Industry solution

Pharma, Biotech & Life Sciences

Keep every result, sample and batch traceable from bench to release

Lab records, instruments, SOP training, trial data and batch release, designed to link so every quality question has a traceable answer.

Products
8
Challenges
5
Concepts
15
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Scientist checking a tablet beside an ultra-low-temperature freezer in a biotech lab, with an HPLC and a clean-room suite behind.
LabsOfScience(opens LabsOfScience in a new tab)AssetHandler(opens AssetHandler in a new tab)ManufacturedOps(opens ManufacturedOps in a new tab)CrewFoundry(opens CrewFoundry in a new tab)SemanticFed(opens SemanticFed in a new tab)BigConsole(opens BigConsole in a new tab)FluidGrids(opens FluidGrids in a new tab)Botlit(opens Botlit in a new tab)

The problem

Why traceability is hard in a regulated lab

In a regulated lab, one event, a warming freezer or a failed calibration check, reaches samples, results, training, trial supply and batch release, and the evidence for each sits in a different system.

Life-sciences organizations are judged on evidence. Regulators, partners and their own quality teams expect records to be attributable, legible, contemporaneous, original and accurate, and they expect every result to trace back to the instrument, method, analyst and material behind it. The science moves quickly, and the evidence has to keep pace with it.

The trouble is that the evidence is spread across systems bought one at a time: an electronic notebook, spreadsheets or a LIMS for samples, a maintenance tool for calibration, a learning system for training, a quality system for deviations, and a CRO's portals for trial data. Each is sound on its own. The gaps between them are where investigations stall, where a training lapse goes unnoticed, and where people export data they should not.

The pressure arrives from every side at once: discovery timelines, study enrollment, clinical-supply schedules and inspection readiness, often carried by small teams in a growing biotech. What helps is not another silo but records that link across the work, automation for the routine hand-offs, and a named person accountable for every quality decision.

Who this is for

  • Head of Quality or QA Director

    Inspection-ready records, deviations and CAPAs closed on time, and batch release decisions that can be explained later.

  • Lab Operations Manager

    Instruments in calibration, freezers watched, samples findable, and no bookings on an instrument that is out of service.

  • Head of Discovery or Research Director

    Results that trace back to the exact run, data version and method, so candidate decisions rest on data that holds up.

  • Clinical Operations Lead

    One agreed view of enrollment, query aging and kit supply per site, without moving subject data where it should not go.

  • Training and Compliance Coordinator

    Knowing who is trained on the current SOP version before the effective date, without chasing acknowledgements by email.

  • Data Governance and Privacy Lead

    Fields that could re-identify a subject masked by role, residency respected, and a record of every query and agent tool call against sensitive data.

A day in the life

The story behind the solution

From Monday's freezer alarm to Friday's batch release

Amara Osei is Head of Quality at Quillon Biotherapeutics. Her team, working alongside Priya Raman's lab operations group, oversees three things at once: a discovery group closing in on its next development candidate, a bioanalytical lab processing samples from a Phase 2 study, and a small GMP suite that fills clinical supply for that same study. All three depend on records that live in different places. This is one week.

  1. Monday, 02:47

    The minus-80 in Room 214 is warming

    The building alarm calls Tomás Reyes, the on-call technician from Priya's lab operations team, at 02:47: freezer F-12 is at -61 °C and climbing after a compressor fault. F-12 holds Phase 2 pharmacokinetic plasma samples and two boxes of working cell bank vials. Tomás has space in a backup freezer but no reliable map of which racks hold what, and the excursion SOP is a file on a shared drive he cannot open from his phone. By the time Amara is awake the boxes have been moved, but nobody can yet say which ones were above -70 °C, or for how long.

    How this is solved: Cold-storage excursions with no sample map

  2. Tuesday, 10:15

    HPLC-07 fails its calibration check

    HPLC-07 is a qualified instrument in the shared analytical lab, kept under the GMP calibration program and used by both discovery and QC. This morning its routine check fails wavelength accuracy, six weeks after the last pass. Dr. Hana Kobayashi's discovery team used it for purity data in the development candidate nomination package due Thursday, and the QC lab used it for a release test on clinical-supply batch CS-2611. Amara needs every result produced on HPLC-07 since the last good check. Today that means asking three groups to search their notebooks, while the instrument still has two bookings this afternoon.

