Last week, we hosted the Agentic AI in Lending Webinar: Unlock Your Competitive Edge🚀 Huge thank you to everyone who joined live and especially to the incredible leaders who shared what real AI adoption looks like inside regulated lending teams ❤️ Key takeaways that hit hard: 1. Scale lending capacity without scaling headcount FORUM Credit Union publicly shared: AI-driven underwriting lets them process up to 70% more loans, no extra staff needed. Pure operational leverage. 2. The real bottleneck is workflow friction, not demand Manual doc review, endless back-and-forth, siloed systems → slow decisions. Agentic workflows orchestrate intake → analysis → validation → recommendations end-to-end. 3. Transparency is non-negotiable In lending, you MUST see: what the agent reviewed, which rules applied, why it recommended X. Full traceability = trust. 4. Underwriters evolve into high-leverage decision-makers AI handles ingestion first-pass analysis. Humans focus on judgment, edge cases, and building member relationships. If you're a credit union leader eyeing AI: the opportunity is massive → scale decisions, not headcount. 🥂 Comment “Multimodal Lending”below and we’ll DM you the full recording! Follow @MultimodalAI to watch us tackle more financial services bottlenecks with agentic AI. Exciting builds incoming... 👀 #AgenticAI #Lending #CreditUnions #FinTech #AIinFinance

The finance ops problem nobody talks about: Your best analyst isn't slow because they're bad. They're slow because they spend 60% of their day pulling data from systems that don't talk to each other, formatting it into templates, and waiting for approval chains that haven't changed since 2004. The bottleneck isn't intelligence. It's plumbing. That's what we're fixing. @MultimodalAI

One of Europe's leading telecommunications companies processes thousands of payment orders and payment forms every month. Until recently, every single one was handled manually. Staff reviewed each document individually, extracted transaction details by hand, categorized payment types, and entered data into downstream systems. As volume grew, the team couldn't keep up without adding headcount. Errors accumulated. Rework was constant. Compliance exposure grew alongside it. The technology to fix this existed. The infrastructure to connect it to their actual documents didn't. That's where we came in. We deployed Document AI and Decision AI on their existing infrastructure, no rip and replace, no migration project, no disruption to live operations. The AI agents took over document classification, field extraction across transaction details, payer information, and payment amounts, and routing to downstream systems. Built around their document types. Integrated with their existing stack. The results came immediately: 50% reduction in processing time across payment orders and payment forms. 93% classification accuracy significantly above the manual baseline. Fewer errors, less rework, and a compliance posture that no longer depended on individual staff not having a bad day. More importantly: the foundation is now in place to expand automation across additional document types as volume grows without expanding the team proportionally. This is what scalable AI looks like in practice. Not a pilot that produces a dashboard. A deployed system that changes the economics of a core operation. #DocumentAI #AgenticAI #PaymentAutomation #FinancialServices #AI

We moved away from @cursor_ai to @claudeai Code and Codex. Not because Cursor isn't good it is. But if you're on the bleeding edge, you're moving toward coding agents that don't need an IDE at all. Here's what that has actually changed at Multimodal: The workflow used to be: engineer understands the requirement, engineers writes the code, engineer reviews the PR, repeat. The workflow now is: agentic coding drafts the PRD from existing code and roadmap context, creates the Linear tickets, writes the implementation, generates the PR stack. The engineer reviews, redirects, architects. The ratio of human judgment to human execution has inverted. We run Claude Code and Codex in tandem — they're not the same tool. One plans, the other reviews. One drafts, the other stress-tests. The ping-pong between them catches things neither would alone. What this has meant in practice: we're hitting our product roadmap materially faster with the same team size. We measure it in two ways compression in time-to-release for product features, and PR volume normalized for headcount. The honest framing: the constraint isn't gone, it's just shifted. You still need engineers who can think architecturally, who know when the agent is wrong, who can define what "done" actually means. Those people are now 5-10x more productive. The engineers who can't operate at that level who still want to write every line themselves are increasingly misaligned with how software gets built now. That's not a comfortable thing to say. But it's what's true. #AgenticCoding #AI #Startups #Engineering #ClaudeCode

We've been building AI for financial institutions for three years, and getting our own organization to adopt it has still been one of the harder problems. We say that not as a confession. We say it because we think it's the most honest and useful thing we can tell anyone who's trying to drive AI adoption inside their own company right now. Two years ago, the bar was: is your team using ChatGPT? Fine. Adoption was uneven but the stakes were lower. Maybe some people were using it well, some weren't. It didn't break you. Today the bar is completely different. Agentic AI - coding agents, workflow automation, AI-assisted go-to-market isn't a productivity tool you opt into. It's an organizational capability you either have or you don't. And the gap between teams that have it and teams that don't compounds every quarter. At Multimodal, that's meant being very deliberate about who the champions are, who the stewards are, and who the laggards are and being honest about what to do when someone isn't adapting fast enough. We've made hard calls. Some people who weren't moving at the pace the technology demands are no longer here. That's not a comfortable thing to say. But we think it's the right frame. The job of a leader right now isn't just strategy or product vision. It's getting every level of the organization to actually use the tooling — in the most optimal way — before your competitors do. The pace of adoption inside your organization is a competitive variable. Most leaders haven't started treating it that way yet. #AI #Leadership #AgenticAI #Startups #FintechNews

Tomorrow we publish the most comprehensive look at agentic AI in private equity that exists. 51 pages. 50 sources. The honest answer to why 95% of PE AI fails and what the 5% that works actually looks like. Free. 9am ET. Tomorrow.

