The intelligence layer
for bioprocess.

Invert connects your bioprocess equipment and data sources across process development and manufacturing. Do better science and move faster than ever, both upstream and downstream.

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integrates with

Integrations / Connect

Consolidate process data on autopilot.

Invert integrates easily with all your equipment and systems. Online and offline data are automatically linked to relevant runs, all in real time. True off-the-shelf connectivity.

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Invert — Live Run · DK13-Exp012 · BR-09

BR-09 · 58h elapsed

DK13-Exp012 · Ambr250 vessel 3

DO %

Temp

VCD offline

LIVE

Offline sample · h36

VCD24.1 ×10⁶/mL

Viability95.4 %

Glucose1.8 g/L

↗ Vi-CELL BLU · auto-linked from ELN

Foundation / Lineage

View your full process in one place.

Understand exactly how upstream parameters drive downstream outcomes. Invert automatically provides full traceability across your unit operations, making complex step yield calculations easy.

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Process lineage — Batch 51

›

Biostat STR 200L

Cell Culture

Alfa Laval BTPX

Centrifugation

Feed200 L

Centrate~185 L

Conc. Sludge~15 L

SS Hold Tank 200L

Hold

Hold~185 L

ÄKTA Process 50

Chromatography

Load~185 L

Pool~12 L

Foundation / Harmonize

Handle data inconsistencies and units easily.

Are you also tired of playing spreadsheet tetris? Invert's Library automatically harmonizes metric names and data units across data sources. All to your specifications.

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Metric library

Name ⋮	Unit ⋮	Data Sources ⋮
Agitation Speed2 ▾	RPM	DeltaV – Process DataBiostat STR – OPC
Agitation	rpm	DeltaV – Process Data
Agitator speed	rpm	Biostat STR – OPC
Air Sparge5 ›	L/h	OSIsoft PI – HistorianDeltaV – Process Data
Average diameter3 ›	µm	Vi-CELL BLU – Cell Counter
Base Totalizer (Volume)5 ›	L	DeltaV – Process DataOSIsoft PI – Historian

Enablement / Report

Generate deep reports automatically.

Autogenerate interactive reports and deep bioprocess analyses. The Invert Assist AI only writes Python that can be audited, reproduced, and improved upon. Gone are the days of pasting screenshots of plots into presentations.

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Multivariate Clustering Analysis — DK13 250mL Bioreactor DOE

Overview

Multivariate comparison of 12 bioreactor runs from DK13-Exp012 (250 mL, E. coli K-12-4373). DOE factors: temperature shift, pH shift, feed rate. Hierarchical clustering identifies 3 performance groups primarily driven by pH shift setpoint.

Table · DOE Factor & Cluster Assignments

Run	Temp Shift (°C)	pH Shift	Feed Rate	Titer (g/L)	Cluster
BR-01	24.0	6.5	Low	7.35	C1
BR-11	24.0	6.5	High	8.84	C2
BR-04	24.0	4.5	High	1.91	C3
BR-12	26.0	4.5	Medium	3.03	C3

Chart · Mean Final Titer by Cluster

C1 · 7.35

C2 · 7.18 ± 1.09

C3 · 2.36 ± 0.88

Analysis · Key Findings

01pH shift is the dominant factor. All pH 4.5 runs cluster together with ~2.4 g/L titer; pH ≥ 5.5 runs achieve ~7.2 g/L — a 3× difference.

02C3 runs grew to higher OD (mean 210 vs 192) but produced less product, suggesting pH 4.5 favours biomass over product formation.

Intelligence / Data quality

Monitor your data health.

Invert constantly monitors data quality across your workflows. You will automatically get notified when data is missing or incomplete. Be confident that expensive experimental results yield data assets ready for future use.

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Data Quality — Invert (demo)

FAIR Data Score

Overall assessment across FAIR principles

Findable100

Accessible100

Interoperable11

Reusable24

AI Readiness

Data readiness for AI/ML workflows

Severity	FAIR	Issue Type	Affected	Data Type
Critical	FindableReusable	Missing timeseries data for runs	587	Runs
High	InteroperableReusable	Missing event data for runs	792	Runs
Medium	InteroperableReusable	Properties with unspecified data type	266	Properties
Low	FindableReusable	Metrics without any timeseries	179	Metrics

Intelligence / Invert Assist

Use AI to answer anything
about your bioprocess.
Prompt in. Python out.

Assist is our AI. It is an expert in both bioprocess and data science. Assist lets you dive deeper than ever before into your data, using only your curiosity. You will be surprised at what Assist can do, from hybrid modeling to model based DOEs, it's all a prompt away.

Answer hard questions in minutes, no coding background needed

DOE, anomaly detection, hybrid modeling, or PCAs — Assist will help you dive as deep into your data as you want

Reproducible and traceable. Assist only ever writes Python code that can be audited, reproduced, and defended

Learn more

Build prediction model

DK13 clustering rep...

