusing on data quality and start running experiments to optimize customer value and ROI. A key benefit of working with Invisible, Boosted noted, was its meticulous metadata and documentation, which led to new insights.</p>

Improving prediction accuracy for an AI Investment Assistant to cut costs by 90%.

Launching a Better, Faster, AI Investment Assistant for Boosted.AI

<h2><strong>Helping DoorDash Capture a Surge in Demand</strong></h2><p></p><p>When demand for food delivery doubled during the pandemic, Invisible enabled DoorDash to match supply with demand and capture market share.</p><p></p><h3><strong>Onboarding speed: <span style="color: rgb(112, 0, 224)">+233%</span></strong></h3><h3><strong>Datapoints structured monthly: <span style="color: rgb(112, 0, 224)">1.5 million</span></strong></h3><h3><strong>Onboarding cost reduction: <span style="color: rgb(112, 0, 224)">50%</span></strong></h3><p></p><h3><strong>Problem: A Surge in Demand Complicated Onboarding</strong></h3><p>On-demand delivery giant DoorDash was hit with a supply shock in March of 2020 as restaurant dining shut down due to the pandemic. Thousands of restaurants scrambled to be onboarded onto DoorDash, creating a backlog of menu digitization that their team simply couldn’t keep up with.</p><h3><br><strong>The Solution: A Solution Configured Just for DoorDash</strong></h3><p>Invisible turned a drawn-out manual process into a bespoke 7-step solution to turn DoorDash’s restaurant onboarding process into a scalable, machine-augmented system.</p><h3><br><strong>The Impact: Accelerating Onboarding by 233%</strong></h3><p>Invisible set benchmark standards in the first 10 days, and within 30 had built a team of 200 and a custom OCR tool to automate the extraction of information from menu photos. Within 90 days, our service had become so fundamental that we fully integrated with DoorDash’s internal tech systems. Now, streamlined Invisible processes structure 1.5M unique data points monthly, and have reduced onboarding costs by 50%.</p>

Accelerating onboarding speed for Doordash by over 200% to meet demand during the pandemic. 

Boosting Onboarding Speed for Doordash

<h2><strong>End-to-End Pipeline Support</strong></h2><p></p><p>One of the fastest-growing companies in the US propels solar adoption by unifying vendors, lenders, and homeowners for cost-saving proposals and installation. Invisible scaled the company's end-to-end turnkey customer support experience to keep up with continued growth.</p><p></p><h3><strong>Turnaround time: <span style="color: rgb(112, 0, 224)">25 minutes</span></strong></h3><h3><strong>Contracts daily: <span style="color: rgb(112, 0, 224)">180+</span></strong></h3><h3><strong>States: <span style="color: rgb(112, 0, 224)">30+</span></strong></h3><p></p><h3><strong>Problem: Scaling Complex Pipeline Processing</strong></h3><p>The company leverages a turnkey customer experience both online and via the hundreds of sales reps it partners with to rapidly convert warm leads into happy customers.</p><p></p><p>As demand continued to expand, it couldn’t scale its team of administrators in California responsible for generating cost savings proposals for customers and lenders in under an hour. The proposals had to be crystal clear in order to confirm customer savings and secure lender financing and depended on variables including regional tax implications and the placement of actual solar panels on clients’ homes.</p><p></p><h3><strong>Solution: Rapid, End-to-End Support</strong></h3><p>Invisible worked with the company to develop highly detailed SOPs regarding proposal creation for homeowners interested in installing solar panels on their properties.</p><p></p><p>Steps included utilizing its proprietary platform and satellite data to predict the amount of sunlight a roof would receive and comparing customer utility bills against potential savings by factoring for energy generated, installation costs, and any local or federal tax savings.</p><p></p><p>Invisible’s team was so effective that the team wound up requesting downstream support to work with lenders on financing contracts for solar installations and monitoring energy usage thereafter to validate projected cost savings.</p><p></p><h3><strong>The Impact: Frictionless Contract Closes</strong></h3><p>Invisible’s coordinated team of agents shattered the company's turnaround target at a fraction of previous costs. Proposals were delivered, on average, 25 minutes from request.</p><p></p><p>Continuing to help it move prospects through its pipeline, Invisible moved on to generate financing contracts in under 24 hours. At peak, we were generating 180 contracts per day for new installations.</p><p></p><p>As more contracts closed, we even proceeded to support the company's ongoing benchmarking and validation efforts by setting up energy monitoring of properties post-installation.</p>

