From Manual to Autonomous: How AI-powered TOUCHLESS® is Reshaping Mortgage Lending
The mortgage industry is at a pivotal moment. Rising borrower expectations, margin pressure, and regulatory complexity are forcing lenders to rethink how loans are originated, underwritten, and serviced. Traditional, manual-heavy processes are no longer sustainable in a market that demands speed, accuracy, and seamless digital experiences. This is where TOUCHLESS® by Tavant is redefining what’s possible. The Shift Toward Intelligent Automation For decades, mortgage lending has relied on fragmented systems and labor-intensive workflows. Underwriting decisions, document reviews, and condition clearing often require significant human intervention, which leads to longer cycle times, higher costs, and inconsistent borrower experiences. Today, forward-thinking lenders are embracing AI-driven transformation to address these challenges head-on. At the same time, lenders are under pressure to do more with less, making intelligent automation less of a future ambition and more of a present-day necessity. The Cost of Doing Nothing The numbers tell a stark story. The average cost to originate a mortgage remains above $10,000 per loan, while average cycle times still stretch beyond 40 days at many institutions. Manual underwriting reviews, redundant document checks, and fragmented condition workflows are the primary culprits—and they are entirely addressable with intelligent automation. Forward-thinking lenders are already acting. Industry data shows that institutions deploying AI-driven mortgage platforms are achieving 20–40% reductions in cycle time and cutting manual condition creation rates by 30–50%. Those are not pilot metrics. They are enterprise-scale results that directly impact profitability and borrower satisfaction. What Is TOUCHLESS®? TOUCHLESS® is Tavant’s AI-powered mortgage platform designed to automate and orchestrate the key stages of the lending lifecycle. It enables lenders to underwrite loans faster, clear conditions more accurately, and scale operations without proportionally growing headcount. At its core, the platform integrates four capabilities that work together as a unified system: AI-powered underwriting that evaluates borrower data and risk in real time, accelerating decisions while improving consistency Automated condition generation and clearing that eliminates the “stare and compare” manual review cycle through intelligent document recognition and validation Intelligent workflow orchestration that removes bottlenecks and routes work based on context, not just rules MAYA™, Tavant’s AI assistant, which acts as an intelligent co-pilot for lending teams—interpreting loan data, responding to underwriting inquiries, and guiding decisions with contextual insights Together, these capabilities transform TOUCHLESS® from an automation tool into a decision intelligence engine for the entire mortgage value chain. Use Cases That Move the Needle TOUCHLESS® is designed for real operational impact across the lending lifecycle. Here is where lenders are seeing the greatest returns: Faster, Smarter Underwriting AI-powered underwriting enables lenders to process loans with greater speed and consistency. By analyzing borrower data, documents, and guidelines in real time, TOUCHLESS® reduces reliance on manual reviews and improves decision accuracy. The impact is significant: lenders can scale underwriting capacity without increasing headcount, allowing teams to handle higher volumes without sacrificing quality. Automated Condition Management Condition generation and clearing have long been among the most time-consuming aspects of mortgage lending. TOUCHLESS® automates document recognition, validation, and fulfillment, dramatically reducing manual intervention. With AI handling these repetitive tasks: Manual condition creation can drop by 30–50% Cycle times are reduced by 20–40% This not only accelerates loan processing but also minimizes operational friction across teams. Enhanced Borrower Experience Modern borrowers expect the same speed and transparency from mortgage lending that they experience in other digital interactions. Delays, rework, and lack of visibility can quickly erode trust. By streamlining processes and reducing turnaround times, TOUCHLESS® enables lenders to deliver a smoother, faster, and more predictable borrower journey. Loans move forward with fewer interruptions, creating a more consistent and satisfying experience. Operational Efficiency at Scale TOUCHLESS® allows lenders to fundamentally rethink how they scale operations. Instead of adding resources to handle growth, lenders can rely on automation and AI to increase throughput. Key outcomes include: Lower cost