    How this is solved: Results produced on an instrument that failed calibration

  3. Wednesday, 14:00

    Sample-receipt SOP version 5 goes effective Monday

    After Monday's excursion, the sample-receipt SOP gains a temperature-log check at intake. Version 5 is approved and effective in five days. Thirty-four analysts across two sites must read and acknowledge it before they receive samples again, and the last internal audit found an analyst who ran a method before their training record was complete. The training coordinator is tracking acknowledgements in a spreadsheet, and the bench keeps asking what actually changed.

    How this is solved: SOP revisions that outrun training records

  4. Thursday, 08:30

    Three enrollment numbers for one study

    At the weekly study team meeting, Grace Mwangi from clinical operations brings the CRO's enrollment spreadsheet, the data manager brings open query counts from the EDC, and supply brings kit levels from the randomization system. The numbers disagree for four of eighteen sites. Two EU sites are close to running out of kits, which is why batch CS-2611 matters, and nobody can reconcile the sources without pulling subject-level rows onto a laptop.

    How this is solved: Trial oversight built from conflicting spreadsheets

  5. Friday, 16:00

    Batch CS-2611 is ready for QA review, almost

    Oliver Brandt in QA opens the executed batch record for CS-2611. One deviation from the filling step is still open, the QC purity result flagged on Tuesday is still under impact assessment, and he needs evidence that the checkweigher was in calibration for the whole run. Each answer sits in a different system. Packaging needs the batch released on Monday to get kits to sites on time, and Amara will not let a batch go forward on assumptions.

    How this is solved: Batch release that depends on five systems

None of these problems is exotic. They are the ordinary work of a regulated lab, made hard because the evidence is scattered. The rest of this page shows how Burdenoff products are designed to work together so that each question Amara faced this week has one traceable answer, with a person making every quality decision.

Challenges and how they are solved

5 problems, several products, one connected answer

Each challenge shows the problem as it happens, how the products are designed to hand work to each other, and the concepts that illustrate it. Share any challenge on its own.

Challenge 1 of 5

Cold-storage excursions with no sample map

AssetHandler(opens AssetHandler in a new tab)FluidGrids(opens FluidGrids in a new tab)LabsOfScience(opens LabsOfScience in a new tab)Botlit(opens Botlit in a new tab)

The problem

A freezer alarm in the middle of the night tests every gap between systems at once. At 02:47 an alarm reports that a -80 °C freezer holding clinical PK samples, cell bank vials and reference standards has been warming since 02:31, but the alert names only the unit. The rack map is a spreadsheet last updated in the spring, the excursion SOP sits on a shared drive, and the service contract number lives in someone's inbox. Boxes get moved in the dark with no move times logged, and the next morning the lab spends hours reconstructing which ones were exposed, for how long, and which studies and experiments they belong to, before anyone can decide what to quarantine.

What it costs

Irreplaceable study samples and cell banks can be lost or used without a documented assessment, and every excursion becomes a manual investigation that pulls scientists off the bench.

How the products work together

AssetHandler is designed to hold each freezer as an asset with its temperature probe connected as an IoT device, so an excursion arrives as a reading against a named unit with its service history. FluidGrids can take that alert as a webhook trigger: it pages the on-call technician with the unit's location and backup capacity, raises an AssetHandler work order for the service vendor, and asks LabsOfScience what is inside. LabsOfScience is designed to list every sample, reagent and cell bank vial stored in that unit by its recorded location, with the study or experiment each belongs to, and to let the lab quarantine them pending assessment. At the freezer door, a Botlit agent grounded in the controlled SOP library is designed to return the excursion steps with the source cited. The trace, the work order and each quarantine decision are designed to sit on one excursion record.

The outcome it is designed for

The morning after an excursion is designed to start with a list, not a hunt: which materials were exposed, who owns them and what was decided, all on one linked record.

The concepts behind it

Challenge 2 of 5

Results produced on an instrument that failed calibration

AssetHandler(opens AssetHandler in a new tab)LabsOfScience(opens LabsOfScience in a new tab)FluidGrids(opens FluidGrids in a new tab)ManufacturedOps(opens ManufacturedOps in a new tab)

The problem

When an instrument fails a calibration check, every result it produced since the last pass is in question. On a shared HPLC that can mean discovery purity data headed for a candidate decision and a QC release test for a clinical-supply batch, owned by different groups. The certificate sits in the metrology vendor's portal, the booking calendar in a shared spreadsheet, and the results across notebooks and instrument exports, so the impact assessment starts with emails to every group and relies on nobody forgetting a run. Until someone updates the calendar, the instrument can still be booked.

