PROOF POINT · JITTERBIT
Unlocking a legacy platform: migrations from weeks to hours.
Jitterbit’s legacy-platform customers were locked out of its modern cloud platform. A costly migration project had already failed. We built—then licensed—an AI-based converter that made the migration routine.
The problem
A large base of customers was running on Jitterbit’s legacy platform and could not move to the modern cloud product. Every migration was a bespoke project—slow, expensive, and error-prone—and a serious attempt to industrialise it had already failed.
The consequence was strategic, not just technical. As long as customers stayed on the legacy platform, growth on the new one was constrained. Years had passed in that state.
The starting material was thin: a handful of sample legacy project files, hand-built target outputs, and no reliable specification of either format. The input was only partly understood; the target was understood only through what the new platform would accept.
The intervention
The breakthrough was not a better converter. It was a different question. Instead of hunting for a transformation from input to output, we set out to understand each element of the legacy format so completely that the conversion was forced rather than guessed. That method became our EuclidAI investigation platform.
Each structure in the legacy format was classified, broken down until irreducible, interpreted, and then validated by injecting the interpretation into the real platform—which accepted or rejected it.
Every validated element entered a permanent rule library. Known patterns were retrieved rather than relearned, so each new file took a fraction of the time of the last.
The AI works offline, inside explicit constraints, with an embedded audit trail. It never certifies its own output.
What runs in production is deterministic converter tooling that applies the proven rules. Unknown patterns loop back to investigation instead of being guessed at.
The outcome
- Migration went from several weeks to several hours
- The conversion runs as verified, deterministic tooling rather than as a bespoke project
- A growth constraint that had stood for years was removed
- IOIntegrated built, then licensed, the converter to Jitterbit—and the partnership was announced publicly
The migration had already been attempted and had failed. The unlock was not a better converter—it was understanding each element of the legacy format until the conversion was forced, not guessed.
The second engagement: enabling the Professional Services team
Once the converter was in place, Jitterbit asked for something different: help its Professional Services pre-sales team work with AI themselves. We designed and ran a three-session AI enablement programme for 20 to 25 people, two hours each, starting from a readiness assessment of the team and moving from fluency, to building live demonstrations, to a repeatable framework for identifying and positioning AI integration opportunities with customers. Each session was co-facilitated with the team’s manager so the practice would outlive the programme.
- Readiness assessment before session one
- Three two-hour sessions, hands-on rather than lecture
- Fluency, then build, then apply to the team’s own sales conversations
- Co-facilitated with the manager; no dependency on us afterwards
One participant described their own work as ten times faster after the programme.
Why it matters beyond Jitterbit
Any organisation with a format, a configuration, or a system that nobody fully understands faces the same shape of problem. The method that solved this one—classify, decompose, interpret, validate externally, store—applies wherever the unknown is the real obstacle and the data is proprietary. That is where we do our best work.
Is there a migration that has already failed once?
That is usually the sign that the problem was framed as a transformation when it was really a question of understanding. Bring it to us.