Batch 51 vs Batch 4...

We have 14 weeks to IND. Based on current batch cadence and remaining scale-up milestones, are we going to make it?

Using skills: run-analysis · milestone-tracking

Runs & context · 4/9/2026 09:14

USP-Run-001 · USP-Run-002 · And 36 more
Skills: run-analysis · milestone-tracking

›Loading skill content and exploring run scope

›Computing batch cadence across 38 USP runs

›Mapping remaining scale-up milestones to timeline

›Analysing Protein A step yield trend — last 5 batches

Batch	PA Yield %	Δ vs prior
USP-034	98.4	—
USP-035	97.8	−0.6%
USP-036	97.1	−0.7%
USP-037	96.5	−0.6%
USP-038	95.2	−1.3%

›Synthesising IND timeline assessment

Projected USP completion11 / 14 weeks

⚠ DSP risk flagged−4.2% Protein A yield trend

IND Timeline Assessment — 14-Week Review

Open report

Report ready

Can you build a model to predict step yield from the upstream parameters and tell me what's driving the decline?

Using skills: ml-modeling · dsp-analysis

Runs & context · 4/9/2026 09:15

USP-Run-001 · USP-Run-002 · And 29 more pairs
Skills: ml-modeling · dsp-analysis

›Preparing USP Protein A paired dataset

›Feature engineering — extracting upstream predictors

# upstream feature matrix (31 pairs)
features = ['peak_vcd_h72', 'do_deviation_integral',
             'glucose_depletion_h48_h72', 'mean_pH',
             'peak_osmolality', 'lactate_max']
✓ 31 complete pairs · 7 held out for validation

›Training Gaussian process regression — 31 training pairs

›Evaluating on holdout set and ranking predictor importance

Predictor	Importance	Trend
Peak VCD at h72	0.61	↑ drifting high
DO deviation integral	0.22	stable
Glucose depletion h48–h72	0.11	stable

›Generating model summary and mechanistic explanation

GP regression holdoutR² 0.91

Principal drivers identified3 predictors

⚠ Root causePeak VCD DBC overload

Protein A Yield Prediction Model — GP Regression

Open report

Report ready

Ask anything about your bioprocess data...

Why teams choose Invert

Tools to speed up science.

01 / Integrations

Save scientists from grunt work.

Bioprocess teams spend over 50% of their time managing data. Exporting data from instruments, aligning timestamps, normalizing units all costs precious time. With Invert, scientists can focus on the most important work.

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02 / Enablement

Solve problems across unit ops.

Bioprocesses are complex, and no single team member can optimize the whole process. With Invert, data from all teams and sources are easily analyzed and learned from. Customers consistently gain process insights that would have been otherwise missed.

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03 / Intelligence

Turn experimental spend into data assets.

Every run represents a significant investment. Process data and learnings are often lost to time. With Invert, every experiment contributes to your team's institutional knowledge.

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04 / Intelligence

Accelerate your science manifold.

Invert users report doing in minutes what previously took months. Whether writing reports or training predictive machine learning, Invert's AI can help turn ideas into reality in minutes.

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“

We’d invested over two million dollars and six months of data science work into a digital twin that couldn’t run. Invert had us live in three days. An AVEVA PI integration would have taken six months minimum — and still wouldn’t have captured everything Invert does.

IT Lead

Top 10 global biopharma

Customer Stories

Compounding institutional knowledge.

Top 10 Biopharma

Operationalized a $2M+ digital twin by closing the Ambr250 data gap.

<1 moto go live

$2M+investment unlocked

Real-timedata feed

Read the case study

Top 10 CDMO

Recovering 3,000+ FTE hours per program in CMC biologics development.

3,046hours saved per program

~98%less manual data work

Zeronew headcount

Read the case study

Clinical-Stage Cell Therapy

Unified process data across process development and MSAT.

$400Kcosts avoided

5platforms unified

12–18 mobuild effort avoided

Read the case study

Compliant with

ISO 27001SOC 221 CFR Part 11EU GMP Annex 11EU Data ActAudit trailLearn more about Invert’s security & compliance

The intelligence layerfor bioprocess.

Consolidate process data on autopilot.

View your full process in one place.

Handle data inconsistencies and units easily.

Generate deep reports automatically.

Monitor your data health.

Use AI to answer anythingabout your bioprocess.Prompt in. Python out.

Tools to speed up science.

Save scientists from grunt work.

Solve problems across unit ops.

Turn experimental spend into data assets.

Accelerate your science manifold.

Compounding institutional knowledge.

Operationalized a $2M+ digital twin by closing the Ambr250 data gap.

Recovering 3,000+ FTE hours per program in CMC biologics development.

Unified process data across process development and MSAT.

The intelligence layer
for bioprocess.

Use AI to answer anything
about your bioprocess.
Prompt in. Python out.