Hammer Labs
The medium is irrelevant. Intent is everything.
At Hammer Labs, we create systems that interpret human intent seamlessly, adapting to how work actually happens in the real world. We do that by bridging human uncertainty with technological precision.
Our Mission
To advance intelligent intent processing in regulated domains. We build systems that understand what matters, not just what's documented, bridging human intent and technological capability.
Core Research Themes
The Uncertainty-Certainty Gap
We investigate the fundamental tension between the fluid, uncertain nature of human systems and the rigid, deterministic logic required by technological ones. Our research explores new computational models that can represent and reason with ambiguity, context, and implicit knowledge, moving beyond brittle, rule-based systems.
The Reality-Expectation Divide
Many technological approaches fail because they assume idealized conditions, expecting humans to adapt to the machine's needs. We study real-world workflows to design systems that meet reality where it is, building technology that adapts to human behavior rather than demanding humans adapt to it.
The Medium-Intent Disconnect
Modern AI often fixates on the medium (e.g., the structure of a PDF, the pixels in an image) rather than the underlying human intent. We are developing architectures that look past the superficial format to capture the core purpose, creating more resilient and adaptive systems that don't break when the medium changes.
A New Economic-Driven Paradigm
A core pillar of our research is the formulation of business-oriented loss functions. We question the sufficiency of traditional ML metrics like accuracy or precision, which often fail to correlate with real-world value. Instead, we are pioneering optimization frameworks that directly incorporate the nuanced, often asymmetric, costs and benefits of decisions in a business context.
Conceptual Structure of a Business-Aware Loss Function
BusinessLoss = ƒ(PredictionLoss, Cost_compute, Cost_data, Cost_opportunity, ...)
Where the function ƒ seeks to balance:
- Prediction LossThe conventional ML loss (e.g., cross-entropy).
- Computational CostA proxy for latency and resource expense.
- Data Acquisition CostPenalizes reliance on expensive or high-friction data sources.
- Asymmetric OpportunityReflects the real-world cost of being wrong in different ways (e.g., a false negative vs. a false positive).
Our Ethos
Meeting Reality Where It Is
Rather than designing for idealized environments, our research addresses how technology can adapt to existing workflows, recognizing the constraints and patterns in regulated domains.
Embracing Complexity
While many approaches attempt to simplify what's inherently complex, we investigate how systems can thrive within complexity, making it manageable without oversimplification.
Building for the World As It Is and Will Be
Our research focuses on the world as it exists and evolves, not how it 'should' be, examining how technological systems can deliver value in dynamic regulatory environments.
From Explanation to Causation
We believe the future of enterprise AI lies in moving beyond correlation to causal reasoning, enabling systems that don't just predict, but explain and justify.
Research Applications
Our theoretical frameworks find practical expression in real-world systems that validate our research hypotheses.
Anvil
A practical implementation of our business-aware validation research
Anvil demonstrates how our research into business-oriented loss functions and intelligent intent processing can be applied to real-world AI agent validation. It serves as a testing ground for our theoretical work on bridging the uncertainty-certainty gap in regulated domains.
Implements our research on context-aware validation beyond static compliance checking
Validates our business-aware loss function approach in production environments
Tests our medium-intent disconnect theories in multi-modal agent systems
Provides empirical data on reality-expectation divide solutions
Research Impact
Our theoretical frameworks and research methodologies are being explored and validated through real-world applications across diverse domains. These collaborations help us understand how intent-preserving systems perform in practice.
Financial Intelligence
Investigating how business-aware loss functions apply to financial decision-making systems where asymmetric costs and regulatory constraints create complex optimization landscapes.
fenero.ai →Sales Intelligence
Exploring intent-preserving architectures in sales environments where understanding stakeholder motivations across different communication mediums is critical for success.
salefin.ai →Venture Research
Collaborating on fundamental questions about how technology can adapt to human systems rather than forcing adaptation, with applications across portfolio companies.
multiversal.ventures →Join the Conversation
Our work aims to redefine how organizations extract value from information by focusing on what truly matters: intent. If you are a researcher, engineer, or visionary passionate about solving these foundational problems, we invite you to connect with us.