Daxe: AI Infrastructure for Regulated Enterprises
Enterprise-grade AI platform delivering auditable intelligence for banks, law firms, and government agencies where precision and compliance are non-negotiable.
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The Banking AI Adoption Crisis
AI deployments in regulated industries remain trapped in perpetual pilot mode. Despite massive investment, financial institutions, law firms, and government agencies cannot move beyond limited proof-of-concept implementations. The challenge isn't innovation—it's trust, compliance, and operational reality.
Accuracy Failures
Large language models hallucinate, contradict themselves across document chains, and degrade when processing complex multi-source information. In regulated environments where a single error can trigger regulatory action or financial loss, this unpredictability is unacceptable.
Zero Auditability
Compliance officers, internal auditors, and regulators require complete transparency into AI decision-making. Current copilots, basic RAG systems, and vector search architectures cannot trace outputs to specific source documents—making them unusable for regulated workflows.
Data Fragmentation
Enterprise data exists in silos—documents separated from transactions, communications disconnected from CRM systems. Today's AI tools cannot correlate information across these boundaries while maintaining compliance and data governance requirements.
Cost at Scale
Large-context language models become prohibitively expensive and unreliable when processing millions of tokens. Performance degrades, latency increases, and costs spiral—preventing deployment at the scale required for enterprise operations.
Daxe's Next-Generation Architecture
GraphRAG Foundation
Daxe transforms unstructured documents into finance-aware knowledge graphs, enabling unprecedented correlation across contracts, transactions, emails, and enterprise systems. This structural approach delivers coherent reasoning across billions of data points.
Our graph-native architecture enables timeline reconstruction, entity relationship mapping, clause-to-contract linking, and multi-system anomaly detection—capabilities impossible with vector embeddings alone.
01
Structured Ingestion
Documents, transactions, and communications converted to entity-relationship graphs
02
Cross-System Correlation
Automatically links related information across previously siloed enterprise systems
03
Agentic Processing
Executes complete workflows with validation at every decision point
04
Auditable Output
Every answer traced to exact source text for regulatory compliance
Core Platform Capabilities
Finance-Aware Knowledge Graph
Converts all unstructured data into structured entities and relationships, enabling cross-system correlation of contracts, transactions, emails, and CRM data. Purpose-built for billion-scale document environments with no performance degradation.
Full Auditability Layer
Every AI output is automatically tied back to exact source text with complete chain-of-reasoning transparency. Required by internal audit, compliance teams, and regulators—a core differentiator versus copilots, standard RAG, and embedding-only systems.
Model-Agnostic Foundation
Seamlessly swap between OpenAI, DeepSeek, Google, Anthropic, or internal proprietary models. Prevents vendor lock-in, meets diverse security requirements, and continuously optimizes for performance and cost efficiency across workloads.
Agentic Workflow Engine
Automates up to 80% of regulated workflows including M&A diligence, underwriting, regulatory reporting, ESG analysis, and fraud investigations. Every step is traceable, eliminating the unpredictability that makes copilots unusable for compliance.
Enterprise-Grade Deployment
Deploy on-premises, air-gapped, or in fully zero-trust environments. SOC-2 Type II aligned with granular access controls and role-based permissions. Processes structured and unstructured data simultaneously without compromising security.
Why Daxe Wins in Regulated Markets
Daxe's competitive advantages are structural, not incremental. While competitors adapt consumer AI for enterprise use, we built our platform specifically for the demands of regulated industries from the ground up.
True Source Traceability
Most AI systems cannot show why they generated specific outputs. Daxe provides automatic, complete traceability to source documents and reasoning chains. This unlocks adoption in legal, audit, compliance, and banking functions where "trust but verify" is regulatory requirement, not preference.
Graph-Native Reasoning
Competitors rely exclusively on vector embeddings, which excel at semantic search but fail at complex reasoning. Daxe's persistent knowledge graph enables timeline reconstruction, entity tracking, clause-to-contract linking, and multi-system anomaly detection—a structural advantage in regulated workflows.
Regulatory Workflow Design
While competitors output single answers, Daxe executes complete workflows with validation at every step. This transforms AI from "assistant" into "auditable operator"—the only model that meets regulatory standards for automated decision-making.
Scale Without Degradation
Daxe handles millions of documents and thousands of concurrent queries without losing accuracy, speed, or cost efficiency. Our architecture eliminates the performance and cost walls that limit large-context LLMs at enterprise scale.

Regulatory Alignment Advantage: Daxe actively supports the California Department of Financial Protection and Innovation (DFPI) on AI safety frameworks and agent-to-agent protocols—providing early visibility into emerging regulatory requirements that banks will soon be required to follow.
Enterprise Traction and Deployment
Production Platform Metrics
Daxe has moved beyond pilot stage into production deployments with 70 active enterprise users. Our platform powers agentic workflows across the most demanding use cases in financial services: M&A diligence, underwriting automation, mortgage and loan, Customer Data reporting, and regulatory comparisons.
Every deployment features multi-layer validation and 100% source traceability. Enterprise customers run Daxe on-premises and in zero-trust environments, processing structured and unstructured data simultaneously while maintaining complete audit trails.
