"What's the pH of sample X in this report?"
Single lookup, single doc, value in visible text. Frontier models do this well. We won't pretend otherwise.
Disconnected reportsParsed structure
Drag to watch the structure form.
"Which batches failed pH spec last month?"
That question just cost an analyst two hours and forty PDFs. It should take seconds.
Lab reports already hold the answers. When that knowledge sits scattered across disconnected documents, investigations slow and analysts spend hours re-entering data.
What is AI Intime
A sovereign, hyper-contextualised knowledge engine for batch manufacturing and complex laboratory operations. Analytical lab reports are its first proof, not its identity.
Desktop exercise
Please continue on your desktop to complete the exercise. 🥹👉👈
The hands-on test works best with a file download, your own AI tools, and our sandbox open side by side, which a larger screen handles far better. Please open this same page on your computer to run it.
Choose your email
AI Intime · a Vegam Solutions product
Forwarded this by your VP or lab director? It stands alone. Start at the exercise. Your own AI tools, no salesperson.
Sovereign, hyper-contextualised knowledge engine · batch manufacturing & complex lab operations
"Which batches failed pH spec last month?"
That question just cost an analyst two hours and forty PDFs.
It should take seconds.
Lab reports already hold the answers. But when that knowledge sits scattered across disconnected documents, investigations slow, decisions wait, and analysts spend hours searching and re-entering data.
Don't take our word for it. Take the dataset, run the same queries in the AI tools you already use, and try to prove us wrong.
No form. No gate. Forward this link to whoever runs your lab data.
Drag to watch Parameter → Method → Sample → Batch crystallise. This is the product.
The shift
Scattered reports become one queryable structure.
Disconnected reportsParsed structure
Drag to watch the structure form.
General AI can find any of these words. It cannot link them, and every question you actually ask depends on the link.
The problem
Why generic AI structurally fails on lab reports
One report. Multiple samples, multiple batches, several test methods, with values split between tables and prose, parameter names reused and overloaded.
Retrieval AI, the engine behind Copilot, ChatGPT and Glean, finds text that mentions things. But your questions aren't about text. They're about relationships: which parameter, from which method, on which sample, in which batch.
The approach
How the Lab Agent reads your reports
Relationships, not retrieval. The Lab Agent, one domain agent inside the AI Intime knowledge engine, builds a custom parser per report template. For each template:
01
Identifies samples and batches
Every ID, wherever it hides: header, table, footnote, prose.
02
Detects test methods
Which procedure produced which result.
03
Extracts parameters from tables and narrative
Values live in both. Table-only parsers miss the deviations discussed in prose.
04
Maps every relationship explicitly
Parameter → Test Method → Sample → Batch. Stated and stored, not inferred at query time.
05
Stores it as structured data
Your library becomes queryable like a database. Filtering, aggregation, boolean logic, trend analysis.
The parser is a capability. AI Intime is the product: a sovereign, hyper-contextualised knowledge engine for batch manufacturing and complex laboratory operations. Lab reports are its first proof, not its identity. Your reports stay exactly as they are; the parser adapts, not you. What you're standing up is a platform you own.
Before you run anything
You're about to think we rigged this
"Of course it works flawlessly. You built the parser for this exact file. That proves nothing."
Correct. That's the whole point.
Generic AI is genuinely good. It drafts, summarises, translates, brainstorms. We're not claiming it's stupid; we use it too.
What it cannot do is read a value and know which method produced it, which sample it belongs to, which batch it traces to, because the report shows that chain in its layout. It never states it as data.
Generic AI
Good at
- Writing
- Summarising
- Translation
- Brainstorming
AI Intime
Purpose-built for
- Batches
- Samples
- Methods
- Parameters
- Quality records
- Operational relationships
Not a smarter model. Custom parsing that beats generic parsing on lab data, structured and unstructured. One parser per template, built for yours.
The exercise
Prove it yourself 45 to 60 minutes
Same data, same queries, two environments. You run both. You judge.
◧ Desktop exercise
This part runs on your computer.
The hands-on test works best with a file download, your own AI tools, and our sandbox open side by side, which a larger screen handles far better. Please open this link on a desktop to run it.
STEP 1
Download the dataset
40+ synthetic lab reports: multiple samples and batches, several methods, values across tables and prose. Synthetic only, so you can test without exposing real data.
STEP 2
Run the queries in your own AI tools
Copilot, ChatGPT, Claude, Gemini. Paste each query, note what comes back.
STEP 3
Run the same queries in AI Intime
Load the same data in the sandbox, paste the identical queries, compare side by side.
STEP 4
What it means for your reports
Like what you saw? We build a custom parser for your report formats. Your data does not adapt to us; our parser adapts to you.
The seven queries you'll run
- Which reports contain Sodium?
- Which reports contain Sodium and Chloride?
- Sample order 010002838482: what test types in 2023?
