I think using a weighted hierarchical regression model makes sense. I would look at outcomes aggregated up to the company level because that's ultimately the question: how can we make companies happier. If this were about employees, the study design would have obviously been tooled to follow employees prospectively and also to randomize the intervention within a particular company (so, say half the employees in company A get motivational interviewing and half do not). But that's not how it was done. It was a "community randomized trial". Average the Likert responses for each time period and company as an observation and use the inverse of the variance of responses as the weight. This gives a total of 4 observations. Don't worry about "sample size", since weights account for that.
In an ideal pre/post analysis you would use a model like the following:
\begin{equation}
\mathcal{E}[\mbox{Score | Time, Company}] = \beta_0 + \beta_1 \mbox{Time}
+ \beta_2 \mbox{Company} + \gamma \mbox{Company} \times \mbox{Time} \end{equation}
Where each of Time and Company are considered a binary variable: Baseline versus follow-up, Company A versus B. And it is the last coefficient $\gamma$ which measures the difference in differences from baseline to follow-up, accounting for differences between general company profile. The "excess" difference in time in Company A is what we call the intervention effect.
However, the issue of correlated data remains. The extent of correlation in baseline to follow-up is a function of how many people are in both samples. The more people in common, the more correlated the pre-post results are... usually. Uncontrolled correlation like this tends not to lead to biased betas, but incorrect standard errors. This tends to lead to inference that is conservative, but I cannot promise that this is the case.
This is simply a matter of you, the analyst, verifying as much as possible to convince readers about the generalizability of results. Was drop-out of baseline participants relatively equivocal between the two companies? Was the number of new-hires also equivocal in the follow-up? Among people whom individually you could verify pre-and-post follow-up, was the results stemming from a correlated data analysis consistent with the findings from the heirarchical one?