What it costs

Decisions on a candidate or a batch may rest on data from an out-of-tolerance instrument, and the impact assessment takes days of manual searching.

How the products work together

AssetHandler is designed to keep each lab instrument's calibration and qualification schedule, with due dates, tolerances, certificates and out-of-tolerance events on the asset record. When a check fails, AssetHandler marks the instrument out of service and raises a work order. FluidGrids can carry that status to LabsOfScience, where the instrument is designed to show as under maintenance and new bookings go to its custodian for approval. LabsOfScience is designed to start the impact assessment from its booking record: every booking on HPLC-07 since the last passing check, with the analyst and experiment it was made for. From those experiments the lab pulls the runs, notebook entries and dataset versions to review. The list goes to QA and the discovery lead, and the QC result for CS-2611 is designed to reach ManufacturedOps flagged as under assessment.

The outcome it is designed for

An out-of-tolerance finding is designed to become a complete, reviewable list of the bookings and experiments to reassess, and anyone booking the instrument sees that it is under maintenance.

The concepts behind it

Challenge 3 of 5

SOP revisions that outrun training records

LabsOfScience(opens LabsOfScience in a new tab)CrewFoundry(opens CrewFoundry in a new tab)Botlit(opens Botlit in a new tab)

The problem

An SOP revision is simple on paper and messy on the floor. The document system knows the version changed, the learning system knows who opened a course, and the lab knows who actually works the bench, but no single list shows who is current on the new version today. As the effective date approaches, supervisors cannot say with confidence who may perform the method, analysts asking what changed from the last version get answers from memory, and a training record completed after the work it covers is exactly the kind of gap an internal audit or an inspector finds.

What it costs

Work done by someone not yet trained on the current version is a finding waiting to happen, and chasing acknowledgements by email eats the training coordinator's week.

How the products work together

In LabsOfScience the SOP lives as a versioned protocol, and publishing version 5 freezes its steps. Each published version is designed to be added to the Botlit knowledge base that holds the controlled SOP library. CrewFoundry is designed to pick up the new version and assign read-and-understood training to everyone whose role or skills include sample receipt, due by the effective date. Its training matrix shows who is current, overdue or not yet cleared, and is where CAPA-driven retraining gets assigned. CrewFoundry is designed to send the not-cleared list back to LabsOfScience, where bookings on the intake bench's instruments by those analysts go to a supervisor for approval. At the bench, a Botlit agent is designed to answer 'what changed in version 5?' by quoting the changed steps with the source cited, and to say plainly when the library does not cover a question.

The outcome it is designed for

Before the effective date, the lab is designed to see exactly who is cleared to receive samples under version 5, and questions about the change get answers that cite the controlled text.

The concepts behind it

Challenge 4 of 5

Trial oversight built from conflicting spreadsheets

SemanticFed(opens SemanticFed in a new tab)FluidGrids(opens FluidGrids in a new tab)BigConsole(opens BigConsole in a new tab)Botlit(opens Botlit in a new tab)

The problem

A multi-region Phase 2 study draws on sources that each count subjects a little differently: a CRO enrollment spreadsheet sent weekly, open data queries in the EDC, randomization and kit levels in the IRT, and sample shipments from the central lab. Study team meetings open with an argument about whose number is right. The sponsor's data-transfer policy keeps subject-level rows from EU sites in their region, so reconciling the sources properly would mean exporting rows the policy does not allow to move. Meanwhile slow enrollment at one site, aging queries at another and a looming kit shortfall surface later than they should.

What it costs

Site risks such as slow enrollment, aging queries and kit shortfalls surface late, and teams improvise data handling that governance would never approve.