per loan through reduced manual effort Higher capacity per FTE without linear headcount growth Improved compliance and auditability through consistent, explainable AI decisions This is not just efficiency, it’s a structural shift in how mortgage operations are run. MAYA™ as a Co-Pilot A standout part of TOUCHLESS® is MAYA™, Tavant’s AI assistant. MAYA™ acts like a digital co-pilot for lending teams, helping users interpret loan data, respond to underwriting and servicing questions, and guide decisions with contextual insights. This human-plus-AI model matters because lenders do not want a black box. They want faster decisions that are still explainable, auditable, and consistent with policy. MAYA™ helps make that possible by giving teams a smarter way to work, not just a faster one. Why This Matters Now Market conditions in 2026 have created a narrow window for lenders willing to invest in intelligent infrastructure. Regulatory complexity is increasing. Experienced staff are difficult to retain. And borrower expectations are being set by industries that have nothing to do with mortgages. Lenders deploying TOUCHLESS® are gaining a measurable competitive edge by addressing all of these pressures simultaneously: Reduced loan cycle times that improve borrower satisfaction and increase pull-through rates Lower cost per loan through automation of routine reviews, document handling, and condition workflows Improved accuracy and compliance with AI-driven consistency that applies policies the same way, every time Scalable operations that grow with volume without growing headcount proportionally The Path to Autonomous Lending Fully autonomous mortgage lending is no longer a theoretical horizon—it is an operational trajectory. TOUCHLESS® is the platform that moves lenders along that path systematically, combining AI, automation, and data-driven intelligence to shift operations from reactive and manual to proactive and self-executing. Tavant delivers TOUCHLESS® alongside adoption playbooks and outcome-linked KPIs so lenders track business results, not just system activity. When teams experience fewer reworks, faster clears, and AI recommendations they can trust, resistance gives way to real productivity gains. The future of mortgage lending is autonomous, intelligent, and frictionless. With TOUCHLESS® by Tavant, that future is not on the roadmap, it’s already in production. If you want to see TOUCHLESS® in action, join Kayla and the Tavant team at HousingWire’s The Gathering in Austin. We’ll be hosting a live
Operationalizing Contextual AI in Advertising
What Industry Leaders Are Actually Doing at Scale Insights from discussion with leaders from Roku, DAZN, Philo, Intersection and Tavant at Streaming Media Connect on building real-time, AI-driven advertising systems: ▪ Why AI is now foundational to ad operations at scale ▪ How platforms align ads with tone, sentiment, and live context ▪ Where most ad tech stacks break and why ▪ The 4-layer framework powering contextual intelligence Download the Full article First Name * Last Name * Work Email * Phone Number Company * Job Title Download the Article Why This Matters Now Contextual advertising is no longer a targeting tactic, it’s becoming a must-have operational capability. Scale is challenging existing workflows Millions of creatives, real-time signals, and multi-platform delivery are overwhelming traditional systems. Ad operations are deeply fragmented Campaigns span CRM platforms, ad servers, DSPs, and reconciliation layers, creating friction at every step. Real-time decisioning is expected, but rarely operationalized Most teams still act on delayed insights instead of live signals Measurement gaps persist, but decisions can’t wait The gap between what advertisers want to know and what systems can prove remains unresolved “The question is no longer whether contextual advertising works, it’s whether your organization can execute on it and operationalize it at scale.” At scale, AI is not optional, it’s foundational The biggest constraint isn’t technology, it’s fragmented operations. AI-powered accelerators help unify workflows Context now means tone, sentiment, and real-time signals Leaders are optimizing in real time, even without perfect measurement Download the article to explore all The 4 Layers of Contextual Intelligence Learn how leading platforms structure their AI systems to process signals, enrich context, make decisions in real time, and activate across fragmented ecosystems. See how this framework works From the Experts Running AI in Production “AI isn’t giving you the answer. It’s giving you a confidence level.” Roku “The future isn’t about placing ads in a show — it’s about aligning with moments.” Philo See How Industry Leaders Are Scaling Contextual AI Download the Full Article
