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.
What Mosaic Does
Seven capabilities. One AI layer across the platform.
Every design decision in Mosaic follows from the same assumption: if evidence is organised, it will be used. Mosaic structures, connects, and surfaces evidence at the moments it matters.
Hypothesis Grader
Turns rough ideas into structured, testable hypotheses. Scores clarity, measurability, and specificity before your team commits resources to building anything.
Collision Detection
Keep your strategic goals and planning in one place to improve employee engagementChecks every new idea against your existing backlog and test history. Flags overlaps, contradictions, and ideas that have already been tested with different wording.
Research Synthesis
Connects findings across research sources. Identifies patterns, contradictions, and gaps that individual reports miss. AI-drafted synthesis with human verification.
Quality scores
Scores the rigour of every experiment, hypothesis, and result across your programme. Surfaces governance gaps before they become trust problems. Tracks quality trends over time so you can see whether your evidence is getting stronger or weaker.
Insight Synthesis
Synthesises results from completed experiments into reusable insights. Captures what was learned, not just what was measured. Builds institutional memory that persists.
Mosaic Search
Ask questions of your experiment history and research archive , program and insights in plain language. "What do we know about checkout friction?" returns evidence, not documents.
STANDALONE TOOL
Mosaic Companion
Joins your meetings as a silent participant and extracts testable experiment ideas in real time. Works with Google Meet, Teams, and Zoom. No Efestra account needed. Ideas can be exported standalone or imported into your Efestra backlog for evaluation.
£8.99 a month for live sessions
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.
