Private AI for R&D documentation

Two reports worth revisiting

Suppose the archive contains two reports about the material. The first describes an earlier revision tested at an elevated temperature. The second covers a revised material, but the trial took place at room temperature. The engineer wants to know which earlier work will help the discussion about the new housing.

An assistant could point to both reports, keeping the material revision and stated conditions attached to each. Its answer should make clear that the elevated-temperature trial concerns the earlier revision. The report found for the revised material describes room-temperature conditions. The engineer opens the sources, reads the details, and brings documents covering two different situations to the meeting.

The team can then begin with a specific question: is there another report covering the revised material under the conditions of interest? They no longer have to rely entirely on what the person who led the previous project remembers. If that report was not found in the searched collection, the answer should leave the gap visible. A search result does not establish that the trial never happened.

Combining the reports into “the material was tested at an elevated temperature” would hide information the team needs. The comparison should make earlier research easier to read. The research team still assesses its relevance to the new project; finding the documents does not provide a basis for recommending the material.

Start with what the team can already find

If reports have consistent names and revision labels, filters in the existing repository may be enough. An engineer who remembers the project and author can narrow the collection without an additional assistant. That provides a useful comparison: how much work remains after finding the documents?

Benchling's search tools illustrate this option. Its documentation describes project, tag, author, and date filters, with results limited to objects the user can access. This is an option for teams permitted to use that environment. Benchling is a cloud platform and does not support installation on local servers.

An assistant becomes worth considering when finding the files still leaves a substantial reading task. The material revision may be buried in the text, conditions may sit in an attachment, and titles may offer little guidance. The assistant could prepare an initial comparison with references to the relevant passages. Its value lies in helping the engineer reach the differences the team needs to discuss.

What private processing means for this company

Before sharing reports with a proposed application, the company needs to agree on where they can be processed. It may permit a particular provider's service or require processing on its own server. “Private AI” in a proposal should lead to a concrete answer: where does the report's content go when the engineer asks a question?

A local model runs on a designated company computer or server. Ollama's documentation distinguishes local models from cloud-hosted models and describes the option to disable cloud features. Choosing the product therefore does not, by itself, establish where a task runs. The proposal needs to explain how the application will use it.

A copy of report content may remain in text stored for search. A passage may also appear in a saved conversation where the engineer compared the trials. Both locations, along with their backups, should fall within the agreed rules for storing research records. The company also needs to know who can read that material, including a supplier supporting the application.

Access to one project should not expose another project's research through an assistant's summary. If the engineer in this example can read only the first report, the application needs to respect that scope in its answer too. Source permissions continue to matter after a search system has been added.

A first scope the team can assess

A selected part of the archive and a question like the housing-material example provide a useful starting point. Someone familiar with the project can judge whether the assistant found the right reports and preserved the differences between trials. The references need to help them reach the relevant details. If reading the answer means starting the archive search over again, the assistance has added little.

Compare this work with the existing search tools. Better revision labels may remove much of the difficulty in one collection. Another may benefit from an application that brings scattered descriptions together. Our article on buying local AI with a trial on your own documents covers the broader comparison process.

Syntalith can help build a custom AI application for documents that finds earlier research, compares the recorded conditions, and lets the reader open the reports. The proposed starting point is a limited set of reports in an environment approved by the company. That work can help establish whether to develop the assistant further and which parts of the archive to include next. Pricing information is available on our pricing page.

For the first conversation, describe something the team struggled to find before a project meeting, where the reports are stored, and which environments may process them.