How the products work together

SemanticFed is designed to federate the study's systems, EDC, CTMS, IRT and central lab, through its database and REST/GraphQL connectors into one governed model, with shared definitions such as randomized subject and open query older than 30 days. Queries are designed to run where the data lives, so subject-level rows stay in their region, and fields that could re-identify a subject, such as date of birth and visit dates, are masked for roles that do not need them. FluidGrids can run the approved site-level queries on a schedule and write the results into BigConsole datasinks. BigConsole turns them into a study oversight console that drills from study to country to site, and is designed to flag a site when enrollment, query aging or kit cover crosses thresholds the study team sets. Before the Thursday meeting, a Botlit agent can query the console and summarize what moved.

The outcome it is designed for

The study team is designed to start each meeting from one agreed set of numbers, with site risks flagged by threshold and no subject-level data leaving its region.

The concepts behind it

Challenge 5 of 5

Batch release that depends on five systems

ManufacturedOps(opens ManufacturedOps in a new tab)LabsOfScience(opens LabsOfScience in a new tab)AssetHandler(opens AssetHandler in a new tab)

The problem

Before a clinical-supply batch can move on, QA has to close or justify every open item, and the evidence rarely sits in one place. For batch CS-2611 there are three: an open deviation from the filling step, a QC purity result produced on an HPLC that later failed calibration, and evidence that the checkweigher stayed within calibration throughout the fill. The deviation lives in the quality system, the QC result in the lab notebook, the calibration record in a maintenance tool. The release checklist gets built by hand in a spreadsheet, against packaging and site supply dates.

What it costs

Release decisions are slow and hard to defend, and a missed open item can mean a batch reaches sites before its evidence is complete.

How the products work together

ManufacturedOps holds the batch as a production order with its lots, operations and quality records, and its batch review screen is designed to assemble every open item before disposition. The filling-step deviation and its CAPA sit in the ManufacturedOps non-conformance register, tracked to closure. QC results are designed to come across from LabsOfScience with their notebook status, so a result under impact assessment shows as blocking and a signed and witnessed retest can clear it. Where equipment is managed centrally in AssetHandler, its calibration status is designed to appear here against the run window; line equipment can also stay in ManufacturedOps' own asset registry. QA resolves or justifies each item and records release or hold with a comment. That checklist and decision are designed to form the release file the Qualified Person reviews before certifying the batch for EU sites.

The outcome it is designed for

QA is designed to review a batch against one complete list of open items and evidence, and to record a release or hold decision that can be explained later.

The concepts behind it

How it fits together

How a failed calibration check reaches the release decision

Follow one thread through the week: HPLC-07 fails calibration, and the question of what that affects travels from the instrument to the lab record, the batch, the people and the dashboards, with a person deciding at each step. Each hand-off shows how the products are designed to work together.

  1. To LabsOfScience: Out-of-service status and the last passing check date, carried by a FluidGrids automation

  2. To ManufacturedOps: QC result for CS-2611 flagged as under impact assessment

  3. To CrewFoundry: CAPA action: retrain QC analysts on the revised HPLC purity SOP, which adds a wavelength check to system suitability

  4. To SemanticFed: Training status by analyst and SOP version, read as a governed source

  5. To FluidGrids: Approved governed queries for a scheduled workflow

  6. To BigConsole: Aggregated quality and supply metrics in a datasink

  7. To Botlit: Live console figures an agent can query

Step 1 of 8: Detect

Products in this solution

What each product brings

  • LabsOfScience

    Lab record: notebook, protocols, data and inventory

    The system of record for the bench: signed and witnessed notebooks, versioned protocols, immutable dataset versions, sample and reagent inventory and instrument booking, with each pipeline run bound to the dataset version it used and a provenance chain from result to protocol designed on top.

    Visit LabsOfScienceAll concepts

  • AssetHandler

    Instrument calibration, cold storage and work orders

    Keeps every instrument and freezer as an asset with preventive maintenance schedules, custody and work orders, and is designed to extend those schedules to calibration and qualification and to take readings from connected sensors, so equipment fitness is on record rather than in someone's memory.

    Visit AssetHandlerAll concepts

  • ManufacturedOps

    Batch production orders, deviations, CAPA and release review

    Runs production orders, material lots, inspections, non-conformances and CAPA, with lot genealogy in progress, and is designed to bring the evidence for a clinical-supply batch together for QA disposition.

    Visit ManufacturedOpsAll concepts

  • CrewFoundry

    SOP training matrix and qualifications

    Its learning paths, certificates and skills graph are the basis for tracking who is trained on which SOP version and who is cleared to perform a method.