Testing AI Agents: Enterprise-Grade Strategies for Reliable, Safe, and Trustworthy Systems
AI agents represent a significant shift from traditional deterministic software systems. Unlike rule-based applications, AI agents are typically composed of large language models (LLMs) combined with memory, planning logic, and external tool integrations. Their behavior is probabilistic, context-driven, and often adaptive based on inputs, retrieved knowledge, and execution feedback. Because of this, testing AI agents is not limited to validating outputs against predefined expectations. Instead, it requires assessing behavior, robustness, safety, consistency, and user experience across evolving contexts and workflows. Quality Engineering for AI agents must therefore extend beyond conventional functional testing into behavioral, conversational, and governance-oriented validation. This article outlines six practical, enterprise-ready strategies for testing AI agents deployed in real-world systems. 1. Build a Structured and Comprehensive Prompt Test Suite AI agents are fundamentally driven by natural language inputs. In production, these inputs are rarely clean, complete, or unambiguous. A robust test strategy must therefore include a well-structured prompt suite that reflects real user behavior. An effective prompt suite should cover: · Happy-path prompts aligned with documented user journeys · Ambiguous or incomplete instructions that test intent inference · Colloquial language, typos, and informal phrasing · Domain-specific terminology (e.g., finance, healthcare, supply chain) · Adversarial or edge-case prompts that stress reasoning limits · Cultural and regional language variations · Boundary prompts that approach policy or capability limits From a QE perspective, prompts should be categorized, version-controlled, and traceable to requirements or user intents. This enables repeatable regression testing as models, prompts, or orchestration logic evolve. 2. Integrate Human-in-the-Loop Evaluation for Qualitative Validation Automated validation can measure response structure, latency, and basic correctness, but it cannot fully assess semantic quality, intent alignment, or user usefulness. Human-in-the-loop evaluation remains essential, particularly during early releases and major changes. Enterprise-grade approaches include: · Structured evaluation rubrics for clarity, relevance, and completeness · Rating scales for helpfulness and intent fulfillment · Standardized tags such as ambiguous, overly generic, or hallucinated · Reviewer notes for edge cases and failure patterns · Periodic sampling rather than full manual review for scalability To reduce subjectivity, organizations should define clear evaluation guidelines, involve domain SMEs, and track reviewer agreement trends over time. 3. Perform Behavioral Consistency and Regression Testing AI agents can exhibit behavioral drift due to: · Model upgrades · Prompt or system instruction changes · Toolchain modifications · Memory or retrieval logic updates Consistency testing ensures that critical behaviors remain stable across versions, even when exact wording varies. Recommended practices include: · Maintaining a golden prompt set for regression validation · Capturing baseline responses or semantic embeddings · Comparing responses using semantic similarity, not exact text matching · Flagging material behavior changes for human review · Defining acceptable variation thresholds (especially for probabilistic outputs) The goal is not identical responses, but consistent intent fulfillment, safety posture, and decision logic over time. 4. Validate Multi-Turn and Stateful Conversations In enterprise use cases, AI agents rarely operate in isolated, single-turn interactions. They are expected to maintain context, reason across steps, and support long-running workflows. Conversation-level testing should validate: · Context retention across multiple turns · Correct handling of follow-up questions and clarifications · Graceful recovery from interruptions or topic shifts · Memory summarization or recall accuracy · Avoidance of contradictory or repetitive responses Testing should simulate real workflows, not just individual prompts, and explicitly verify how the agent manages context windows, memory constraints, and conversation state. 