People keep using the word "trust" without saying what they mean. When a bank asks whether they can trust an AI system, they are not asking one question. They are asking at least five. - Can I trust how the AI handles our customers' data? - Can I trust that the AI can explain why it made a decision? - Can I trust that the AI operates within proper governance frameworks? - Can I trust that a human will step in when the AI is uncertain? - Can I trust that the AI follows our compliance obligations? Each one of those is a different problem with a different answer. You can have an AI that is completely secure from a data standpoint but has zero explainability built in. You can have an AI that is technically compliant but has no real governance framework around it. Trust in agentic AI is a stack, not a switch. The components: information security, explainability, governance, compliance, AI ethics, human oversight, and audit trails. Every one of them matters. We will go through each one in this series. --> Follow this page and save this post. We are breaking down each pillar over the next 1 week. #TheTrustSeries

Why compliance breaks in fragmented workflows? Regulated workflows fail in the seams, not in the steps. Workflow tools can generate an audit trail for what they touched. The problem is that most financial services processes span multiple systems, teams, and decision points, so the audit record gets fragmented. That is where inconsistency creeps in. What breaks: 1. Policy drift. A rule enforced during origination does not automatically carry into servicing, reporting, or exception handling. 2. Missing context. Approvers review an output without a standardized record of inputs, thresholds, and prior decisions. 3. Unreconcilable audits. When logs live across multiple platforms, proving consistent control becomes a manual, narrative exercise. Process orchestration makes compliance an architectural property. Rules live at the process level, approvals are triggered with full context, and every action is recorded in one continuous trail. That is the difference between “we think we are compliant” and “we can prove it quickly.” #workflowtool

Anthropic CEO just said Ai will wipe out 50% of lawyers, consultants, and finance professionals within 12 months: x.com/theUMreal/status/20269…

We’re proud to share that Multimodal was selected as one of the top 4 winners in the Kansas Bankers Assn Association (KBA) NXTUS Accelerator after the final pitch! 🥂 We entered the program with an incredible group of companies, made it to the final round as one of nine finalists, and then KBA selected us as a top four winner. Our VP of Sales, Nicholas Bianchi represented us and this recognition means a lot, not just to our team, but to the kind of work we’re building toward. Because what this really represents is momentum: → more banks looking for practical AI adoption (not just experimentation) → more urgency around fixing operational bottlenecks → more openness to modernizing how critical work gets done across lending, ops, and compliance Big moment for us. Even bigger opportunity in front of us. #Multimodal #AgenticAI #BankingInnovation #CommunityBanks #CreditUni

Most financial institutions don’t have an AI problem. They have a workflow problem. Over the last year, we’ve spoken to banks, credit unions, and PE firms all running the same experiment: 10 AI tools. Multiple vendors. Internal pilots everywhere. And still… no real operating leverage. The issue isn’t model quality. It’s orchestration. 1. Where exactly does AI sit in your underwriting flow? 2. Which steps should be automated vs supervised? 3. How do you connect outputs across compliance, risk, ops, and investment teams? 4. Who owns the agent once it’s live? This is the gap. And this is why we’re building Playbooks at Multimodal Playbooks are not demos. They’re not sandbox experiments. They’re not generic “AI transformation decks.” They are battle tested, workflow level blueprints designed specifically for financial institutions. Underwriting Playbook. Portfolio Monitoring Playbook. Compliance Automation Playbook. Deal Flow Intelligence Playbook. Each one maps AI agents directly onto how your teams already operate. Day one, it feels native. No chaos. No tool sprawl. No six month science projects. What changes? Time to decision drops. Manual review cycles compress. Analyst leverage increases. Cost per transaction decreases. That’s real margin impact. Most firms are still asking, “How do we use AI?” The better question is, “How do we redesign our workflows around it?” Playbooks answer that. We’re releasing soon. If you’re leading AI, ops, risk, or tech at a financial institution and you’re tired of disconnected pilots, this will matter. AI doesn’t create leverage by existing. It creates leverage when it’s embedded into how work actually gets done. That’s what Playbooks are built for. More soon.

