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Deviations, procedures and documents: AI proposes, the quality specialist decides

AI for pharma logistics: deviations, procedures and documents

In pharma logistics the cost of an error is product quality and the client's trust, so our AI does not decide on its own. In QualityOS it proposes a deviation category and an action, and the quality specialist confirms. The knowledge base answers in the words of the procedures and always shows the source. Language models run on our own servers, so the data stays inside the perimeter.

Discuss your case What is included
01Who it is for
  • Pharma manufacturers and distributors with their own warehouse and logistics
  • Quality teams that track deviations in e-mail and spreadsheets
  • Companies that cannot send data to external AI services
02What it replaces
  • Deviations scattered across e-mail, spreadsheets and folders
  • Searching hundreds of procedure files for an answer
  • External AI services where the data must not leave
AI in pharma logistics
03What is included

What is included

QualityOS 2.0

Quality: signal, investigation, action
in production

A cold-chain quality operating system: one loop from signal to investigation and recurrence prevention, plus field warehouse audits from a phone.

The job

Close quality into one loop: catch the deviation, reach the root cause, close it with an action and stop it recurring.

What hurt

  • Deviations surface at the client, not at home
  • Investigations live in chat and files; the cause never reaches an action
  • Nobody confirms whether corrective actions actually worked

What you get

  • A signal enters the system at once and follows a route
  • AI proposes the classification and the action, a human confirms
  • Warehouse audits happen on a phone, with no paper checklists

What your client gets

  • The client hears about a problem from you, with the analysis already done
  • For a supplier audit there is a complete exportable dossier
  • Repeat deviations from the same cause stop
last change: 17 Sep 2026

Knowledge base with answers

An answer with a link to the source
in production

Every instruction and policy in one search: the system answers in the documents' own words and always shows the source. If the documents have no answer, it says so.

The job

End the hunt for knowledge across folders and colleagues, and give agents a foundation to act on.

What hurt

  • Policies are scattered across hundreds of files and folders
  • A new hire spends weeks learning where things live

What you get

  • The answer comes with a quote and a link to the document
  • Knowledge stops being one employee's private asset

What your client gets

  • Employees give the same answers and they follow the policy
last change: 17 Sep 2026

In-house AI platform

Language models on our own hardware
in production

Language models running on the company's own servers: data never leaves the perimeter, the cost per request does not depend on an external price list, and access goes through one gateway.

The job

Use language models where the data must not leave the building.

What hurt

  • Some data must never go to external services
  • The cost of external model calls grows with usage

What you get

  • Data stays inside the perimeter
  • Predictable cost as load grows

What your client gets

  • Sensitive data processing never leaves the perimeter
last change: 21 Aug 2026

Delivery quality metrics

Are we getting better or worse
in production

Accumulates, day by day, the metrics clients actually pay for: delivery time, documents and cold-chain compliance. The question 'did it improve?' is answered with a chart, not a feeling.

last change: 10 Sep 2026

Cargo photo record

Proof of cargo condition
in production

Warehouse photo capture of freight units: every shot is bound to the unit, time and order, so a dispute about cargo condition is closed with a fact, not a memory.

last change: 15 Sep 2026

Data map

A corporate schema for people and agents
in production

Builds a map of corporate database structure: tables, domains, relations and lineage. Useful both to a person investigating and to an agent working with the data.

last change: 15 Sep 2026
04How it works for you

How it works for you

Bring signals into one loop

Deviations and audit findings enter the system at once and follow a route instead of getting lost in e-mail.

AI proposes the classification

The agent proposes the category and the action; the quality specialist confirms or corrects.

Answers from documents

An employee asks, and the knowledge base answers in the words of the procedure, with a link. If the documents hold no answer, it says so.

An audit dossier

A deviation, from signal to action, exports whole — for a supplier audit or a review with the client.

05Comparison

Three ways to solve this

We are not the only option, and we show plainly where we lose.

Build it yourselfOff-the-shelf vendorBIOCARD Tech
Time to first resultQuarters: hiring, learning the domain, architecture from scratchWeeks to install — then months bending processes to fit the boxA demo immediately, a pilot in weeks: the product already exists
Domain contextYou will have to teach the team what a batch and a temperature mode areA generic product with no notion of a shipment or a cold chainWe work in it daily — the context is included
Who carries the riskYou do: for the timeline and for the solution turning out wrongYou do: changes outside the vendor roadmap usually never happenWe do: the product runs in our own company and we use it ourselves
Changes for youAnything is possible — if the team stays and is not pulled elsewhereOn the vendor's roadmap and prioritiesDays rather than quarters: our own delivery pipeline
Where we loseFull control over the code and priorities stays with youA large partner ecosystem, training and certificationWe are a small team and we do not take every job
06Cost

Price on request

There is no price list, and that is not a trick: almost every product needs configuration for your processes and integrations, so the same system costs differently at two companies. We quote after the demo, when the scope is visible — not before it.

01

What you get

A working product rather than from-scratch development. The core already exists and runs in our own company, so you pay for the rollout and the fit-out, not for inventing the solution.

02

What the quote is made of

The amount of fit-out for your processes, the number and complexity of integrations, and the requirements around data and timing. All of it becomes visible after a demo on your own data.

03

When we name a figure

After the demo and a short review of your case. The quote is fixed — not time-and-materials.

Where the work stops

AI does not close deviations, change procedures or make product decisions: it prepares the classification, a draft and a link to the source, and the responsible employee decides. Language models can run on your servers so the data stays inside your perimeter.

07FAQ

Questions about this solution

Does the AI close deviations by itself?

No. The agent proposes the category and the action, and the quality specialist confirms. The whole case, from signal to action, exports into a dossier.

Can the data stay out of external services?

Yes. Our AI platform runs on our own servers: the data stays inside the perimeter, and the cost per request does not depend on an external tariff.

Where does the knowledge base get its answers?

Only from your documents: instructions, procedures, SOPs. Every answer comes with a quote and a link to the source; if the documents hold no answer, the system says so.

How are you different from an IT integrator?

We are a pharma logistics operator ourselves: warehouse, transport, quality. Everything we offer runs in our own company first — in daily work with medicines.

08Related solutions

Related solutions

See it on your data

Forty minutes, a working product, no slides. Then a pilot in your environment.

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