- Linolenic, Margaric and Stearic acid for batch p021508801
- Which samples failed pH specification in the last 30 days?
- Reports where heavy metal content exceeded 0.1 ppm
- Any parameter that deviated over 10% from spec this month
STEP 1
Download the synthetic dataset
40+ synthetic analytical lab reports. Multiple samples and batches per report, several test methods, values across tables and prose, overloaded parameter names. Three tidy PDFs would be a softball, so we didn't give you three tidy PDFs.
⚠ Use only the synthetic data provided. Do not paste real lab reports into public AI tools.
Your instinct will be to grab one of your own CoAs and throw it at Copilot. Don't. Your real reports are covered by exactly the confidentiality obligations this whole category of tools trips over. The synthetic set exists so you can test aggressively with zero exposure.
STEP 2
Run these queries in your own AI tools
Copilot, ChatGPT, Claude, Gemini, whichever you already trust. Upload the dataset, paste the queries. Each is written the way a lab person would actually ask it.
We're not telling you the right answers yet. That would defeat the point. You'd be checking our homework instead of finding out for yourself. The verified key is sealed until you've run both sides.
Which reports contain Sodium?
An ideal tool shouldEnumerate and count across every report, not just find a mention.
Which reports contain Sodium and Chloride?
An ideal tool shouldIntersect two parameters across the full library. A relational operation, not retrieval.
I am getting complaints about sample order 010002838482, what test types were made on this sample order in 2023?
An ideal tool shouldTrace one sample order across every report and across time.
Give me the value of Linolenic acid, Margaric acid and Stearic acid for batch p021508801
An ideal tool shouldResolve the parameter → batch link, then return all three values.
Which samples failed pH specification in the last 30 days?
An ideal tool shouldFilter by spec outcome across the whole library, bounded by time.
List all reports where heavy metal content exceeded 0.1 ppm
An ideal tool shouldApply the threshold to values scattered through tables and prose.
Flag any parameter that deviated by more than 10% from specification this month
An ideal tool shouldCompute deviation against spec, across every parameter and report.
STEP 3
Run the same queries in AI Intime
Open the hosted sandbox, load the same synthetic dataset, paste the identical queries. Try to break it: reorder, rephrase, chain follow-ups. You'll respect it more if you tried.
STEP 4
What this means for your reports
We built this for one template. If you like what you saw, we build a custom parsing agent for your report formats and your workflows. Your data does not adapt to our template; our parser adapts to your data.
Before you close this tab: send your exported results to whoever asked you to run this. It's the evidence that survives the meeting: a test your team ran, on neutral data, with no vendor in the room. It's also the answer to the first thing IT will say, which is "we already have Copilot."
What you just tested is one proof point, not the product. The engine underneath (knowledge retrieval, memory, governance) is the platform your second use case runs on without a second build-out.
Where it actually runs
AI Intime runs fully on-premise.
Air-gapped. Nothing leaves your boundary.
This sandbox is hosted so you can test the Lab Agent on our template, with zero commitment and zero exposure. When we build a custom parser for your templates, it runs inside your infrastructure, and it will be equally or more powerful, because it's built for your data instead of ours.
Forward this section
For your IT and security team
You will have this conversation without us in the room. This section is written so you can forward it as-is.
Deployment topology
Fully on-premise / air-gapped, inside your infrastructure. Nothing about the deployment requires an outbound connection.
Data flow
No data leaves the enterprise boundary. The platform sits on top of the existing stack (SAP, MES, PLM, LIMS, Salesforce, Microsoft 365) and replaces none of it. No new system of record.
Model flexibility
Model-agnostic: LLMs and SLMs are swappable components, not a lock-in. Governance and guardrails are part of the framework, not bolted on.
Integration approach
Connects to existing systems through the framework's integration layer rather than custom point-to-point plumbing. Integration work is part of the engagement, not left to your team.
The two objections you're already drafting
"We already have Copilot / M365 E5."
You do, and it's good at what it's for. But your Step 2 results expose an architecture gap, not a licensing one: cross-report relational queries.
"We could build this on Azure OpenAI ourselves."
You probably could. The honest question is the cost to build and then maintain a custom parser per template, forever. We'd rather have that build-vs-buy conversation with you than around you.
This is architecture co-design with IT before procurement, not a tool that routes around you. What you'd govern is a sovereign knowledge engine inside your walls, and on-prem makes that easier, not harder.
Who this page is for
Three readers, three jobs
Built for batch manufacturers: specialty chemicals & adhesives, pharma & life-sciences manufacturing, industrial coatings. Pick your seat.
If you run the lab data
You're the person this exercise was written for. You'll be the harshest tester of it, and that's good. It's built to be poked.
QA / QC Manager
All industries
You are the person re-keying CoA values, verifying specs line by line, assembling OOS investigations from a folder of PDFs. The grind this page describes is the job description. The failed queries in Step 2 are the exact tasks eating your week.