Big news! Multimodal has been selected as the build partner for FiLab’s Agentic AI Discovery Test Over the next several months we’ll work with Filene Research Institute and a cohort of credit unions to: → Educate teams on what agentic AI actually is → Build three real, hands-on POCs in our AgentFlow sandbox → Model time savings, error reduction, and member impact No slideware, just workflows credit unions can touch, test, and evaluate. Excited to help shape the next generation of member service! #CreditUnions #AgenticAI #FinTech #MemberExperience #Innovation

- Woke up today - Saw Moltbook exploding as the first social media for AI agents, bots posting, upvoting, and even forming digital religions without humans - Worked through an existential crisis - Created playbooks to automate for financial services @MultimodalAI - Went to sleep

Check out How an Automated Credit Decisioning System Works at @MultimodalAI 😱(Step by Step) A typical automated credit decisioning workflow includes: 1. Case setup and intake Applications and documents are ingested, validated, and normalized automatically. Missing data is flagged early, reducing back-and-forth and manual entry. 2. Intelligent diligence Instead of reviewing everything, the system highlights only the areas that matter; based on policy thresholds, risk signals, and data gaps. 3. Micro-decisioning against policy Identity checks, consistency checks, eligibility rules, and policy constraints are evaluated step by step, not as a single opaque decision. 4. Final decision and routing Applications are approved, declined, or conditionally approved. Edge cases are routed to human reviewers with clear context. 5. Confidence scoring and justification Every decision is paired with a confidence score and explanation, supporting auditability and regulatory review. 6. Reporting and documentation Full decision trails, exceptions, and outcomes are automatically documented for compliance and transparency. The result: faster decisions, lower operational risk, and consistent policy enforcement without sacrificing human oversight. #productvideo #AutomatedCreditDecisioningSystem

Despite the fintech surge, trust remains a credit union advantage. Consumers still see CFIs as safer than digital banks, especially when it comes to data privacy and fraud protection. Double down on that perception. Use AgentFlow to operationalize safety: 1. Employ Decision AI for proactive fraud detection 2. Automate suspicious activity alerts via Conversational AILog and audit every action for compliance with full traceability Security and governance are embedded in the AgentFlow platform, from SOC 2 Type II compliance to VPC deployments and immutable audit logs.

We’ve been trying to figure out why our posts weren’t functioning; just to figure out it was shit by the X algorithm function. Elon literally open-sourced X algorithm, So yeah, now we KNOW. X uses an AI model that predicts how likely people are to: like, reply, repost, watch, click your profile, or share your post in DMs. More of that = boosted. More mutes, blocks, “not interested” = buried. As a company, for X posting strategy we thought quantity will eventually compound down but —-> Post too much in one day? Your reach actually decays. (aka stop spamming.) Longer watch time on videos = more push. Profile clicks new followers after a post = strong signals. So no, it’s not shadowbans. It’s not “the app hates me.” It’s people aren’t engaging → the AI stops showing your content. Brutal. But at least now we know what to fix

The current wave of AI is human-to-agent: you ask, it answers. The next wave is agent-to-agent: one agent completes a step, hands off to another, and the workflow keeps moving. That’s the trend I’m most excited about: orchestration. —>The reason is simple, business workflows don’t happen in one system. They span tools, teams, vendors, and data sources. And most of the friction is coordination. The real unlock is when agents can synchronize across that complexity: passing context, triggering actions, and collaborating even when they’re not built by the same company. That’s what turns “AI features” into “AI operations.” If you could automate one cross-tool handoff end-to-end, what would you pick? #AiFuture #Aitrend

Introducing AgentFlow, the Agentic AI Platform for Financial Services In 2026, “using AI” in banking and insurance isn’t the hard part. Running AI safely inside real workflows is. Most teams start with point tools and pilots. Then reality hits: 1. workflows span documents systems approvals exceptions 2. multiple agents need to collaborate reliably 3. compliance needs traceability, controls, and audit-ready logs scaling across teams turns into fragmentation and cost. That’s why we built AgentFlow: an all-in-one platform for creating, orchestrating, and monitoring AI agents in finance and insurance, with governance built in. Key takeaways 1. AgentFlow is an all-in-one platform for creating, orchestrating, and monitoring AI agents in finance and insurance. 2. It embeds governance with confidence scores, explainability, and audit trails for compliance and trust. 3. Flexible deployment and self-serve or white-glove setup let teams automate workflows at their own pace. #Productvideo #MultimodalAgentFlow

📈 In the next 5 years, scale for credit unions won’t mean “bigger.” It will mean “smarter.” Julie Esser reframes what real scale should look like as AI and emerging tech reshape financial services. A few powerful takeaways: 👉 Scale through collaboration. CUSOs and consortium models allow credit unions to pool resources, share risk, and access innovation that would otherwise be out of reach. 👉 Scale execution, not just ideas. The industry talks about technology more than it implements it. The real advantage will come from institutions that can move fast, test quickly, and turn experimentation into action. 👉 Scale data readiness. Data is the foundation of AI impact. Organized, secure, usable data will matter more than the number of tools deployed. 👉 Scale member impact. AI should free humans to deliver better, more personal member experiences not replace them. What does smart scale look like for your organization? #AI #CreditUnions #Leadership #FinTech #Innovation