5. Rigorously Test Safety, Policy, and Guardrails Safety validation is a core responsibility in AI Quality Engineering, not an afterthought. Agents must behave predictably and responsibly when exposed to sensitive or adversarial inputs. Guardrail testing should include scenarios involving: · Offensive, abusive, or harmful language · Attempts to bypass system limitations or policies · Requests related to restricted, regulated, or sensitive topics · Bias-triggering inputs or leading questions · Non-compliant data access or action requests Expected behaviors should be clearly defined, such as: · Polite refusal with policy-aligned explanations · Redirection to safety or allowed alternatives · Escalation to human support where appropriate · Neutral, non-judgmental language in sensitive cases These behaviors should be validated continuously, especially when models or policies change. 6. Measure Success Using Multi-Dimensional Quality Metrics Accuracy alone is insufficient for evaluating AI agents. Enterprise readiness requires multi-dimensional success criteria that capture technical performance, behavioral quality, and user experience. Key metrics may include: · Task Completion Rate: Did the agent successfully complete the intended workflow? · Intent Alignment: Did the response match the user’s underlying goal? · Clarity and Explainability: Was the output understandable and actionable? · Latency and Responsiveness: End-to-end response time, including tool calls · Safety and Ethical Compliance: Absence of unsafe, biased, or policy-violating content · User Satisfaction: Ratings, feedback, and adoption signals Together, these metrics provide a holistic view of agent quality in production environments. Conclusion Testing AI agents is fundamentally a challenge, not just a model evaluation exercise. It requires validating behavior across uncertainty, ensuring safety under stress, and maintaining trust as systems evolve. By combining structured prompt testing, human evaluation, behavioral regression checks, conversational validation, safety guardrails, and multi-dimensional metrics, organizations can move beyond experimentation and build enterprise-grade AI agents that are reliable, responsible, and production-ready.
TURNING ENCOMPASS® INTO A TOUCHLESS®, INTELLIGENT MORTGAGE PLATFORM
In 2026, the most competitive lenders are not asking how to configure Encompass®, they are asking how to turn it into a system of intelligent execution. As margin compression, lean staffing models and growing loan complexity reshape the industry, the integration of Tavant’s solutions with ICE Mortgage Technology’s Encompass® is emerging as a practical path to that future.
Customer success AI agents: transforming dealer and partner support in European manufacturing
As aftermarket revenues surge, Europe’s manufacturers must rethink support, according to Roshan Pinto, SVP & Head of Manufacturing at Tavant. AI agents are emerging as always-on partners, transforming dealer service, consistency, and customer trust after the sale. Europe’s manufacturing industry growth increasingly depends on what happens after the sale. Service and aftermarket revenues are rising faster than new equipment sales, and leading industrial players now generate one-third or more of total income from aftermarket services. For Europe’s vast service ecosystem from automotive to industrial equipment, this shift is structural: the vehicle fleet keeps getting older and complex equipment stays in service longer, expanding demand for timely, high-quality support. The opportunity is big; so is the operational strain on OEMs, suppliers, and dealer networks. Fragmented service systems and rising customer expectations are forcing OEMs to rethink their support strategies. It’s time to augment the frontline with Customer Success AI Agents: autonomous digital team members that understand context, act within enterprise systems, and learn continuously, so every dealer and partner interaction delivers consistency and builds trust. The dynamics shaping partner support today Aftermarket and dealer support operations across Europe and globally are navigating several converging dynamics: 1) Multiple systems, manual lookups Support teams often work across multiple platforms—CRM, ERP, warranty, and knowledge bases—to answer a single query. As volume increases, response times can lengthen, backlogs expand, and escalation costs rise. 2) Varied experiences across regions, languages, and channels A European dealer network spans languages, time zones, and tools. Manuals may exist in only one language, service advisories land late, and tone varies by region. Delivering consistency across channels requires multilingual, omnichannel capabilities. 