Applied AI for B2B SaaS & data-rich products
Turn AI ideas intoshipped product.
Conifer Labs partners with founders and product teams to design, build, and launch production AI features—without the hand-wavy hype. From workflow agents to custom models, the focus is on shipped outcomes, not slide decks.
Previously: Microsoft, NBC, General Motors, Department of Energy, Vibrant Planet, and multiple venture-backed startups.
What we typically help with
- • Designing AI features that users actually adopt.
- • Going from prototype notebook to production system.
- • Choosing the right models, architecture, and stack.
- • Reducing inference cost and improving reliability.
- • Building the first version of an AI team and roadmap.
Every engagement is led by Kevin Rohling — founding engineer / CTO, applied AI practitioner, and researcher at UT Austin.
Productized ways to work together
Clear starting points, defined outcomes, and transparent expectations—so you know exactly what you are buying and when it will ship.
AI Discovery & Strategy Sprint
2–3 week engagement to clarify where AI will actually move the needle in your product and business.
90-Day AI Product Launch
Design, build, and ship a production-quality AI feature or internal tool in ~90 days.
Custom Agent & Model Engineering
Hands-on design and implementation of LLM agents, retrieval systems, and custom-trained models.
Fractional AI CTO / Advisor
Ongoing strategic and technical partnership for founders and product teams navigating AI.
Why Conifer Labs
A lot of AI consulting looks impressive in a deck but never quite lands in production. Conifer Labs is designed for founders and product leaders who care most about what ships, not how fancy the slideware looks.
Deep technical rigor, product-first lens
Experience as founding engineer and CTO across multiple startups plus hands-on work with deep learning, LLMs, computer vision, and agents. The result is solutions that are technically sound and actually usable.
From research to production
Active research at UT Austin keeps us close to the frontier, but the bar is always: does this make the product better, faster, cheaper, or safer for your users?
Flexible, senior-only engagements
No bench to keep busy. You work directly with Kevin, with a network of trusted collaborators brought in only when they add clear value.
Clear communication & shared language
Expect candid guidance, written decision memos when needed, and updates that align engineering, product, and leadership on the same page.
Selected outcomes
The specifics vary by client, but the pattern is consistent: focused scope, measurable impact, and production deployments that stick.
B2B SaaS · Series B
Automated onboarding workflows for a data platform.
Designed and shipped an internal agent that orchestrates multi-step customer onboarding across 4 systems.
→ 60% reduction in manual onboarding time, faster time-to- value for new customers.
Industrial analytics · Growth stage
Forecasting & scheduling for field operations.
Built a custom sequence model and decision pipeline to prioritize technician visits and inventory placement.
→ 15–20% improvement in on-time SLAs and lower truck rolls.
Climate / geospatial
Prototyping next-gen decision support tools.
Helped a climate-tech startup evaluate and integrate foundation models for mapping, planning, and scenario analysis.
→ Clear build-vs-buy decisions and a roadmap the team could execute.
How engagements typically work
The details are tailored to your context, but most projects follow a simple pattern designed to reduce risk and keep everyone aligned.
Step 1 · Discovery
Clarify the problem & constraints.
Short working sessions with product, engineering, and go-to-market to understand users, data, systems, and constraints. Output: a concise opportunity brief.
Step 2 · Design & prototype
Design something worth shipping.
Model selection, architecture, UX flows, and evaluation plan. Often includes a targeted prototype to validate feasibility before full build.
Step 3 · Build, integrate, iterate
Ship, measure, and refine.
Implementation in your stack, hand-in-hand with your team. Clear handoff docs and metrics so you can operate and extend the system.
FAQs
A few of the questions that come up most often. If you do not see yours here, feel free to ask it directly.
- Who is Conifer Labs a good fit for?
- Primarily B2B SaaS and data-rich companies that have clear customers and revenue, but need a senior partner to turn AI ideas into shipped product without hiring a full in-house team.
- What does a typical engagement look like?
- Most projects start with a short discovery sprint to clarify opportunities, shape the roadmap, and de-risk the work. From there we either run a 60–90 day build or set up an ongoing advisory / implementation retainer.
- Do you only work with greenfield AI projects?
- No. A lot of work is helping teams level up existing prototypes: hardening them for production, improving reliability, reducing cost, and integrating them cleanly into the rest of the product.
- How soon can we start?
- Availability fluctuates, but there is usually room to kick off a discovery sprint within 2–4 weeks. If you have a tight deadline, reach out and we can talk about options.
Ready to talk about a specific project or idea? A short working session is often the fastest way to see if there is a fit.
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