Cutting turnaround times to maintain growth for a fast-growing Solar adoption firm. 

Supporting End-to-End Customer Experiences for a Solar Solutions Provider

<p>Our client provides a range of insurance products, including auto, home, life, and commercial coverage, catering to individuals, families, and businesses across the United States. Our client focuses on delivering personalized service and innovative solutions to protect and support their clients' financial security and peace of mind.</p><p></p><h3><strong>Cost savings: <span style="color: rgb(112, 0, 224)">$450k</span></strong></h3><h3><strong>Reduction in claim denial response times: <span style="color: rgb(112, 0, 224)">50%</span></strong></h3><h3><strong>Increase in W9 accuracy: <span style="color: rgb(112, 0, 224)">30%</span></strong></h3><h3><strong>Hours in labor savings: <span style="color: rgb(112, 0, 224)">16,000 hours</span></strong></h3><p></p><h3><strong>Client Situation (Problem/Opportunity):</strong></h3><p>Our client sought to automate and improve the efficiency of various processes, such as invoice reconciliation, W9 processing, claim denial, and compliance workflows. They aimed to reduce manual errors, speed up processing times, and enhance overall operational efficiency.</p><p></p><h3><strong>Our Solution:</strong></h3><ul><li><p><strong>Invoice Reconciliation</strong>: Invisible Technologies partnered with our client's automation team to improve the invoice reconciliation process by increasing straight-through processing rates and ensuring accurate coding of invoices​​​​.</p></li><li><p><strong>W9 Processing</strong>: Streamlined the process by accessing AFICS email, updating Contact Manager, and notifying adjusters about updated or new contacts​​.</p></li><li><p><strong>Claim Denial Letters</strong>: Automated the process of sending partial rental denial letters to customers​​.</p></li><li><p><strong>Compliance Support</strong>: Converted files into PDFs, combined them, and sent back to AFICS as part of a recurring compliance process​​.</p></li></ul><p></p><h3><strong>Results Achieved:</strong></h3><p>Increased processing speed and accuracy in invoice reconciliation, achieving higher straight-through processing rates and improved team efficiency.</p><ul><li><p><strong>Efficiency Gains</strong>: Increased the straight-through processing rate from 26% to a significantly higher rate, improving team efficiency and reducing errors. Invisible Technologies helped save internal time and costs of approximately $130,000 annually for only 1 of 10 total processes for clients associated with invoice processing.</p></li></ul><p></p><p>Enhanced operational efficiency in W9 processing and claim denial processes, leading to faster response times and reduced manual workload.</p><ul><li><p><strong>Operational Efficiency</strong>: Processed 6,000 W9 requests annually and improved accuracy rates from 75% to 98%. Claim denial processes saw a 50% reduction in response times, significantly easing the manual workload.</p></li></ul><p></p><p>Significant time and cost savings through automation and streamlined compliance processes..</p><ul><li><p><strong>Document Processing</strong>: Improved compliance document processing by 83%, going from processing 40 documents per week to 350 per week.</p></li><li><p><strong>Labor Savings</strong>: Saved managers over 16,000 hours of manual work, realizing cost savings of over $320,000 and helping avoid the risk of millions in potential penalties.</p></li></ul><p></p>

Our client saved $450k in costs and reduced claim denial response times by 50%.