Senior Analyst / PhD Analytical Chemist
All industries
You own the method quirks nobody wrote down, and you get interrupted daily for answers only you can give. Try to break the demo: reorder queries, chain follow-ups, hunt edge cases. If it holds, it's earned something; if it doesn't, you've gained ammunition either way.
Stability Coordinator
Pharma
Timepoints × batches × parameters × methods: your data is the relational chain this parser maps. Stability studies trapped in PDFs are the single strongest fit for this exercise anywhere. Run the cross-batch queries first.
Lab Operations Manager
All industries
Throughput, turnaround time, analyst hours burned on lookup, and the eternal "have we tested this before?" The exercise shows exactly which class of lookup stops consuming analyst hours once reports are structured.
QC / Regulatory Scientist
Pharma
Every number needs a traceable origin. The Lab Agent's explicit Parameter → Method → Sample → Batch mapping is what makes an answer auditable rather than plausible. Check the traceability in Step 3, not just the answers.
Head of AI / Digital Transformation
All industries
You need AI wins you can defend. A comparative evaluation your own team ran, generic tools versus purpose-built parsing on neutral data, is exactly that. It also shows firsthand how hard this parsing is to build in-house, which is worth knowing before anyone proposes doing so.
If you hold the budget
You won't run this exercise, and you shouldn't. Your move is to assign it: forward this link to your sharpest skeptic and ask for their verdict in a week. Their hands-on result is worth more than any demo we could give you, and it's the evidence that survives the IT conversation.
VP / Director / Head of Quality Control
Pharma & Life Sciences
Stability studies are the structurally perfect case: timepoint × batch × parameter × method, all trapped in PDFs. Structured lab data compresses OOS investigation cycles, CoA release turnaround, and audit prep, with the explicit traceability your data-integrity posture (ALCOA+, 21 CFR Part 11) demands.
Director of Analytical Development
Pharma & Life Sciences
Method transfer across sites and compiling analytical data for submissions both run on historical test data your team currently excavates by hand. Structured history also surfaces redundant testing before it's re-run.
VP / Director of Quality
Specialty Chemicals & Adhesives
CoAs on every batch shipment, customer-specific spec sheets, raw-material qualification, spec drift, complaint investigations: all queries against data you already own but can't currently query. The exercise shows the difference in an afternoon of your analyst's time.
Technical Director / VP R&D (often titled CTO)
Specialty Chemicals & Adhesives
"Have we made or tested something like this before?" is an NPD tax paid in redundant testing and formulation history buried in old reports. Structured historical test data speeds formulation and cuts repeat work. This is an R&D asset, not an IT project.
Director of Analytical Services
Specialty Chemicals & Adhesives
Analytical throughput and turnaround time to production are gated by lookup and re-keying, not by instruments. Eliminating manual PDF review is capacity you already paid for.
VP / Director of Quality
Industrial Coatings
OEM spec compliance and batch-to-batch consistency benefit where your QC produces dense analytical output. Honest note: if your lab is mostly physical-property testing (gloss, adhesion, viscosity, cure), this use case is a lighter fit. The stronger entry for coatings is our Knowledge Twin use case. Worth a conversation either way.
How to delegate it: forward this link with one line: "Run this against Copilot and tell me if they're right." The page stands alone; your analyst needs nothing from us. What they find becomes your business case and your IT briefing in one.
If you run IT & security
Honest framing: this page wasn't written to excite you, because the pain it describes isn't yours. What you own is where data goes, what runs on your infrastructure, and who maintains it. That's covered, specifically and forwardably, in the IT & security section. When the lab brings you their Step 2 results, that's the conversation to start with: architecture co-design, before procurement, not after.
Who's behind this
Built for ourselves first
AI Intime is built by Vegam Solutions. The origin is unglamorous: Vegam's own institutional knowledge was trapped in emails, meeting notes, and proposals, and cloud AI was disqualified by their own customers' T&Cs and data regulations. So they built it fully on-prem, for themselves. It saved hundreds of hours per week internally. Then they productised it.
0years in manufacturing digitalisation
0plant deployments
0countries
Flagship deployments
Early flagship deployments at larger enterprises, proof the platform holds up in regulated, multi-plant environments:
BASF CoatingsHenkelSIKA (MBCC)ArkemaJSW SteelLG ElectronicsSaint-GobainSKFUnilever
Commercial model: subscription, scoped to single-use-case payback. It pays for itself in saved labour, starting from a 30-day quick win and a 60-day rollout to first-phase production.
Ran the exercise? Let's look at your templates.
Bring your Step 2 results, especially the failures. Thirty minutes: your report formats, what a custom parser for them looks like, and what a 30-day quick win would be scoped to. Your data doesn't adapt to our template; our parser adapts to your data.
Book the follow-up callA scheduled call, not a contact form. Fully on-prem in production. Nothing you show us leaves your boundary either.