Sometimes they become products. Sometimes operational systems. Sometimes entirely new businesses.
Over the last 20 years I've applied the same methodology across manufacturing, startups, AI, digital products and operations — turning ambiguity into systems that scale.
Two decades building for
A few things I took from a vague problem to a working product — deciding scope, orchestrating AI and teams, and getting them into real use.
An AI-orchestration tool that makes two models from different providers debate adversarially — one generates, one critiques — until the output converges. Idea, design, build and validation, solo.
A WhatsApp + Instagram + n8n CRM that turns inbound leads into quoted and scheduled inspections. I owned product behavior, safety boundaries and rollout while directing AI coding agents.
Designed and built a real-time operations platform with KPI dashboards and WIP tracking for a 45-person manufacturer. Live and in daily use for 3+ years.
As founder of the studio's software factory, I defined the products and directed the team that built them: a proprietary API + CRM platform and interactive systems for global brands.
An AI-orchestration tool that makes two models from different providers (Claude and GPT) argue in an adversarial loop: one generates, the other audits, and the system iterates until it converges on a refined result.
Working with several AIs daily means jumping between windows, copy-pasting output from one into another, with no structured way to make them contrast. Worse: each model has blind spots, and used in isolation they share them. A single model reviewing its own output isn't rigorous — quality jumps when one model audits another's work.
A working web app: a Node backend acting as orchestrator and secure proxy to both APIs (solving credential isolation and browser limits), and a web interface showing the dialogue in real time as a readable vertical timeline, round by round, with export of the final result.
Not a toy test. I used Agent Loop to diagnose a genuine production incident — a system sending repeated messages to a real phone number — that two AI assistants had failed to solve separately. Across several rounds of dialogue, combined with verification against the database, the system reached the actual root cause (contamination between test and production environments) and produced an actionable recovery plan.
A commercial-operations CRM for pre-purchase vehicle inspections — integrated with WhatsApp, Instagram and n8n — that turns inbound leads into quoted and scheduled inspections with far less manual work. Not "a chatbot": a sales-operations system.
The business receives leads from people who want a used car inspected before buying. It was mostly manual: answer each lead one by one, ask for vehicle data and location, price the inspection, explain the service, follow up and schedule. People asked the same questions over and over; pricing depends on vehicle type and location (so it can't be improvised by an AI); leads arrived scattered across WhatsApp and Instagram; and there was no reliable way to know where each conversation stood.
A centralized inbox with conversation history; lead state (qualifying, quoted, scheduling, booking requested, human review); vehicle candidates; deterministic pricing; WhatsApp Flow forms; automated replies; booking-request handling; human-review flags; and the foundation for future buyer/seller matching.
Estimated 60–80% reduction in first-response and qualification workload for standard leads. Most CABA/GBA cases can be quoted automatically once vehicle and location are known. Scattered WhatsApp/Instagram chats are centralized into one CRM with structured lead states, making testing and scaling safer because pricing and state are deterministic — not improvised by AI. A customer can now arrive by WhatsApp, ask a question, get a natural answer, complete vehicle and location forms, receive a correct price, request a slot and generate a booking — while the CRM keeps full state and knows when a human must step in.
I'm Lara Dittmar. For twenty years I've done one thing: turn vague ideas into real, working things. It started in industrial design and brand experiences for Nike, Netflix and Pepsi; it grew into running a studio with its own software factory, and into scaling a manufacturing operation 10× on a digital platform I built and still runs today.
Today I lead product and orchestrate AI to build and ship. I'm not an engineer competing on code — I'm the person who decides what's worth building, directs the AI and the people who build it, and has the design taste to make it good. In 2026, writing code is no longer the scarce skill. Knowing what to build, and getting it shipped, is.
Discovery, ruthless scoping, and framing decisions in metrics and outcomes.
Building apps and automations with AI and no-code — directing models, not hand-writing code.
Twenty years of craft and judgment. Figma, prototyping, friction-based iteration.
Founder and operator. Directing technical teams and shipping under real pressure.
Open to product roles — AI / Technical PM, Product Lead — remote or relocation. If you need someone who decides well and ships, let's talk.