Slashing Costs with Automation for National Insurance Company

<h2><strong>Model Refinement</strong></h2><p>One of the world's leading AI platforms with enterprise-level LLM and RAG products, increasing productivity and improving customer and employee experience for their clients. Invisible refined training data and provided ideal responses, enabling them to enhance model performance and accuracy efficiently.</p><p></p><h3><strong>Rating/ranking tasks performed: <span style="color: rgb(112, 0, 224)">1,100</span></strong></h3><p></p><h3><strong>Problem: Unrefined Training Data</strong></h3><p>The client was facing challenges in evaluating and improving model responses, as existing data lacked thorough annotation and comparison.</p><p></p><h3><strong>Solution: Expert Human Review and Ideal Responses</strong></h3><p>Invisible's team stepped in to review partially completed conversations between different models. We analyzed the search results, reference material, and model responses.</p><p></p><p>Our experts evaluated sufficiency, usefulness, and quality of the responses, ranked them, and created an ideal response by editing the preferred one. Where conversations were incomplete, we extended them and repeated the process.</p><p></p><h3><strong>The Impact: Improved Accuracy and Quality</strong></h3><p>The client received refined, high-quality annotated data with ideal responses, enabling them to enhance model performance and accuracy efficiently.</p>

Refining annotated data with ideal responses, enhancing model responses.

Ranking and Optimizing RAG AI Models for Leading Enterprise AI Platform

<h2><strong>Accelerating Go-to-Market for a World-Class AI Model</strong></h2><p></p><p>Invisible helped a leading AI developer scale model capabilities in record time by recruiting and mobilizing AI trainers, overcoming their previous challenges due to limited staffing resources and infrastructure.</p><p></p><h3><strong>Data tasks completed daily: <span style="color: rgb(112, 0, 224)">5,000+</span></strong></h3><h3><strong>Accuracy above benchmark: <span style="color: rgb(112, 0, 224)">+10%</span></strong></h3><h3><strong>Go-to-market vs. expectations: <span style="color: rgb(112, 0, 224)">-6 months</span></strong></h3><p style="column-count: 3"></p><h3><strong>Problem: Scaling a Manual Process</strong></h3><p>A major AI development firm needed to create an AI chatbot to reliably answer user queries where previous contractors had failed to meet quality and volume targets.</p><p></p><p>Client researchers were overwhelmed in manual processes, spending valuable time on trying to scale adversarial natural language inference (NLI) and reinforcement learning from human feedback (RLHF) processes which were designed to reduce the risk of model hallucinations and improve quality.</p><p></p><p>This manual work was taking time away from strategizing and upgrading their model with technical expertise. The need for additional operators was mission-critical.</p><p></p><h3><strong>Solution: Strategic Process Partnership</strong></h3><p>To support this effort, Invisible rapidly hired, trained, and scaled coordinated teams of experts.</p><p>Invisible deployed two solutions to do so:</p><ol><li><p>A bespoke org structure tailored to coordinate people around the world in a dynamic, efficient manner.</p></li><li><p>Comprehensive onboarding materials and training systems to deploy expert AI trainers at scale.</p></li></ol><p>Operators were cross-trained for adaptability—teams were transferred to new processes within hours of reconciliation requests. Researchers had entire units of manual trainers to toggle at will.</p><p></p><h3><strong>Impact: Scale in Every Direction</strong></h3><p>Within 24 hours of being approached by our client, Invisible’s task force was processing over 500 data points. This scaled to 487 operators completing over 5,000 tasks per day in under two months.</p><p></p><p>Accuracy ratings of nearly 95% (10% above the benchmarking standard) enabled a public launch six months sooner.</p>

Helping a leading AI developer scale model capabilities and accelerate go-to-market by half a year.

