Independent product consulting
AI products from pilot to production.
ScottByte helps B2B SaaS teams close the gap between a working AI demo and a shipped product. Fifteen years of senior product leadership building data and ML platforms at scale.
Is this for you?
B2B SaaS teams with an AI initiative stuck between demo and production. Typically 5–50 engineers. Product and engineering aligned on the opportunity, but unclear on the path to shipping. If that sounds like your situation, the next section will feel familiar.
What ScottByte does
Where ScottByte adds value.
Audit
Diagnosing why an AI initiative is stuck. Usually it is not the model. It is the data pipeline, the evaluation framework, the integration scope, or the team alignment. ScottByte finds the actual blockers and names which ones matter most.
Strategy
Defining the data model, the quality framework, and the production readiness criteria the team needs but does not have. Precision and recall thresholds. Evaluation datasets. A roadmap that does not collapse under contact with production.
Execution
Embedded work with engineering and data science teams to get the first AI feature into production within three to six months. Building internal capability so the team can repeat it without external help.
How this works
How engagements work.
- Stage 1
Diagnostic Day
One day. Paid. Either side can stop here.
A focused day with your team to find what is actually broken. Walk through the project, the data, the people, and the tooling. By the end of the day, the blockers are named in concrete terms and the next step is defined. You receive a written diagnosis you can use whether or not the engagement continues.
- Stage 2
Pilot Engagement
One to two weeks. Scoped fix.
Address the most critical blocker identified in the diagnostic. Defined deliverable, defined timeline, defined exit. The pilot produces a concrete result, not a recommendation. Either side can stop after the pilot.
- Stage 3
Full Engagement
Three to six months. Embedded.
ScottByte works embedded with your engineering and data science teams to ship the AI feature into production. Building internal capability so the team can repeat it without external help. Clear exit criteria from day one.
Selected work
Where this work has been done.
At Zalando, six years building the pricing platform that runs across 17 European markets and tens of millions of SKUs. The system evolved from governance tooling into a goal-based AI optimization engine used daily by 20 to 40 commercial stakeholders. Forecast accuracy 98% at two weeks, 85% at two months.
At Booking.com, the first data science PM team. Voice search shipped in four months with measurable booking uplift. Search personalization at scale serving 80% of traffic from a cached ML layer. Two research papers published at WWW 2022 and SIGIR 2020.
About
ScottByte is the independent product consulting practice based between Berlin and Tel Aviv, working remotely with B2B teams across DACH, Israel, and beyond.
Fifteen years across Zalando, Booking.com, and an Israeli adtech scaleup. Imperial College computer science. Two published ML research papers. Long-form writing on product management at the intersection of data, AI, and B2B SaaS at undsonntag.substack.com.
FAQ
Five questions worth asking before we work together.
01How do you actually work inside our team?
Embedded. In your standups, in your repo, in the conversations where decisions get made. ScottByte does not deliver slide decks and walk away. The product manager joins your team for the duration of the engagement, with the access and accountability of a senior internal hire. The difference is the time horizon and the focus. This means we work on your tools, in your codebase context, with your data. Pull requests, design reviews, sprint planning. The work is operational, not advisory.
02What if the diagnostic shows we need to kill the project?
Then we tell you. The diagnostic exists to surface the truth, including the version of the truth that nobody on the inside wants to say out loud. Sometimes the right answer is to ship a smaller version of the project. Sometimes it is to pause and rebuild the foundation. Occasionally it is to stop entirely and redirect the budget. ScottByte makes the recommendation that the data supports, not the one that extends the engagement. This is also why the diagnostic is a paid standalone deliverable. You own the conclusions whether or not you continue working with us.
03How do you scope work when we ourselves don't fully know what's wrong?
That is the most common starting point, not the exception. Most teams that engage ScottByte know something is stuck but cannot precisely name where. The diagnostic day exists for this. We walk through the project, the data, the team dynamics, and the tooling. By the end of the day, the problem is named in concrete terms and the next step is defined. You should not need a perfect brief to start the conversation. You should need a real problem.
04What stays with your team after the engagement ends?
The shipped feature is the obvious deliverable. The bigger one is the operating capability: how your team sets quality criteria for AI features, how they evaluate model performance against business outcomes, how they decide what to ship and what to kill. Every engagement includes documentation of the decisions made and the reasoning behind them. The goal is that your internal team can repeat the work on the next AI feature without external help. If you find yourself needing to re-engage ScottByte to ship the next thing, we have not done our job.
05How is this different from hiring another consulting firm?
Most consulting firms sell strategy. ScottByte ships product. The work happens in the repo, not in PowerPoint. The other difference is scope. A traditional firm wants the longest engagement they can sell. ScottByte's offer is structured so you can stop after any stage, including the first day. The diagnostic day is paid but standalone. The pilot is one to two weeks with a defined deliverable. The full engagement is three to six months with clear exit criteria. At every stage, either side can decide not to continue. This works because the work is operator-level: each stage produces something concrete enough that the value is visible before the next commitment.