Figuring out where AI fits
We start from how the work actually happens today: where AI genuinely helps, where it doesn't, and which assumptions need evidence before anything bigger gets built.
See where engagements start
wvlen
pronounced “wavelength”
AI consulting and coaching that starts with your work.
I'm Dan Corin, a software and AI engineer. I help teams figure out where AI fits into their work, validate what's worth building, and ship.
I work with startups, research teams, industry, and mission-driven organizations before the plan is obvious, pairing domain expertise with software, ML, and production engineering.
Get in touchScrollWorking together
Most experts are underserved by software. Existing tools are generic, cumbersome, or simply don't exist for the work you do.
When you know your domain deeply, two paths open up that weren't viable until recently: build the tool you actually need, or reach for technologies like databases, pipelines, models, and infrastructure that used to take years of expertise just to implement the basics. Coding agents have collapsed the prerequisites. Both paths are unlocked by the same thing: clear vision of the work, paired with knowing what today's technology can actually do.
I embed with your team and help solve your actual problems. The work starts with figuring out where AI fits into your work and what's worth doing, then moves into prototypes that test the riskiest assumptions cheaply, and into scoping and shipping.
You don't need a plan to start. Most of the value comes from the conversation before the plan exists.
How I help
The work takes a few shapes, and most engagements mix more than one.
Prototyping and validation
Build the smallest version of an idea that can teach us something, run it against your real work and data, and find out what's worth keeping before committing to a roadmap.
See what validation looks likeProduction software and ML engineering
When something proves useful, I help scope and ship it: the software, data, and evals it takes to run reliably in production.
See shipped workAI upskilling
I teach teams how language models and agents actually work, from the ground up. Not prompt recipes, but the mental models that let people reason about whatever AI throws at them next.
More on AI upskillingHow an engagement works
Step 1
Start with the work
We start with a conversation about your actual work and constraints, not a tool or model someone has already decided on.
Step 2
Test the riskiest assumptions
We build prototypes that test the riskiest assumptions cheaply, against real work and real data, and figure out what useful actually looks like.
Step 3
Ship and hand off
When the evidence is there, we scope it and ship it, and your team ends up with the context to own and extend it.
Testimonials
In just a few hours, Dan completely changed how I work with AI. I went from only trusting the technology for trivial tasks to running many parallel agents at once, relying on it for all of my day to day coding work. He taught me how to plan with, provide effective feedback for and invest in my agent.
I worked with Dan at Block, where we co-hosted AI office hours for the company. Before those sessions, I had no real path into agentic coding -- it felt out of reach. Dan taught me one-on-one how to set up and actually use the tools, and now it's part of how I work every day. What stuck with me most, though, was watching him do the same for designer after designer. He had a gift for unlocking creativity and potential in people across the whole company, patiently and without ego. He elevated our entire design team through his mentorship, and that impact has far outlasted any single session. Anyone who gets to work with Dan is lucky.
[Dan will] perform magic in front of you and then show you how to do it. He's a fantastic teacher and will meet you wherever you are at. If you have any interest in improving you or your team's ability to leverage AI in a meaningful way, he's the coach you want. Not just a demo, or the latest hot new skill, but solving whatever weird problems you have (where being right matters). If you want confidence and reliability in the tools you are building, he will get you there. And he will leave you with the ability and wisdom to keep building. You will not regret working with him. I promise.
Dan came into the project without deep content expertise, and he worked hard to understand the full scope and nature of the problem we were trying to solve. His strength is finding responsive, creative technical solutions using software development, machine learning, and AI, and he helped us create a workflow to conduct analysis at scale in a highly secure environment, something that simply was not possible before in our work. He worked with our non-technical team collaboratively, with a great deal of patience and curiosity, and helped us understand our own research questions in a new way.
I have found tremendous educational value in working with Dan on several projects across disciplines. He models a unique ability to elicit, frame, and target the core features of any problem. Dan taught me to build such a skillset and use it, with the help of AI tools, to find elegant and rigorously validated solutions using powerful computational approaches outside my domain of expertise.
Background
I've spent my career as a software engineer building, shipping, and maintaining systems at scale.
My background is in software and product, but my favorite thing to do is work with people from different backgrounds and industries, learn their challenges, and help them get computers to work for their needs. I've worked with CTOs, CIOs, designers, doctors, and PhDs in a wide range of industries.
Engineering experience
Productionized AI workflows for disputes intelligence and restaurant menu extraction, built internal LLM tooling, and coached teams on coding-agent workflows.
Built micromobility systems at Uber app scale, including search, aggregation, IoT firmware rollouts, payments, and fleet-management services.
Built member-support systems across chatbot, voice, and email workflows, with a focus on reliability, performance, and platform architecture.
Built prototypes that brought the best of open source to enterprise, turning emerging tools and ideas into proofs-of-concept that clients could run with.
Selected work
A few examples of what this looks like in practice: domain knowledge turned into analysis, shipped software, and new ways of working.
Cancer research analysis
Letting the biology define the statistical methods, then using coding agents to move quickly from scientific questions to working analysis.
Read the case studyA backtesting framework
A hedge fund trader’s concept turned into a shipped, extensible first version: a structured conversation, then a spec, then implementation.
Read the case studyCoding-agent adoption
Pairing with a classically trained CTO on their real work: planning with an agent, implementing, and reviewing a PR together.
Read the case studyFrequently asked questions
- What does an AI consultant do?
- For me, it means helping a team figure out where AI genuinely helps, testing those ideas against real work, and shipping the ones that hold up. In practice that mixes product judgment, software and ML engineering, and working directly with the people who know the domain.
- What kinds of teams does wvlen work with?
- Startups, research teams, established companies, and mission-driven organizations. The common thread is a hard, domain-specific problem that generic software doesn't solve well.
- Do we need an AI strategy or project plan before getting in touch?
- No. You don't need a plan to start. Most of the value comes from the conversation before the plan exists: we look at the work, find what's promising, and decide what's worth testing before anyone commits to a larger build.
- Can an engagement include both strategy and implementation?
- Yes. Some engagements run the whole path, from figuring out where AI fits through prototypes and into production. Others focus on one piece. The shape follows what your team actually needs.
- Do you work with non-technical domain experts?
- Often, yes. Domain experts usually hold the most important context for whether an AI system will work. I've worked with designers, doctors, traders, researchers, and operations professionals, turning what they know into prototypes, software, and workflows they keep running themselves.
If you're interested in working together, please reach out.