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Private AI for R&D documentation
Short answer
A private AI assistant finds earlier material trials in the R&D archive and shows each with its material version and test conditions. The engineer sees that the high-temperature trial covered an earlier version, while the changed version has only a room-temperature trial. At Syntalith an application on a local model costs from €3,000 net plus GPU hardware. At 200 questions about earlier tests a month of 30 minutes each, it pays back in 1.2 months.
How we workDetails ↓
- 01Call30 minutes with an engineer, free
- 02PrototypeA working system on your process and data, free
- 03ImplementationTarget and fixed price in the contract; 50% at signing
- 04Acceptance50% once the agreed target is met
- model on your server or in the cloud in the EU
- quoted after the review
- same work in application with EU cloud model
- from €3,000
- task, data and equipment analysis
- €0
We belong to the OpenAI and Anthropic partner programmes and learn through their official courses. What this means ↓
Author: Syntalith · 3 min read
Statistics
Research and AI: statistics 2026
1.2 mo.
payback of a €3,000 local application at 200 questions about earlier tests a month: €3,490 of work today, €1,087 with checking and the GPU server. Scenario.
49%
According to Anthropic (2024), combining contextual embeddings with BM25 search cut failed retrievals by 49%, and by 67% with reranking.
25.1%
According to a Harvard Business School study (2023) of 758 consultants, tasks within the model's capabilities were done 25.1% faster with AI.
48.8%
According to Eurostat (2025), 48.8% of EU enterprises that considered AI but did not use it named concerns about data protection and privacy.
20.0%
According to Eurostat (2025), 20.0% of EU enterprises with at least 10 employees used AI.
Figures checked: 22 October 2026
Two reports worth going back to
An engineer is preparing a discussion about a housing material and wants to check whether the team tested it at high temperature. They remember the project, but not the name of the report. The archive holds two reports. The first covers an earlier version of the material and a trial at high temperature. The second covers the changed version and a trial at room temperature.
The assistant points to both reports and keeps the material version and the conditions beside each. The answer is clear: a high-temperature trial was found for the earlier version, and none for the changed one. The team starts the discussion with a specific question: is there a report for the changed version under those conditions? A general sentence such as „the material was tested at high temperature” would hide the information the decision needs.
- 01Questionthe material and the test conditions
- 02Reportsearlier trials with their version and conditions
- 03Differencesmaterial version and conditions shown separately for each report
- 04Gapwhat was not found for the changed version
- 05Sourcethe full report, ready to open
What it costs and when it pays back
- Today
- 200 questions about earlier tests × 30 min = 100 h (€3,490)
- With the application, a person checks the output in 25% of the time
- €873
- Hardware in the company, counted in the scenario as a rented GEX45 server
- €214
- Saving
- €2,403 a month
Payback of €3,000
1.2 months
The volume, the time and the checking share are the scenario's assumptions, which we measure in the test. €34.90 an hour is the EU average hourly labour cost for 2025 according to Eurostat. The price of the GEX45 with 24 GB of GPU memory is from Hetzner's website on 22 October 2026, without the one-off setup fee of €209. We price hardware in your own server room after the analysis.
The count covers the time spent searching, reading and putting material together. What remains is the time to check the result against its source, 25% of today's work in the scenario. We measure both times in a test on 20 to 100 cases the team has already handled.
What runs locally and what you have to agree
„Private AI” in an offer should lead to a specific place where the processing happens: a named vendor service or your own server. A local model computes on a named machine, and Ollama distinguishes local models from cloud models. A copy of the report's content stays in the text prepared for search, and a passage can end up in a saved conversation. Both places, with their backups, fall under the access rules.
- Where the application takes documents from and where it saves results
- What stays in logs, backups and the question history
- Who has access to questions, answers and summaries
- Who updates the model, the application and the document set
First use what you can already find
If the reports have consistent names and version labels, filtering in the repository is enough, and an engineer who knows the project and the author narrows the set quickly. An example is the Benchling search tools, with filters for project, tag, author and date. An assistant is worth considering when finding the files still leaves a lot of reading: the material version is described in the text, the conditions sit in an appendix, and the titles say little. Anthropic describes search by meaning and by words together in its Contextual Retrieval article.
Consistent report names and versions
filters in your current repository
no build
Versions and conditions described in the text
a private assistant that sets out the trials
from €3,000 plus hardware
Inconsistent labels in the archive
tidy the labels before an assistant
the team's own work
Where to start
The first trial covers a chosen part of the documentation and a question like the one about the housing material. A person who knows the project judges whether the assistant found the right reports and kept the differences between the trials. The rules for comparing before you buy are in buying local AI with a test on your documents. We deploy local models as part of private LLMs, and we build the view for working with sources as an AI application. For the free analysis, a description of one case and of the places where the material sits is enough. Confidential files stay in the company, and you get a written plan within 2 working days.
Worked example · Scenario
How we count the payback: a local model in R&D
We run the same count for your archive in the free analysis.
€5,817
a month for the work on 1,000 questions
EU cloud version payback: 0.6 mo.
Our assumptions are 10 minutes to find the right instruction, and checking in 15% of the old time. €34.90 an hour is the EU average hourly labour cost for 2025 according to Eurostat, for companies with 10 or more employees. Model cost: Claude Sonnet 5.5, about 6,000 + 500 tokens per answer in the EU cloud version, at the ECB rate of 1.1205 USD per euro on 22 October 2026. We count the payback for an app with a model in the EU cloud (from €3,000 net). We price the model on your server after a call, and the cost of people's work stays the same.
See the full calculation →