3) Complex product lines; steep learning curves Ever-expanding SKUs and software-defined machines mean longer “time to competency” for staff, heavier reliance on scarce experts, and variability in fix rates—especially for first-line partners. Meet the customer success AI agents Imagine if every dealer and partner could access a tireless, always-on expert, one that understands the nuances of your products, speaks your partners’ languages, and never forgets a detail. That’s the promise of the Customer Success AI Agent. Learn more Manufacturers deploy agents that move beyond FAQs and manuals—learning from each interaction and adapting to product change. These agents are more than chatbots; they can: Distinguish emotions from transactional requests: Detect sentiment, adjust tone, and escalate when a relationship is at risk. Provide 24/7 multilingual support: Whether your dealer is in Lyon, Milan, or Warsaw, they receive consistent, expert assistance in their native language, at any hour. Leverage multi-agent collaboration: Advanced support leverages a team of specialized AI agents (for triage, troubleshooting, escalation, etc.) that work together seamlessly, ensuring every inquiry is handled by the best “virtual expert” for the job. Check out our monthly thought leadership webcast series showcasing how AI Agents are transforming manufacturing aftermarket operations. The technology behind customer success AI agents The capabilities behind these AI agents aren’t just raw computing power; it’s a stack of technologies purpose-built for manufacturing: Domain-tuned Large Language Models (LLMs): Unlike generic AI, these are fine-tuned on technical manuals, service histories, and even warranty data, so they understand not just language but the context of manufacturing and service. Deep system integration: AI agents can perform secure operations directly in your ERP or CRM, logging cases, checking inventory, or scheduling field service, without human intervention. Real-time analytics and anomaly detection: By scanning support tickets and IoT sensor data across your dealer network, AI agents surface emerging issues (e.g., a batch of faulty sensors in France) before they become costly recalls. Built-in compliance and knowledge management: With strict data protection standards like GDPR in play, today’s AI agents are designed with privacy, security, and auditability from the ground up. Benefits for OEMs, dealers and partners 1) Faster response and resolution – Automation clears queues, routes issues to the right expert, and resolves repetitive cases quickly and efficiently, giving service networks resilience as volumes and complexity grow. 2) Higher partner satisfaction & loyalty – Consistency across languages and channels builds trust. Faster time-to-answer and first-time-fix lift NPS. 3) ROI & continuous improvement – Service is now a growth engine, AI agents amplify that momentum by reducing cost-to-serve and creating a self-improving knowledge flywheel. Five AI agent capabilities powering customer success Leading solution providers bring these capabilities together through domain-trained, production-ready AI agents designed for manufacturing aftermarkets. Each capability directly contributes to stronger customer relationships and dealer success: 1. Early-Warning Insights Agent – detects emerging product issues by analyzing service and sensor data so OEMs can act before problems spread. 2. Knowledge Management Agent – summarises complex troubleshooting steps from manuals, videos, and historical cases, making expertise accessible to every partner. 3. Multilingual Support Agent – delivers consistent, high-quality guidance in German, French, Italian, and beyond, reducing errors and enhancing the dealer experience. 4. Ticket Triage & Technician Assist Agents – automate case prioritization and equip technicians with on-demand, step-by-step instructions, driving faster repairs and higher first-time fix rates. 5. Sentiment Monitoring Agent – spots and acts on signs of frustration or dissatisfaction before they escalate, protecting dealer relationships and loyalty. The new standard for European manufacturing support Europe’s aftermarket is expanding, and equipment is ageing, creating more opportunities to win or lose dealer loyalty. AI-powered solutions built with an agentic approach are purpose-built for this reality: domain-tuned, transaction-capable, multilingual, and compliance-ready, so your dealers and partners get fast, consistent, and trustworthy support. OEMs investing in Customer Success AI Agents today are setting a new standard—delivering faster, more consistent, and more empathetic service on scale. Those who act now will strengthen their dealer networks, reduce support costs, and unlock new revenue streams. The future of intelligent service is here—and it speaks your language. Ready to transform your aftermarket operations? Discover how Tavant’s Service Lifecycle Management solutions leverage agentic AI. Visit Tavant.com to learn more or request a demo. This article was originally published by Tavant on The Manufacturer.
