How HawkenAI led AI engineering for a leading patent intelligence platform, cutting LLM costs and latency dramatically while preserving the accuracy high-stakes legal work demands.
Our client is one of the leading AI platforms for patent intelligence, trusted by top law firms, enterprises, and litigation funders to power high-stakes IP litigation and monetization work. As the company brought AI deeper into its patent workflows, it needed that AI to perform to the same standard as its human experts: rigorous, fast, and reliable enough for high-stakes legal matters.
Patent work leaves no room for sloppy output. A claim chart, a prior art result, or a portfolio analysis feeds directly into litigation and licensing decisions where accuracy and defensibility matter. HawkenAI led that effort as the company's AI engineering team.
Early implementations pushed heavy volumes of work through language models in ways that were slow and expensive. The cost of running these workflows and the latency users experienced both needed to come down sharply, without giving up any quality. The company needed an AI engineering leader who could take these systems from promising to production-grade.
Accuracy and defensibility high enough to trust in litigation and licensing decisions.
Speed that keeps pace with real deadlines across claim charting, prior art, and portfolio analysis.
Affordable enough to run at scale without brute-forcing every operation through an LLM.
Stable production infrastructure with the tracing and control operations teams demand.
HawkenAI led the company's AI engineering, owning the architecture and execution that turned LLM-powered patent workflows into efficient, reliable production systems.
HawkenAI introduced deterministic processing layers that handle what does not require a model, reserving model calls for the work that genuinely benefits from them.
Intelligent batching and caching ensure repeated and related operations are computed once and reused, cutting cost and latency without sacrificing accuracy.
HawkenAI built the observability, routing, and fallback infrastructure needed to run these systems reliably, with the tracing and control production operations demand.
Hands-on engineering leadership across the platform's core patent intelligence workflows, from prior art discovery to claim charting and portfolio analysis.
The workflows run on multiple frontier models through HawkenAI's enterprise-grade LLM gateway, with the routing, caching, tracing, and fallback logic to use the right model for each task while keeping cost, latency, and reliability under control.
HawkenAI's gateway provides routing, caching, tracing, and fallback logic so the platform uses the right model for each patent intelligence task.
Workflows run on Claude from Anthropic and models from OpenAI, with flexibility to route across models as needs change.
Building on Claude gave these workflows the reasoning quality that patent intelligence demands, without sacrificing speed or cost control.
Multi-model routing keeps cost, latency, and reliability under control across claim charting, prior art discovery, and portfolio analysis.
The impact on cost and speed was immediate and substantial. Beyond the numbers, the systems became production-grade, with better tracing, routing, and fallback logic that made workflows more reliable and far easier to operate and improve.
The company gained AI patent intelligence that runs fast, costs a fraction of what it did, and holds to the accuracy its high-stakes work requires.
HawkenAI cut LLM costs by more than 60 percent and reduced latency by nearly 90 percent by replacing brute-force model calls with a deterministic processing layer.
An advanced caching layer eliminated repeated model calls for frequently repeated operations, cutting their latency by up to 90 percent with filter and date accuracy fully preserved.
The company holds its AI to the same standard as its experts, because clients make high-stakes decisions on what the platform delivers.
“We hold our AI to the same standard as our experts, because our clients make high-stakes decisions on what we deliver. HawkenAI led our AI engineering and took these systems from promising to production-grade. They cut our costs and latency dramatically while preserving the accuracy our work demands, and built the reliability we need to run this at scale. They operated as a true extension of our team.”
HawkenAI is an engineering and applied AI consultancy that builds and leads production AI systems for growth-stage companies and enterprises. HawkenAI partners with teams to design, build, and ship AI that creates measurable business value, with a focus on real outcomes, efficiency, and reliability at scale.
From core intelligence layers to customer-facing AI applications, HawkenAI ships products that move from prototype to operational use.
The focus is measurable leverage: faster decisions, lower operational drag, and AI that fits the business instead of adding complexity around it.
If you’re ready to accelerate your roadmap, outpace competitors, and get to market faster than ever, let’s talk.
HawkenAI customer story about leading AI engineering for a leading patent intelligence platform, cutting LLM costs and latency while preserving accuracy for high-stakes legal work.
Our client is one of the leading AI platforms for patent intelligence, trusted by top law firms, enterprises, and litigation funders to power high-stakes IP litigation and monetization work. As the company brought AI deeper into its patent workflows, it needed that AI to perform to the same standard as its human experts: rigorous, fast, and reliable enough for high-stakes legal matters.
HawkenAI led that effort as the company's AI engineering team.
Patent work leaves no room for sloppy output. A claim chart, a prior art result, or a portfolio analysis feeds directly into litigation and licensing decisions where accuracy and defensibility matter. Bringing large language models into these workflows raised a hard set of engineering problems at once. The AI had to be accurate enough to trust, fast enough to keep pace with real deadlines, affordable enough to run at scale, and stable enough to operate as production infrastructure rather than an experiment.
Early implementations pushed heavy volumes of work through language models in ways that were slow and expensive. The cost of running these workflows and the latency users experienced both needed to come down sharply, without giving up any quality. The company needed an AI engineering leader who could take these systems from promising to production-grade.
HawkenAI led the company's AI engineering, owning the architecture and execution that turned LLM-powered patent workflows into efficient, reliable production systems.
The team re-engineered how these workflows use language models. Instead of sending every operation to an LLM by brute force, HawkenAI introduced deterministic processing layers that handle what does not require a model, reserving model calls for the work that genuinely benefits from them. It added intelligent batching and caching so repeated and related operations are computed once and reused. And it built the observability, routing, and fallback infrastructure needed to run these systems reliably at scale, with the tracing and control that production operations demand.
This was hands-on engineering leadership across the platform's core patent intelligence workflows, including claim charting, prior art discovery, and portfolio analysis.
The workflows run on multiple frontier models, including Claude from Anthropic and models from OpenAI, accessed through HawkenAI's enterprise-grade LLM gateway. The gateway provides the routing, caching, tracing, and fallback logic that let the platform use the right model for each task while keeping cost, latency, and reliability under control.
Building on Claude gave these workflows the reasoning quality that patent intelligence demands, with the flexibility to route across models as needs change.
The impact on cost and speed was immediate and substantial. On a core prior art workflow, HawkenAI cut LLM costs by more than 60 percent and reduced latency by nearly 90 percent, by replacing brute-force model calls with a deterministic processing layer. On another workflow, intelligent batching and caching reduced both cost and latency by more than 40 percent, with filter and date accuracy fully preserved. An advanced caching layer eliminated repeated model calls entirely for frequently repeated operations, cutting their latency by up to 90 percent.
Beyond the numbers, the systems became production-grade. Better tracing, routing, and fallback logic made the workflows more reliable and far easier to operate and improve. The company gained AI patent intelligence that runs fast, costs a fraction of what it did, and holds to the accuracy its work requires.
“We hold our AI to the same standard as our experts, because our clients make high-stakes decisions on what we deliver. HawkenAI led our AI engineering and took these systems from promising to production-grade. They cut our costs and latency dramatically while preserving the accuracy our work demands, and built the reliability we need to run this at scale. They operated as a true extension of our team.”
HawkenAI is an engineering and applied AI consultancy that builds and leads production AI systems for growth-stage companies and enterprises. HawkenAI partners with teams to design, build, and ship AI that creates measurable business value, with a focus on real outcomes, efficiency, and reliability at scale. Learn more at hawken.ai.
