Resume Sizzler: AI Labor That Parses, Matches, and Acts with Context
Parsing, Matching, and Acting with Context: A Foundation for the Future of AI Labor
Unlocking New Possibilities with Multi-Modal AI
The Resume Sizzler, developed by P2 Labs, is more than a resume builder—it’s a glimpse into the future of applied intelligence. A few years ago, extracting meaningful insights from unstructured data at scale was nearly impossible. Built on advanced multi-modal AI, this system transforms unstructured data into actionable insights, enabling businesses to automate complex workflows, extract meaning from documents, and make smarter decisions faster.
Built for More Than Resumes
The Resume Sizzler began as a proof of concept for automating resume parsing and job matching, but its real value lies in the underlying technology. With LLMs and advanced workflows, it can tackle a range of document-driven challenges:
- Classifying and structuring data from diverse sources
- Extracting key information from natural language queries
- Enabling dynamic, context-aware automation
The integrated architecture delivers a responsive, state-aware interface. Users can pick up where they left off, with the system persisting and managing data across sessions. LangGraph powers shared state management across nodes, allowing real-time modifications and fluid user experiences.
Reimagining What AI Can Do
The Resume Sizzler demonstrates how cutting-edge AI can handle the complexity of real-world data. Tasks that were previously manual, error-prone, or impractical at scale are now automated with precision. Whether parsing resumes, analyzing contracts, or managing workflows, this technology showcases the broader potential of AI to make sense of complex, unstructured information.
Read through the use cases below to explore the full capabilities and future applications of this advanced AI system.
OCR Anything (Resume Upload)
Multi-model LLMs are incredibly good at OCR and understanding unstructured data. Much better than traditional OCR and in most cases better than humans. This is especially true when using a council-of-agents architecture.
At this point it is safe to say that LLM based OCR should be the default methodology for document parsing and that it is cheaper and more reliable than any previous methodology including human data entry.
Parse Natural Language into Structured Forms (Form filling and structured data creation)
Turning natural language into structure is an extremely difficult computer science problem. A few years ago trying to take a document like a resume and using the information in it to fill out a structured form would have taken years of engineering effort. In the end those years of work would have resulted in a somewhat reliable system.
Now in a couple weeks using LLMs we can parse a document like a resume and with high accuracy fill out a form with that information. It’s a completely game changing capability turning natural language and unstructured information into consumable structured information.
This will have applications across most domains including:
- Legal Documents
- Insurance claim processing
- Simplified User Interfaces with natural language
Context Matching (Job Match/Position Match)
Matching context from two large natural language text blocks is a difficult challenge in traditional software engineering and even with many NLP tools. LLMs provide a significant improvement in our ability to match these large blocks of text for relevance.
By leveraging this capability we can accurately match an entire resume to an entire job listing in a way that was simply not possible with previous technology. Almost in a human-like fashion we can tune this matching feature to work effectively and at scale.
Engineering Accelerator (App Buildout)
Some LLMs are very good at directed code production. Code writing code has been a technique we’ve used for a long time and with LLMs we have a much more flexible toolset available to us. This gives us 2x to 10x acceleration on top of our 10x engineers.
LLMs also help us create realistic sample data, smarter testing, and in general save us time everywhere in the engineering cycle. This allows us to deliver value faster and, what’s even more exciting, solve much larger problem sets.
Language Translation (App Wide)
LLMs provide us with an unprecedented ability to work with software in our natural language, almost any language. The ability to translate language back and forth has been a dream since Star Trek TNG’s universal translator. Today we can implement a highly reliable version of that into our applications opening incredible access to users.
Relevant Content Creation (Resume, Cover Letter)
Unstructured to structured data is only one direction an LLM can empower. We can take structured data and produce incredible documents like in this case a resume and cover letter. LLMs can fill in the gaps and expand the language to generate great documents.
Result Grading (Graded Job Opportunity/Resume Match)
Grading the quality of matches based on sentiment, fully content, and a variety of other criteria is a great way to sort results by relevance. Before LLMs it would take large teams working in large organizations like Amazon making complex machine learning algorithms to get the same quality we can produce in weeks.
Multi-modal (voice, text, chatbot)
Being able to talk in natural language to software is an incredible advancement. We don’t mean one of those phone solutions with a set list of questions either, complete dynamic but directed conversations. What felt like Scifi 18 months ago can now be implemented in any application when it makes sense. This capability changes how we can engineer solutions and design user experience.
Deployment model
Most AI platforms encourage you to host agents with them in the cloud. Hosting agents this way will often provide some instrumentation for you to evaluate your agents. With sizzler, we run our graphs in our python backend directly, from a Modal deployment. For instrumentation, we use LangSmith to trace executions which allows us to monitor and evaluate how things are going basically in real time.
Graph functions and model (Graph based agents)
Langchain, the popular framework for developing applications powered by large language models (LLMs), put out a new library called LangGraph that has become the de facto framework for composing graph based agents. Loosely defined, an ‘agent’ is an LLM that has been given a task and one or more tools that it can use. The most common tool you’ll see an LLM use is a web browser but with the MCP protocol and other similar concepts we have a new way to give our agents a huge range of tools.
A graph based approach gives us a few benefits when composing an agent, especially for multi agent architectures. First of all, it provides a unified state management solution so that as your executions flow from one ‘node’ to the next, state is passed along and preserved. Second, when composing your graphs, you can elect to allow a given LLM ‘node’ the agency to decide control flow for itself. Rather than ‘hard coding’ steps that should happen in a particular order, a node with many connections to other nodes can decide for itself, based on that shared state, what information it has, what it needs, and which node should be invoked next.
Additional capabilities provided by LangGraph specifically are ‘human in the loop’ capabilities so that execution can be paused at any point, ‘time travel’, so that you can go to a previous point in the executions history and rerun the events from that point in time, and power IDE features with hot reloading to make the development process much faster than other offerings provide.
How AI and Multi-Modal Models Are Transforming Business Workflows
The Resume Sizzler is just the beginning. As organizations face growing volumes of unstructured data, tools like this redefine what’s possible with AI. With its multi-modal foundation and dynamic architecture, it opens the door to smarter systems, faster decisions, and scalable innovation—well beyond resumes.
The question isn’t whether AI can help your business. It’s how soon you’ll start using it to stay ahead.
AI is ready to assist. How will you put it to work?