    Visit CrewFoundryAll concepts

  • SemanticFed

    Governed trial and quality data across systems

    Is designed to federate EDC, CTMS, IRT, lab and quality sources through its database and API connectors into one versioned semantic model, with row and column policies and masking defined once, and queries pushed down so regulated rows stay where they live.

    Visit SemanticFedAll concepts

  • BigConsole

    Study oversight and quality consoles

    Turns governed metrics into consoles with filters and drill-down, with threshold alerts designed to attach at the datasink, for study teams and quality leaders who need one set of numbers.

    Visit BigConsoleAll concepts

  • FluidGrids

    Automation between alarms, systems and people

    Connects the events that start quality work, such as a sensor alarm or a failed check arriving as a webhook, to the actions that follow, and feeds scheduled results into BigConsole datasinks.

    Visit FluidGridsAll concepts

  • Botlit

    Cited answers from SOPs and consoles

    Agents are designed to answer bench and management questions from the controlled SOP library and from live consoles, showing their sources and saying plainly when nothing matches.

    Visit BotlitAll concepts

One platform underneath: Burdenoff Workspaces

All eight products run on Burdenoff Workspaces: one sign-on for scientists, QA, clinical and manufacturing staff, role-based access set once, a single audit trail across products, and one bill. Records are designed to link across products without extra accounts or exports.

Concept gallery

Every concept in this solution

15 concepts from 8 products. Each one links to its own page on the product's website, and every view has a link you can share.

Pitch kit

The Life Sciences solution in one minute

Quality questions in a biotech rarely stay inside one system. A warming freezer, a failed calibration check or an SOP revision reaches samples, results, training records, trial supply and batch release. Burdenoff brings LabsOfScience, AssetHandler, ManufacturedOps, CrewFoundry, SemanticFed, BigConsole, FluidGrids and Botlit together on one workspace, so each question is designed to have one traceable answer and a person makes every quality decision.

  • Freezer excursions are designed to arrive with a sample map: what was inside, how long it was exposed, and what the lab decided.
  • A failed calibration check is designed to produce the bookings and experiments to review, and to flag the instrument as under maintenance.
  • New SOP versions are designed to assign read-and-understood training and show who is not yet cleared.
  • Study oversight is designed to come from one governed model, with subject data kept in its region and re-identifying fields masked by role.
  • Batch release is designed to bring deviations, QC results and equipment calibration onto one checklist before QA decides.
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Questions

Frequently asked

Are these products validated for GxP use or compliant with 21 CFR Part 11?

No such claim is made. The products are early, and this page describes a solution design. They are built around attributable, signed and versioned records and a shared audit trail, the kinds of controls a validation effort looks for, but computerized system validation and any compliance determination remain with your quality organization.

Do we need all eight products to start?

No. Most labs would start where the pain is sharpest, for example LabsOfScience with AssetHandler for instruments and cold storage, and add training, trial oversight or batch release later. Because every product runs on the same workspace, records added later are designed to link to what is already there.

Can subject-level trial data stay in its region?

SemanticFed is designed to push queries down to each source, so subject-level rows are read where they live and only approved aggregates or masked fields travel onward. What may cross a boundary is set by policy that a person approves; residency rules are still yours to decide with your privacy and legal teams.

We already run an ELN, a LIMS and a quality system. Does this replace them?

Not necessarily. SemanticFed is designed to federate existing systems into one governed model through its database and API connectors, and FluidGrids can move events and results between them. Some teams may choose LabsOfScience or ManufacturedOps as the system of record for a new lab or suite; others may run Burdenoff products alongside the tools they have today.

How are AI answers kept from inventing procedures?

Botlit agents are designed to answer only from the knowledge bases you connect, such as your controlled SOP library, to show the passages they used, and to say plainly when nothing matches. Agents do not sign records, release batches or approve policies; those decisions stay with named people.

Does this fit CROs and CDMOs as well as sponsors?

The same pieces apply. A CRO's lab needs instruments, samples and training records under control, and a CDMO needs batch review. Each organization runs its own workspace with its own access control, and anything shared with a partner follows the policies that organization sets.

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Where these products also work together

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This is a solution concept: it shows how Burdenoff products are designed to work together in this industry. The images are illustrations of the concepts, not screenshots of the actual products, and every name and figure in them is sample data.