Launching a Leading LLM 6 Months Faster

<h2><strong>Turning 50,000 Dormant SKUs into a 9x ROI for a Big 4 Retailer</strong></h2><p></p><p>Matching search intent with relevant products is critically important for retailers today. In order for products to be discoverable, however, their online data including descriptions, categories, and more, need to be cleanly structured and relevant to buyer intent. A Big 4 retailer with millions of third-party listings partnered with Invisible to close the gap on other eCommerce giants by optimizing their SKU data for optimal relevance.</p><p></p><h3><strong>Number of SKUs: <span style="color: rgb(112, 0, 224)">50,000+</span></strong></h3><h3><strong>Increase in conversions: <span style="color: rgb(112, 0, 224)">49%</span></strong></h3><h3><strong>ROI: <span style="color: rgb(112, 0, 224)">9x</span></strong></h3><p style="column-count: 3"></p><h3><strong>Problem: Limitation of Machines</strong></h3><p>At the crux of the retailer's challenge was machine limitation. The process of enriching its data requires both human judgment and tasks beyond the scope of full automation. Yet, the team did not have the resources to implement the manual workflow internally.</p><p></p><p>The solution required a well-coordinated process operated by a scalable workforce of human operators. To test the hypothesis, Invisible was brought on as a strategic partner to verify, enrich, and update tens of thousands of SKUs and measure performance against listings with low SKU fidelity.</p><p></p><h3><strong>Solution: Intelligent Operations</strong></h3><p>In just 30 days, Invisible hired and trained a team of 70 agents to complete the 6-step process of product data enrichment in 16 days.</p><p></p><p>Invisible applied generative AI to perform the heavy lift data enrichment steps at an unparalleled scale and also to flag data containing errors. Our platform wove in these AI-driven steps with tasks performed by skilled operators that required a finer touch.</p><p></p><h3><strong>Result: 9x ROI</strong></h3><p>Our client saw near-immediate ROl. In 30 days, 3,100+ dead-stock items generated nearly $1M in revenue. The next 10,000 products outperformed in their category by 139.54%, netting $21K in daily sales. The 44% of SKUs entered page one for search results, increasing visibility and sales velocity.</p>

Generating 9x ROI for a Big 4 Retailer with scaled product data enrichment powered by interoperable AI.

Enriching 50,000 Dormant SKUs for 9x ROI for a Big 4 Retailer

<h2><strong>Optimizing Payment Dispute Resolution</strong></h2><p></p><p>In the competitive renters insurance market, an innovative provider was founded to empower renters with reliable and accessible insurance options. While demand grew, optimizing back-office operations to scale effectively without losing quality became paramount, especially in a payment dispute resolution process that had become increasingly complex and costly due to a rising caseload.&nbsp;</p><p></p><p>The company partnered with Invisible to engineer and execute a standardized process for resolving payment disputes at scale.&nbsp;</p><p></p><h3><strong>Custom Automations: <span style="color: rgb(112, 0, 224)">2</span></strong></h3><h3><strong>ROI: <span style="color: rgb(112, 0, 224)">10x</span></strong></h3><p></p><h3><strong>Problem: Manual Processes Created a Bottleneck</strong></h3><p>As the company grew, it was essential to handle more claims efficiently while maintaining a high win rate. The existing manual processes for payment dispute resolution weren’t sustainable, with internal resources unable to pivot between rising demand and disputes simultaneously.&nbsp; The team was looking for a scalable, cost-effective solution without sacrificing the quality of service.</p><h3><br><strong>Solution: Process Expertise and Custom Automation</strong></h3><p>Invisible experts redesigned the payment dispute resolution process and engineered two custom automations. The first automation refined the claims resolution process through rigorous end-to-end address validation to ensure accuracy at every step.&nbsp;</p><p></p><p>The second enabled the efficient updating of user records and aimed to significantly reduce the frequency of payment disputes. When required, Invisible’s elite global team submitted evidence on the company’s behalf that payments were valid, including documents and communications between the company and its customers.&nbsp;</p><p></p><h3><strong>Result: 10x ROI</strong></h3><p>The company’s frequency of reclaimed disputes skyrocketed and it achieved a 10x ROI in won disputes in only one year. The strategic move successfully decoupled the scaling of operations in the back office from increased costs. Realizing Invisible’s value across functions, the company turned to Invisible to support processes across 5 different departments.</p>

Deploying an expert payment dispute resolution process that 10x'd ROI in just one year. 

