In the wrong laboratory, a gifted scientist can spend three hours doing work that looks remarkably like clicking “yes.” A machine has already measured 96 samples. The peaks are clean. The controls behave. Still, a human checks the batch, trace by trace, because the result matters and the standard operating procedure says so. This is the corner of health care where Indigo BioAutomation has built its business: the stretch between an instrument producing data and a lab feeling confident enough to release an answer.
The Carmel, Indiana company makes two hosted software systems. ASCENT handles chromatography and mass spectrometry data from LC/GC-MS/MS instruments. ARQ works on q/RT-PCR data. Their interfaces and underlying measurements differ, but the job is the same. Indigo translates a laboratory's own quality rules into an automated workflow, processes the raw data, identifies routine results and sends the questionable cases to a scientist. The phrase of art is “review by exception.” The plain-English version is: stop paying experts to stare at things that are fine.
01 / THE BOTTLENECKThe instrument was fast. The review queue was not.
Precision Diagnostics supplied Indigo's cleanest before-and-after. According to laboratory manager Josh Corbin, reviewing a batch of 96 samples took roughly three hours before ASCENT. After the lab kept improving its assays alongside the software, review time fell to about 45 minutes. That is a 75 percent reduction, but the more useful detail is what disappeared: the obligation to give every unremarkable result the same attention as a suspicious one.
The problem becomes uglier as testing volume climbs. During the early pandemic, HealthTrackRx launched a COVID-19 test and ran into a familiar scaling trap. The lab built semi-automated tools and hired more scientists and analysts, yet training and review threatened its 24-to-36-hour turnaround promise. It was refusing business to protect quality. ARQ automated the interpretation and reporting layer, and HealthTrackRx later reported a 50-to-60 percent reduction in time spent on analysis and reporting without compromising accuracy or quality.
“We found ourselves refusing business because we needed to maintain our standards of quality and our promised turnaround time.”Pallavi Upadhyay, HealthTrackRx
This is what failed first: not the chemistry, not the instrument, but the human review process wrapped around them. More hiring bought capacity slowly and introduced a second burden - teaching each new analyst how the lab makes judgment calls. What changed the calculation was volume. Once demand moved faster than training, the cost of “we have always checked it this way” became visible in delayed results and declined work.
02 / THE PRODUCTAn SOP that behaves like software
ASCENT's work begins with peak processing. In mass spectrometry, a correct peak is the foundation of the quantitative answer. The product then applies methods and quality rules derived from the lab's SOP, organizes exceptions for review, records the analyst and certifier, and can send finished results to the laboratory information system. ARQ performs the analogous job for PCR amplification curves. Both are browser-accessible, hosted systems meant to work across instruments and users rather than live on one vendor's workstation.
raw data
process + rules + exceptions
review + release
The distinction matters. A laboratory does not buy Indigo to invent its quality policy. The lab supplies the method, acceptance criteria and escalation logic; Indigo makes those decisions consistent and reusable. The software is less a robotic scientist than an exceptionally literal colleague who never forgets the checklist, never gets bored by the 700th clean peak and keeps a record of what happened.
The company's newer data-warehouse offer widens that aperture. Indigo says certified batches contain more than 200 analytics-ready data points, stored through an AWS Redshift-backed warehouse. Instead of treating each released batch as yesterday's paperwork, a lab can compare instruments, users, assays and quality-control behavior over time. That moves the product from “help me finish today's queue” toward “show me how this operation is changing.”
03 / THE WEDGESell attention, not another instrument
Indigo does not make the mass spectrometer or PCR machine. It occupies the seam between instrument software and the laboratory information system. That puts it against several alternatives at once: manual review, local scripts, vendor-specific chromatography data systems and broad laboratory-informatics suites. The company's pitch is that a neutral layer can carry one quality standard across mixed instruments, multiple assays and, eventually, sites.
The default
Instrument-specific software, spreadsheets, tribal knowledge and analysts reviewing nearly everything. It works until volume, staff turnover or assay complexity exposes the seams.
