MOSAIC - Efestra's Experimentation & Research AI
AI that improves your evidence. Not AI that replaces your judgement.
Organisations generate evidence through experiments and research. Most of it never reaches the decisions it should inform.
Mosaic is built for both problems -- making every piece of evidence more rigorous, more connected, and harder to ignore.
Two problems. One gap.
Most evidence never reaches the decisions it was built to inform.
Streamlined steps to navigate and discover your perfect insurance plan effortlessly.
Testing platforms are racing to add agents that plan experiments, build them, QA them, and push recommendations automatically. The pitch is speed and volume. Do more, faster, with less human involvement.
Research repository tools are competing on findability. Store your studies, tag your insights, search your archive. The assumption is that if research is organised, it will be used.
Neither of these is the constraint. The organisations that come to Efestra are not short of experiments, and they are not short of research. They are short of evidence worth acting on, synthesis that connects what has been learned across teams and time, and a reliable path from evidence to the decisions it should inform.
Adding AI to an already-broken evidence infrastructure makes it break faster. Mosaic is built on a different assumption.
Where Mosaic Works
Embedded across experiments, research, and programme governance
Mosaic is not a feature you switch on. It is part of the infrastructure, active at the moments where evidence quality is determined -- whether that evidence comes from an experiment or a research study.
Launchpad is the AI driven experiment planner for teams starting out.
What Goes In
- A rough idea in plain language
- The teams existing experiment history
- What tests are currently running
What Comes Out
- A structured experiment plan
- Collision and duplication alerts across all experiments
- A recorded decision, whatever the PM chooses
What Goes In
- Multiple studies across teams and time
- Business questions from decision owners
- Raw findings. transcripts and reports
What Comes Out
- Synthesis draft in minutes not days
- Contradictions and patterns flagged
- Findings connected to the decisions they were built for
What Goes In
- Experiments and results across the programme
- Evidence records and decision logs
- Activity patterns over time
What Comes Out
- Experiments completed without a decision surfaced
- Governance failures distinguished from individual mistakes
- Signals directed at the people who can act on them
Principles
Built on four commitments that are easy to say and hard to implement.
Every design decision in Mosaic follows from these. This is not marketing language. They are the constraints we gave ourselves.
01
Embedded, not bolted on
Mosaic intervenes at the moment a decision is being made, not in a separate panel waiting to be consulted. It is part of the workflow, not adjacent to it. AI that sits outside the process gets ignored at the moments that matter.
02
Structured, not generative
Mosaic improves the quality of what a human is already doing. The PM writes the idea. The researcher commissions the study. Mosaic makes both better. It does not generate work on anyone's behalf and does not introduce content that has not been initiated by a person.
03
Accountable, not invisible
Every Mosaic suggestion is transparent and reversible. Original inputs are preserved alongside improved outputs. Teams can see exactly what changed and why. When Mosaic flags a collision or a contradiction, it explains its reasoning.
04
Calibrated for governance, not activity
Mosaic does not optimise for volume. It does not reward teams for running more experiments or producing more research. It rewards evidence quality and decision influence. Those are different success metrics, and they produce different behaviour.
