Tangible is applying vertical data moats to financial services, representing a seed vertical AI play with enhancement generative AI integration.
As agentic architectures emerge as the dominant build pattern, Tangible is positioned to benefit from enterprise demand for autonomous workflow solutions. The timing aligns with broader market readiness for AI systems that can execute multi-step tasks without human intervention.
Tangible is a platform to facilitate the structuring, modeling, and management of asset-backed financial transactions.
A domain-specific combination of: (1) explainable ML/agentic models that map physical asset performance into underwriteable finance inputs, (2) live reconciled data infrastructure for collateral/receivable tracking and covenant automation, and (3) packaged structured-finance playbooks plus on-demand experts to operationalize deals for lenders and borrowers.
Tangible is positioning itself around industry-specific domain expertise and datasets (hardtech, batteries, asset-backed financing). The product language (standardized playbooks, domain-first infrastructure, single-source-of-truth for asset classes) indicates a strategy of collecting proprietary vertical data and operational know-how as a competitive moat.
Unlocks AI applications in regulated industries where generic models fail. Creates acquisition targets for incumbents.
Claims about extracting details from documents, reconciling receivables, producing fast diligence materials and surfacing ready insights suggest a retrieval layer over documents/records (vector or document search) that is combined with generative models to produce summaries/insights for lenders and underwriters.
Emerging pattern with potential to unlock new application categories.
Explicit mention of multiple AI providers (Anthropic and Google Cloud AI) signals a multi-model or multi-provider architecture — routing tasks to specialized models or using provider redundancy/fallbacks and ensembles for different capabilities (e.g., safety, summarization, embeddings).
Emerging pattern with potential to unlock new application categories.
Language about a single source of truth, reconciliation of every asset/data point in real time, and fine-grained access controls implies an entity-centric model (graph-like relationships between borrowers, assets, contracts, receivables). This suggests use of graph-like data stores or relationship indexes to support permission-aware queries and lineage for diligence.
Emerging pattern with potential to unlock new application categories.
Tangible builds on Anthropic, Google Cloud AI, leveraging Anthropic and Google Cloud AI infrastructure. The technical approach emphasizes unknown.
Agentic orchestration with simulation/interpretability chains. Multiple AI components (extraction agents, reconciliation agents, simulation agents, matchmaking agents) coordinate, with a human expert layer for verification and policy enforcement. Models are provided by third-party LLM vendors and likely invoked by internal controllers/agents.
Inferred task-specific routing: third-party LLMs + internal agentic components and specialist models/agents likely selected per-task (diligence extraction, reconciliation, simulation). Exact router implementation not described.
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sales led
Target: enterprise
custom
field sales
• customer testimonial: 'Our facility has completely changed the trajectory of our business' (Hardtech CEO)
Help hardware-focused companies structure and manage asset-backed debt funding through a collaborative platform that aligns all counterparty stakeholders.
Applying an agentic, simulation-driven layer specifically to structured finance and asset-backed diligence — with emphasis on interpretability chains — is relatively uncommon and aimed at regulatory/audit credibility in a high-stakes domain.
Creating machine-readable, standardized playbooks for new asset classes is a strong vertical moat — it directly targets the inertia in institutional diligence and turns procedural knowledge into repeatable software.
Tangible operates in a competitive landscape that includes Traditional banks and structured finance boutiques (e.g., large banks, specialty ABS desks, boutique advisors), Capchase / other fintechs offering non-dilutive capital (e.g., Capchase, Clearco, Wayflyer), Data-room / diligence automation providers (e.g., Intralinks, DealRoom, iDeals) and contract/ document AI (e.g., Kira Systems).
Differentiation: Tangible productizes and automates the transaction lifecycle with a collaborative software platform, AI-driven diligence, live reconciled data and subscription/fee-based offering — whereas banks/boutiques rely on manual processes, bespoke models and consultancy engagements.
Differentiation: Those competitors focus on revenue-based or cash-flow financing and templated product offers. Tangible focuses on asset-backed and structured debt for hard-tech/hardware (esoteric assets like batteries), combining bespoke structuring, live collateral infrastructure and AI modelling for physical assets.
Differentiation: Tangible tightly integrates diligence with collateral/receivables reconciliation, covenant and waterfall management and lender workflows for structured finance — not just document exchange or generic contract extraction — and augments this with domain-specific AI for physical assets and lending playbooks.
Explicit use of third-party large-language/assistant providers (Anthropic + Google Cloud AI) for an AI-powered diligence layer rather than attempting to build end-to-end LLM capability in‑house — signals a hybrid strategy that outsources heavyweight model inference while owning the higher-level transaction workflow.
Focus on standardizing 'playbooks' for esoteric physical assets (starting with batteries). This implies they are building domain-specific data schemas, valuation rules, and legal/credit templates that map physical asset properties to structured finance terms — a niche not served well by generic fintech tooling.
Claim of a single source of truth 'live data infrastructure' that reconciles every asset and data point in real time. Technically this suggests an event-driven streaming architecture with strong data lineage, near-real-time ETL from heterogeneous sources (accounting, IoT telemetry, legal docs), and reconciliation engines that create a canonical collateral model.
AI used not just for document extraction but for end-to-end pre-diligence: extracting collateral details, reconciling receivables, surfacing red flags and populating lender‑facing materials. That indicates pipelines combining OCR/IE, entity resolution, automated accounting mappings and business-rule engines to convert messy inputs into underwritable outputs.
Integration of structured finance primitives (covenants, triggers, waterfalls, funding requests) into product suggests they implement domain-specific state machines and contractual logic that automatically enforce or notify on covenant breaches — a step beyond simple reporting.
Tangible's execution will test whether vertical data moats can deliver sustainable competitive advantage in financial services. A successful outcome would validate the vertical AI thesis and likely trigger increased investment in similar plays. Incumbents in financial services should monitor closely for early signs of customer adoption.
“We offer products, features, or tools powered by artificial intelligence, machine learning, or similar technologies.”
“Use of AI Technologies We provide the AI Products through third-party service providers ('AI Service Providers'), including Anthropic and Google Cloud AI.”
“AI powered dilegence layer for transactions”
“Smart automation extracts collateral details, reconciles receivables, and highlights red flags, all before you start digging.”
“Put our AI to work”
“Vertical playbook-as-data: packaging standardized financing playbooks for novel asset types (e.g., batteries) as first-class structured data to feed models and underwriting workflows.”