Delivering a 10x ROI for a Renters Insurance Provider

<h2><strong>Optimizing Processing Speed for a Major Healthcare Company</strong></h2><p></p><p>Headway, a health-tech company that helps patients find mental healthcare providers, was losing efficiency in a key step of their claims processing workflow. Invisible optimized the workflow with an Intelligent Operations solution combining technology and human expertise.</p><p></p><h3><strong>Cost vs. internal team: <span style="color: rgb(112, 0, 224)">-37%</span></strong></h3><h3><strong>Cost vs. BPO: <span style="color: rgb(112, 0, 224)">-57%</span></strong></h3><h3><strong>Increased claim processing speed: <span style="color: rgb(112, 0, 224)">8x</span></strong></h3><p style="column-count: 3"></p><h3><strong>Problem: Insurance Validation Was Too Slow</strong></h3><p>Headway seamlessly matches patients to mental health providers and were required to validate coverage of new patients' insurance. As Headway scaled, internal teams were spending too much of their time validating insurance, causing poor patient experience and long reimbursement cycles.</p><p></p><h3><strong>Solution: Process Optimization Through Tech and an Elite Global Team</strong></h3><p>Invisible optimized the workflow delivering 8x efficiency through batching and parallel processing. We triaged customer tickets by state and insurance provider and engaged our skilled global workforce to validate insurance on Headway's behalf.</p><p></p><h3><strong>Result: 8x Faster Claims Processing</strong></h3><p>Invisible achieved an 8x faster claims processing speed than previous vendors. Furthermore, Invisible achieved a 37% cost reduction compared to the internal team and a 57% cost reduction compared to the previous BPO provider.</p>

Optimizing a key workflow in claims processing for Headway that skyrocketed claims processing speed. 