AI pricing agents: optimising parts prices to maximize sales and market share
European manufacturers are adopting AI pricing agents to protect aftermarket margins, bringing real-time intelligence, discipline, and speed to parts pricing in an increasingly transparent digital market. Our partners at Tavant tell us more. European manufacturers are competing in a parts market that has quietly become digital-first. More enterprises now sell online, buyers compare prices in seconds, and discounting can slip out of control across thousands of SKUs. In 2023, almost one in four EU enterprises made online sales, evidence that the channel shift is pervasive even in traditional industries. At the same time, the aftermarket remains the earnings engine: across advanced industries, aftermarket EBIT margins average 25% versus 10% for new equipment, making pricing discipline in parts a board-level issue. Yet pricing at scale is hard. Large OEMs and distributors often make daily price decisions on hundreds of thousands of SKUs, with disparate ERPs and homegrown tools, creating leakage and latency. Add macro volatility and intensifying price transparency, and margin compression follows. The good news; done well, data-driven pricing routinely moves the needle. Bain’s longitudinal work suggests a one per cent improvement in realised price can lift operating profit by eight per cent, more leverage than similar gains in volume or cost. And deployments of AI-enabled pricing in the aftermarket have delivered two – six percentage points of margin uplift while preserving coherent price ladders and competitive guardrails. From pricing projects to AI pricing agents The pivot manufacturers are making is from episodic “pricing projects” to always-on pricing AI Agents that sense, decide, and act. Based on our Price.AI solution, these three AI Agent patterns consistently create outsized value: Competitor Price Scout Agent: Continuously collects, correlates, cleans, and image-maps competitor parts price data, then cross-references it with OEM part numbers and supersessions. The Scout flags anomalies (e.g., a dealer undercutting list by 12%) and feeds clean signals to pricing and e-commerce systems. Recommendation Agent: Generates context-specific price or offer suggestions in real time, for example, nudging the web store to present a targeted bundle discount for a price-sensitive segment, or advising the dealer to hold price where elasticity is low. Optimisation Agent: Continuously refines list, net, and promotional prices subject to guardrails (price ladders, competitive floors, and segment targets), using ML models that learn from demand, inventory, and competitive moves. These AI agents don’t replace people; they scale good pricing judgment. They monitor market signals, run what-if simulations, and propose changes with explanations (why the net price should move up/down, which features drove the recommendation), so commercial teams can approve with confidence and audit decisions later. Best-practice pricing platforms pair optimisation with explicit guardrails to keep recommendations aligned with strategy and compliance. What great looks like (and why it matters in Europe) Forward-leaning European manufacturers are building four foundations: Unified data fabric that blends historical sales, warranty/claims, and channel data with external price signals from dealers, marketplaces, and aggregators (think a “price harvester” that never sleeps). Demand and elasticity modeling that incorporates seasonality, product lifecycle, promotions, and, where available, IoT/telematics signals to forecast usage-driven parts consumption. Peer-reviewed studies show AI methods (ML/DL and hybrids) consistently improve forecasting accuracy over classical baselines in manufacturing supply chains. Real-time monitoring and alerts (a “price pulse”), so teams see threshold breaches as they happen rather than at month-end. Orchestrated workflows (pricing requests, approvals, exception handling) that mesh with CPQ/ERP, eliminating manual rekeying and cycle time, critical when an online buyer expects a price change to propagate instantly across web, dealer, and marketplace channels. The European context adds two imperatives. First, digital channels are mainstream: with nearly one in four EU enterprises selling online, price transparency is a given, your buyers will find the lowest price in seconds. Second, AI capability is scaling fast: 13.5% of EU enterprises (10+ employees) used AI in 2024, up from eight per cent in 2023. Early adopters will set the reference level for speed and precision in pricing. Designing agent-driven pricing that sales teams trust Trusted pricing is not just about algorithms; it’s about guardrails and governance: Guardrails: