Our approach
Real engagements. Real outcomes. Each client is different, but the pattern is the same: understand the problem, build the right response, and make the team more capable along the way.
Regular sessions — typically weekly, 45–90 minutes — with individuals or small teams. We pair on real work: strategy, blockers, prototypes, and how agents fit into all of it. Between sessions, async in Slack. No final deliverable — one concrete move forward every week. You get better at your job. Your agents get better at theirs.
We build tools for your specific context and integrate with the ones you already rely on — within our platform or alongside your team. Sometimes we build together. Sometimes we connect to your existing stack. Sometimes we build and hand over.
We facilitate innovation programs, design sprints, and discovery sprints of any format. Using our tools, skills, and agentic workflows, we help teams make better decisions, work with evidence, prototype faster — and have more fun doing it.
We begin with one or more conversations to understand where we can create value. No pitch decks. We listen, ask questions, and figure out together what makes sense.
When it fits, we set up a trial using your actual data — to show that our existing tools already work in your context. No hypothetical demos. Real results from day one.
We define success together. What does good look like? What are the numbers that matter? This gives both sides clarity before we commit.
Based on what we learned, we put together an offer that fits your situation.
Most of our clients work with us on a monthly basis. A regular budget that covers everything — personal coaching, consulting, tool access, and development. We are your partner for continuous innovation, not a vendor you call once.
For teams that want to start smaller: focused engagements to solve a concrete challenge. A sprint, a pilot, a proof of concept. Often the beginning of something longer.
Non-technical teams building production software
Challenge
100+ interviews per week, manual bottleneck, no engineering budget.
What we did
Set up our CLI and transcription service. Built custom development workflows. Coached the team on working with agentic tools.
Outcome
A non-technical ResOps researcher built a complete recruiting workflow app — from concept to working application. 100 interviews per week, automated.
Continuous customer understanding at scale
Challenge
Classifying thousands of monthly comments across app stores, support channels, and surveys.
What we did
Built a continuous customer understanding service. Connected app reviews, NPS surveys, support conversations, and interview data into a single analysis pipeline. Integrated into the client's existing platform.
Outcome
30,000 reviews processed monthly. 90% categorization precision. Product teams make evidence-based decisions at a pace that was previously impossible.
Validating a business model with real evidence
Challenge
Five acquired companies, five business models, unclear integration strategy.
What we did
Business model mapping across all five products. Extracted 27 hypotheses and tested them with real customers. Built prototypes. Coached on three levels: design partnerships, combined solutions, and individual products.
Outcome
Validated hypotheses before significant investment. Clear business model. Aligned leadership team.
From ICP discovery to continuous product iteration
Challenge
No structured discovery process, unclear ideal customer profile.
What we did
Ran a design innovation sprint for ICP definition. Then moved into continuous collaboration: prototyping support, continuous customer insights, and tight feedback cycles.
Outcome
Clear ICP. Tested prototypes. A continuous learning loop that feeds directly into product decisions.
From concept to validated product hypothesis
Challenge
AI dating app idea, but unclear which user segment has the biggest need and willingness to pay.
What we did
Discovery framework covering ICP, jobs-to-be-done, and segmentation. Developed attachment-style-based differentiation hypothesis. Designed user testing across 13 screens. Ran a structured design sprint for validation.
Outcome
Clearest segment identified with highest conversion probability. Testable prototypes. Data-based pivot decision prepared.
RAG-powered customer research synthesis across 30 parallel product teams
Challenge
30 parallel product innovation teams at DB — digital twin, long-distance travel, integrated rural mobility — each running their own research. Synthesis and cross-team alignment were the bottleneck: evidence for product pivots was hard to surface, patterns across teams stayed invisible, and research got redone because earlier data wasn't accessible.
What we did
Built a RAG system for analyzing and synthesizing interview data — upload interview notes, semantic analysis and chat over the corpus, audio transcription with chunking for 1:1 interviews, focus groups, and UX testing. Designed around a core insight: for inspiration and pattern-finding, access to data matters more than accuracy.
Outcome
Synthesis 10x faster. Senior executives engaged with customer data for the first time. Patterns became visible across the 30 parallel teams. Insights became searchable and reusable during product pivots — more confident decisions, better presentations, more fun engaging with customer data.
Agentic AI inside leadership coaching — accelerating feedback and building a self-improvement loop
Challenge
Coaching has a bandwidth problem. Sessions happen monthly, but leadership behavior plays out every day — in meetings the coach never attends, in Slack threads, in stakeholder dynamics. How do you give feedback on those moments, measure progress continuously, and let the coachee keep working on themselves between sessions?
What we did
Built an agentic AI coaching assistant alongside the 1:1 engagement. The assistant analyzed meeting recordings and Slack messages against a values-based scorecard (leadership, commitment, proactivity, focus, scope discipline, emotional regulation, strategic alignment), surfaced feedback from situations the coach wasn't part of, and functioned as a self-learning partner the coachee could work with directly. Progress became measurable across sessions.
Outcome
Faster behavioral development, visible to the team within six months. A co-developed, testable AI coaching platform that extends the reach of every coaching engagement — ready to scale beyond one coachee.
We take on a small number of clients at a time. If your product team faces a challenge where people and technology need to work better together — let us talk.
Talk to us