Achieving 8x Faster Claims Processing for Headway

<h2><strong>Getro Leverages Intelligent Operations to Ensure Best-in-Class Data Quality</strong></h2><p></p><p>Our friends at Getro, a seed stage startup building the leading platform for making professional introductions, was featured in the Wall Street Journal at the start of the new year after achieving positive cash flow in spite of market headwinds. As part of this journey, Getro leveraged outsourcing and in-house automation to ensure best-in-class data quality, which is central to their product’s value proposition.</p><p></p><p>Invisible was a critical part of the equation.</p><p></p><h3><strong>Expert Processes Developed: <span style="color: rgb(112, 0, 224)">2</span></strong></h3><h3><strong>Job Postings Enriched Yearly: <span style="color: rgb(112, 0, 224)">100,000+</span></strong></h3><h3><strong>Tasks QC'd: <span style="color: rgb(112, 0, 224)">100%</span></strong></h3><p></p><h3><strong>Problem: High-Volume Data Enrichment</strong></h3><p>Getro helps over 700 elite Venture Capital (VC) firms and other professional networks share thousands of jobs from their portfolio, automatically. VCs share the names of their portfolio companies, Getro then aggregates all the jobs from within the portfolio and keeps them up to date automatically on the fund’s job board. While technology does the heavy lifting, finding around 1.5m jobs daily, job posts need to be quality controlled (QC) and enriched with critical information, for example, a role’s location.</p><p></p><p>Getro needed a partner to make sure the job posts displayed were complete and correct, and they turned to Invisible for help.</p><p></p><h3><strong>Solution: Expert Process Design</strong></h3><p>Invisible runs standardized daily processes for Getro to QC job boards and enrich job posts with key data points. These help to ensure Getro’s platform is up-to-date and able to connect talented people with jobs they’d love. Altogether, Invisible QCs or enriches hundreds of thousands of jobs per year.</p><p></p><p><strong>Enrichment and Formatting of Job Descriptions with Location Data</strong></p><p><strong>Summary: </strong>Invisible operators receive a daily flow of job postings that need the role’s location data added, verified, and formatted for Getro’s platform.</p><p>Combining automation and people-led QC, we process job location tasks with Getro to ensure job posts are complete and accurate.</p><p><strong>How it works: </strong>Process-building experts at Invisible broke the workflow down into repeatable, easily-followed steps.</p><ol><li><p>Invisible operators receive a batch of job posts daily to process for location data to be added by the end of the day.</p></li><li><p>Operators validate location data if it is included with the information provided by Getro, or they add it if not.</p></li><li><p>100% of tasks are QC’d and logged in a tracking system.</p></li></ol><p></p><p><strong>Enrichment and QC of Job Descriptions with Correct URLs</strong></p><p><strong>Summary:</strong> For Getro to connect thousands of people with job openings, the platform has to understand where each VC portfolio company posts its open positions.</p><p>When a new company is added to the Getro platform, Invisible finds the URL that Getro needs, primarily with a people-driven approach.</p><p><strong>How it works:</strong> Process-building experts at Invisible broke the workflow down into repeatable, easily-followed steps.</p><ol><li><p>Invisible operators log on to Getro’s internal platform to find a daily batch of companies missing careers webpages.</p></li><li><p>Operators find the webpage where the company posts its open positions and enter it into Getro’s platform.</p></li><li><p>100% of tasks are QC’d and logged in a tracking system.</p></li></ol><p></p><h3><strong>Result: Leaner Operations for Getro</strong></h3><p>Invisible provided business value for Getro in two ways: by freeing up internal resources and cutting costs on high-volume, repetitive work.</p><p></p><p>Here is how Jess Powers, Business Operations Manager at Getro, describes our relationship:</p><h4><strong><em>“At the beginning of our relationship, Invisible spent time with us to truly understand our business needs and the problems we were trying to solve. They were able to take our feedback to build custom automations using both technology and people - freeing up internal capacity on our team for other work and taking on repetitive work at a reasonable and controllable cost. </em><br><br><em>We love the daily documentation and service they provide - especially the bi-weekly calls with our account manager that keep us in the loop on work performance and quality!”</em></strong></h4>

Enriching 100,000+ data points yearly to ensure best-in-class data quality for Getro. 

Helping Getro Reach Cash-Flow Positivity

<h2><strong>Saving 10,000 Hours Through Seamless Interoperability</strong></h2><p></p><p>High volumes of unstructured and disparate data caused friction when Nasdaq launched a new product. Through a strategic partnership, Invisible and Nasdaq streamlined data integration processes, significantly reducing customer onboarding times.</p><p></p><h3><strong>Onboarding Times: <span style="color: rgb(112, 0, 224)">-63%</span></strong></h3><h3><strong>Developer Hours Saved: <span style="color: rgb(112, 0, 224)">10,000+</span></strong></h3><p style="text-align: start"></p><h3><strong>Problem: Unstructured Data</strong></h3><p>Nasdaq introduced a new product that came with a lengthy two-month onboarding cycle, primarily due to non-standardized data. This process consumed valuable engineering time and resulted in a subpar customer experience.</p><p></p><h3><strong>Solution: End-to-End Interoperability</strong></h3><p>Invisible implemented a comprehensive integration solution, enabling seamless interoperability between diverse data platforms, and ensuring accuracy and reliability through stringent quality control measures.</p><p></p><h3><strong>Result: Thousands of Hours Saved</strong></h3><p>With our partnership, Nasdaq reduced onboarding times by 63%, saving over 10,000+ hours of engineers' time previously spent on non-engineering activities. The client was able to compete with incumbents by offering superior proprietary data for their 400,000+ clients.</p>

Implementing an integration solution that saved Nasdaq thousands of developer hours and supported a product launch. 