Maintain price ladders and competitive floors to keep relative positioning intact while agents optimize within bands, an approach mirrored in leading pricing toolkits. Explainability: Every recommendation should show the drivers, e.g., competitor index, inventory carry cost, lifecycle stage, mirroring the explanatory UI you’d expect in a pricing cockpit. Human-in-the-loop: Give sales visibility and override rights, but measure overrides. Track the magnitude of changes, the number of accepted/declined recommendations, and revenue impact by segment. Speed to value: Start with a high-leverage slice (e.g., top 10% SKUs by revenue and volatility). Well run digital pricing programs often show meaningful margin improvement within three to six months, if operating model and tech changes land together. A practical roadmap for manufacturers Baseline the leakage: Quantify list-to-net waterfall, quote-to-price latency, and promo ROI. Use the “power of 1%” to align leadership on the value at stake [3]. Stand up the Competitor Price Scout: Ingest dealer and marketplace prices; normalize via part numbers/supersessions; create an internal “competitive price index” for each SKU. Segment and simulate: Cluster customers/SKUs by sensitivity, then run what-if simulations to stress-test guardrails before you touch live prices. Activate the Recommendation Agent on one channel (e.g., web store), with clear A/B tests and approval thresholds. Scale to the Optimisation Agent across channels, automating routine moves while escalating edge cases to pricing managers. Embed in SLM: When pricing is integrated with service, warranty, and parts planning, you capture cross-functional benefits, better availability, fewer emergency shipments, and higher customer satisfaction. For reference architectures that connect these functions, see Tavant’s SLM and TMAP overviews. Where Tavant fits At Tavant, these AI agents are part of Price.AI solution within a broader Service Lifecycle Management offerings, spanning competitive price analysis, monitoring/alerts, what-if simulations, demand forecasting, and API-first integration, so pricing decisions flow across dealer portals, e-commerce, and ERP/CPQ without friction. If you’re exploring a pragmatic blueprint, the following resources outline how manufacturers operationalise this at scale. Conclusion In Europe’s increasingly transparent parts market, AI pricing agents turn pricing from an occasional project into a daily competitive muscle. They watch the market, anticipate demand, and recommend moves
Beyond the Base Model: Crafting Niche AI That Works
In a recent discussion with technology leaders from the financial sector, I noticed a clear divide. Some argued for building AI models tailored specifically to finance, while others pushed back against the idea. I don’t believe we need to reinvent the wheel with finance-only AI models. Instead, we should focus on building smart solutions on top of the powerful models that already exist. A recent MIT report shows that nearly 95% of Gen AI pilot projects are failing, a strong reminder that the challenge isn’t in creating new models, but in how we apply them effectively. AI Economics Building your own AI model for financial services sounds bold and visionary but the reality is quite different. Training ChatGPT-4 reportedly costs around $100 million and estimates for ChatGPT-5 range anywhere from $500 million to over a billion. Google’s Gemini Ultra came in at $191 million. That’s an enormous investment, especially when less than 5% of GenAI pilots succeed. Large language models are like cloud computing. When AWS and Azure launched, most organizations didn’t try to build their own cloud platforms. Instead, they leveraged the infrastructure those giants had already created, reducing costs and boosting efficiency. AI should follow the same playbook. Rather than pouring billions into reinventing the model layer, the real opportunity lies in building vertical solutions on top of offerings from OpenAI, Anthropic, Google, or Meta. The true value isn’t in another foundation model, it’s in specialization: blending general-purpose large language models (LLMs) with proprietary data, industry workflows, compliance safeguards, and user experiences that directly solve pain points in financial services. That’s where sustainable differentiation lives. Success Stories To see the value of vertical AI in action, here are some examples where it has already delivered results: Cursor is a great example of how focusing on a niche pays off. Built by the startup Anysphere, it’s an AI-native code editor designed specifically for developers. Instead of trying to create a new model from scratch, they built Cursor on top of ChatGPT. Their real strength was