Indigo's bet
Hosted, instrument-agnostic workflows that encode the SOP, clear routine data, preserve an audit trail and create a shared operational dataset.
Its customers are not casual testers. Indigo names clinical and forensic labs, hospital systems, independent and specialty diagnostics groups, test developers and pharmaceutical or biotechnology teams. Public examples include Gravity Diagnostics, Precision Diagnostics, HealthTrackRx and CAL Laboratory Services. Historical reporting also named Eli Lilly and the Centers for Disease Control and Prevention as users. In 2025, Indigo said its systems had processed more than 250 million clinical samples.
Gravity offers the scale case. The diagnostics company deployed ASCENT as the primary analytical operations system for a large toxicology panel spanning more than 20 instruments. In a joint case study, it reported a tenfold increase in sample capacity and as much as a 75 percent cut in review-and-release time. Gravity's volume had once doubled overnight. Its laboratory chief later described Indigo as a strategic decision that allowed the operation to absorb the influx rather than merely survive it.
04 / THE COMPANYBuilt by a chemist who learned the software problem firsthand
Founder Randall K. Julian Jr. spent 14 years in discovery chemistry at Eli Lilly. Indigo grew from informatics technology developed by his research group, began as Indigo BioSystems in 2006 and later spun out as an independent company. That lineage explains the product's temperament. It is concerned with raw measurements, statistical behavior, laboratory standards and the awkward distance between a scientifically possible test and a repeatable production workflow.
The company won a TechPoint Mira Award for ASCENT in 2013. A year later, it raised an $8.5 million Series B led by Bootstrap Venture Fund. The money was earmarked for product development, new markets, scaling the hosted system and shifting from perpetual licenses to software-as-a-service. Public list pricing remains absent; the sales process runs through a tailored demonstration and an assessment of assays, instruments and integrations. That makes sense for an implementation whose useful unit is not a seat, but a governed laboratory workflow.
Partnerships have extended the reach. SCIEX agreed in 2012 to combine its mass-spectrometry platforms with ASCENT for clinical research and forensic toxicology. Indigo joined the Tetra Partner Network in 2022, pairing its analysis with harmonized pharmaceutical R&D data. The company also says work begun in 2018 with The Binding Site, now part of Thermo Fisher Scientific, helped turn a mass-spectrometry breakthrough into practical analytical infrastructure for the EXENT multiple-myeloma system, alongside Bruker and Tecan.
The machine makes a measurement. The business is deciding which measurements deserve a human's afternoon.
05 / THE COPYABLE IDEAAutomate the boring decision before the glamorous one
The lesson is portable beyond laboratories. Start by finding a high-volume review process where experts repeatedly confirm normal cases. Write down the acceptance rules. Separate objective checks from judgment. Automate the checks, preserve the evidence and route only exceptions to a person with authority. Then measure elapsed time, error rates and how often the automation asks for help. Indigo's customers did not begin by requesting an all-knowing AI. They began with a queue.
The conditions matter. Review by exception depends on stable SOPs, reliable incoming data, validated methods and enough repetition to justify integration work. A tiny lab with a constantly changing assay may not recover the setup cost. A process whose judgment cannot be expressed as rules will still need people in the loop. And in regulated diagnostics, faster review is valuable only if validation, traceability and escalation remain intact. Indigo's ISO 13485 quality system and Class I exempt device listings are part of the sale because automation without governance would merely produce errors at higher speed.
Indigo is now pushing past the batch. ASCENT 4.4 added deeper validation monitoring, including assay versioning, approval timestamps and side-by-side comparisons. The company was also a finalist in the 2025 ADLM Data Science Challenge for a system that retrieves answers from complex laboratory documents using generative AI. The progression is sensible: first encode the rules, then create structured history, then make that accumulated knowledge easier to query.
The company's most interesting claim is not that software can replace a scientist. It is that a laboratory has been misallocating the scientist. A clean result does not need admiration. An odd peak, a drifting control or a broken assumption does. Indigo sells the distance between those two things - and, for a busy lab, 135 reclaimed minutes at a time.