Saving Nasdaq 10,000 Developer Hours

<h1><strong>Structured Rating System</strong></h1><p>You.com is an AI chatbot and search engine that uses LLMs to deliver personalized responses to queries with human-like replies. Invisible improved the relevance of news articles referenced in search results by implementing a structured rating system.</p><p></p><h3><strong>Evaluations: <span style="color: rgb(112, 0, 224)">20,000</span></strong></h3><h3><strong>Increase in relevancy: <span style="color: rgb(112, 0, 224)">+70%</span></strong></h3><p></p><h2><strong>Problem: Unrated Results Led to Irrelevant Responses</strong></h2><p>You.com's search engine faced challenges in ensuring that the news articles referenced in search results were highly relevant and up-to-date.</p><p></p><p>Users expected to see relevant and recent articles provided to them as a response to their query. What they found was that outdated or irrelevant news articles would be surfaced, leading to a less-than-optimal user experience.</p><p></p><h2><strong>Solution: Structured Rating System</strong></h2><p>From data wrangling to labeling and structuring unstructured data, Invisible helps clients overcome challenges which arise from the complexity of integrating retrieval mechanisms with large language models. For You.com, Invisible implemented a structured rating system to evaluate article relevance based on the user's query intent, and evaluated 20,000 model responses.</p><p></p><p>The process would rate the news article for textual relevance, freshness, and diversity to determine relevance to the user’s informational needs. Our system continuously learns from feedback, refining its ability to fetch relevant articles and produce precise responses.</p><p></p><h2><strong>Impact: Unlocking Innovation</strong></h2><p>This approach established a clear framework for enhancing search accuracy and their systems saw a 70% increase in relevance. This enabled their teams to iterate more quickly and and implement experimental updates faster.</p><p></p><h4><em>"Talented, adaptable, helpful partners. Great attitudes. Friendly and fun to work with." - You.com Principle Product Manager</em></h4>

Implementing a structured rating system for an AI-powered search engine to increase relevance by 70%

Boosting User Experiences with a Structured Rating System for You.com

<h1><strong>Model Evaluations and Search Prompting</strong></h1><p>A top 50 startup, serving millions of users, develops personalized and contextual RAG chatbots that can participate in group conversations, with each chatbot conversing as a specific persona. Invisible performed evaluations and provided improved search prompting, enabling the client to enhance model performance and context efficiently.</p><p></p><h3><strong>Conversations Evaluated per Week: <span style="color: rgb(112, 0, 224)">300</span></strong></h3><p></p><h2><strong>Problem: Chatbot Responses Lacked Accuracy</strong></h2><p>The client faced a critical issue: their chatbots, while performing well in one-on-one chats, struggled in group settings. Customers noticed that the personas of these chatbots would shift unpredictably, creating confusion and undermining trust.</p><p></p><p>The client needed a strategic partner—one that could scale, ensure quality, and provide precision in assessing and refining chatbot responses.</p><p></p><h2><strong>Solution: Expert Human Review and Prompt Generation</strong></h2><p>Invisible's team worked within the client's platform to review model conversations from the research team and assess the level of accuracy of the model responses. We evaluated conversations for flow and factual accuracy, and provided revised search prompting. </p><p></p><p>These prompts were especially important and niche for the client - prompts had to match the tone and persona associated with their chatbots. Subsequent model responses were improved by integrating these new search results.</p><p></p><h2><strong>Impact: Improved Relevance</strong></h2><p>By guiding the AI to seamlessly incorporate search results, the client’s RAG chatbot transformed the quality of group chats—making them more engaging and deeply relevant.</p><p></p><h3><em>"Invisible is flexible and attentive to our needs. They have been close working partners and have been able to work closely with us to improve processes and annotation outputs." - Head of Trust and Safety</em></h3>

Evaluating conversations and providing search prompting, improving conversational flow and relevance.

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