doubling down on user experience. Recently Cursor hit $500 million in ARR and reached a valuation of $10 billion indicating their decision paid off big time. Jasper AI was one of the first breakout vertical AI products. Launched in 2021 on top of ChatGPT-3, it targeted marketers who needed help creating content. What set Jasper apart wasn’t just AI, it was the domain-specific templates, brand voice controls, and collaboration features tailored to marketing teams. Users weren’t paying for raw model access; they were paying for a solution built for their world. The result? Jasper’s valuation shot past $1.5 billion in just a couple of years. Harvey AI shows what happens when you go deep into a vertical. This legal AI startup was built on OpenAI’s GPT-4, but it wasn’t about chatting, it was about legal reasoning. Harvey gave law firms a natural-language interface for contract review, case analysis, and compliance research, all with the necessary guardrails for confidentiality. By 2023, Harvey AI had already raised $80 million in funding. Once again, the success came from specializing on top of an existing model, not reinventing it. Being part of the Fintech industry, I see similar moves in the mortgage sector. Some lenders have built internal knowledge bases on top of large language models, feeding them credit policies and seller guides while keeping access restricted to employees. These solutions have improved internal workflows, but the real opportunity lies ahead, when these AI-driven tools are extended to borrowers directly improving their experience. The true success of technological innovation lies not in its complexity or uniqueness, but in the difference, it makes to human life Why Vertical Innovation Here’s what makes vertical differentiation powerful: Carves out a niche: Targets a specific group of users with solutions that feel custom-made for them. Addresses specific challenges: Tackles the unique problems that this community faces, making the product more relevant and worthwhile. Controls cost effectively: Developing a large language model can run into hundreds of millions annually. Companies can use APIs instead, turning these costs into operational expenses and freeing up funds for customer acquisition and improved product design. Accelerates market entry: By leveraging foundation models, companies can launch in months rather than years. Facilitates expansion: Once a vertical solution proves successful, the same strategy can be applied to related products, opening new growth opportunities. Cautionary Note A lot of companies have tried jumping into building foundation models without really figuring out how they’ll make money from them, and it’s tripped them up. If you look back at tech history, you’ll see that infrastructure projects often need a lot of capital and benefit from scale, something most startups just can’t compete with. Conclusion The big players have already cornered the market on LLMs, so for everyone else, the real opportunity lies not in trying to create another one, but in crafting the right foundation tailored for users. The stories of companies like Jasper, Harvey, and Cursor demonstrate how using LLMs as a starting point and zeroing in on specialized, niche applications, businesses can stand out in a sustainable way and see meaningful revenue growth.
Precision Agriculture & the Future of Farming: Sowing Smart and Reaping its Benefits
Explore the world of precision agriculture and its potential to shape the future of the global food system Highlights: Discover how precision agriculture can increase crop yields by up to 20% while lowering costs by 15%. Learn how industry leaders drive sustainable practices with smart farming innovations. Explore how advanced digital farming tools reduce environmental impact while enhancing profitability.
Digging Deep: Revitalizing Agriculture Through Regenerative Soil Health
Regenerative Agriculture is a form of farming and grazing practice that helps rebuild soil organic matter and restore degraded soil biodiversity. Explore the benefits of soil health, its indicators, methods of revitalization, and more to find out how these practices help result in carbon drawdown, improve the water cycle, and reverse climate change.
Top 25 Metrics to Measure Your Service Quality and Drive Excellence
Service quality is the cornerstone of exceptional customer experiences. It’s the foundation upon which lasting relationships are built. In this article, we delve into the top 25 metrics that serve as the guiding stars for achieving operational excellence, efficiency improvement, cost reduction, customer satisfaction enhancement, revenue generation, service quality benchmarking, employee engagement, quality assurance, and predictive insights.