70
Active Enterprise Users
5
Tier 1 Banks in Pipeline
$2M+
Avg Contract Value
Tier 1 Banking Pipeline
Five major financial institutions are in active evaluation:
BMO Financial Group
Agentic M&A diligence, underwriting automation, customer experience data matching
Barclays
Multi-workflow legal evaluation across investment banking and compliance operations
BNY Mellon
Asset servicing and custody operations workflow automation
JPMorgan Chase
Enterprise-scale knowledge graph deployment for compliance workflows, they are also sponsoring an event Daxe is hosting at the SFMOMA.
VISA
Cross-border Agentic payment transaction monitoring and regulatory reporting
Commercial Model and Growth Trajectory
Pilot-to-Contract Structure
Daxe's go-to-market strategy emphasizes rapid proof-of-value through focused pilots that convert to multi-year enterprise agreements. Our pilot structure is designed for quick deployment and clear ROI demonstration.
Pilots run for 1-3 months at $20,000 per month, targeting specific high-value workflows where automation delivers immediate measurable impact. This approach allows enterprises to validate accuracy, auditability, and compliance requirements before committing to full deployment.
Upon successful pilot completion, customers convert to multi-year contracts averaging $2M+ in annual value. This reflects the platform's ability to scale across multiple workflows, departments, and use cases once initial validation is complete.
Remarkable Capital Efficiency
Daxe built production-ready enterprise software with only $100,000 in pre-seed funding—demonstrating exceptional capital efficiency and technical execution. This lean approach validates both the founding team's capabilities and the platform's technical soundness.
Current traction and pipeline support a projection of $5M in annual recurring revenue by end of year 2025, driven entirely by existing enterprise demand and warm introductions through accelerator programs.
Expansion opportunities extend beyond banking into government agencies and law firms, leveraging our DFPI partnership and demonstrated regulatory expertise to capture additional regulated vertical markets.
Go-to-Market Strategy
Tier 1 Financial Institutions
Leveraging warm introductions through top accelerator programs like BMO WMNfintech, BNY Mellon Accelerate introduction. Focus on high-ROI workflows with immediate budget justification—targeting chief risk officers, heads of compliance, and innovation teams at institutions already evaluating AI infrastructure.
Mid-Market Financial Services
Combining AI-driven outbound with targeted demonstration campaigns focused on regional banks, credit unions, and specialty finance firms. Strong presence at fintech conferences paired with founder-led thought leadership on AI governance and regulatory compliance to build category authority.
Regulatory and Government
Building on established DFPI partnership around AI safety frameworks to gain credibility and visibility with regulators. Strong positioning for upcoming federal and state regulatory frameworks requiring auditable AI systems—turning compliance requirements into competitive advantage.

Channel Strategy: Enterprise sales motion combines direct relationship-building with strategic partnerships through financial accelerators and regulatory advisory work. Average sales cycle: 3-6 months from introduction to signed contract.
Leadership Team and Advisors
Erika Bahr – Founder & CEO
Over 10 years of experience building AI and data systems for Fortune 500 companies, with deep expertise in regulated workflow automation. Graduate of Harvard Business Analytics Program and UVA Darden MSBA.
Erika specializes in deploying AI solutions within heavily regulated environments including finance, legal, and government sectors. Her work focuses on systems that meet the unique demands of compliance-driven organizations where accuracy and auditability are paramount.
Nathan Standiford – Co-Founder & CTO
Former Google engineer with extensive expertise in large-scale distributed systems, retrieval infrastructure, and knowledge graph architectures. Previously built core data and search systems at Google serving billions of queries.
Nathan leads Daxe's GraphRAG engine development, agentic workflow orchestration, and enterprise deployment infrastructure. His experience scaling systems at Google directly informs Daxe's ability to handle billion-scale document environments without performance degradation.
Strategic Advisory
Daxe's advisors bring deep domain expertise across graph databases, financial services innovation, and AI governance:
  • Senior leadership from Neo4j providing graph database architecture guidance and best practices
  • Financial services innovation and underwriting experts from major banking institutions
  • AI governance specialists focused on secure computing and regulatory compliance frameworks
  • Enterprise software go-to-market advisors with proven track records scaling B2B infrastructure companies
Investment Opportunity: $2M Seed Round
Use of Funds
Daxe is raising $2 million in seed funding to accelerate deployment across regulated enterprises and capture the massive opportunity in enterprise AI infrastructure.
Engineering
Enhance graph engine performance, expand agentic workflows, build enterprise system integrations
Security & Compliance
Strengthen regulatory infrastructure, achieve additional certifications, expand zero-trust capabilities
Customer Success
Build dedicated support for Tier 1 bank deployments and scale implementation capacity
Go-to-Market
Accelerate pilot-to-contract conversion, expand sales team, increase conference presence
Why Impression Ventures
Impression Ventures' investment thesis aligns precisely with Daxe's trajectory and needs. Your focus on deep involvement with portfolio companies, fintech vertical specialization, and backing founders in regulated industries makes this a natural strategic fit.
Specific alignment:
  • Financial services vertical focus and domain expertise
  • Hands-on, concentrated investment model supporting complex enterprise sales
  • Proven ability to scale fintech infrastructure companies through early growth stages
  • Investment appetite for AI systems with real enterprise defensibility and structural moats