The execution gap

Your product ships.
Then it vanishes.

You know units sold. You know which customers complained loudly enough to open a ticket. You know your internal QC, which describes how the product behaves in your building, not theirs.

Everything in between, the part where your product actually meets the world, goes unrecorded. Adept records it.

Book a demo See a live dashboard

Six manufacturer views, sample data, no form to fill in.

Every feedback loop is built on a unit.

Manufacturing had the defect record. Clinical research had the case report form. Software had the event log. For life science tools, the unit is the protocol execution fingerprint, and it has three parts. Almost nobody holds more than two.

Documented

Intent

Your instructions for use say thirty minutes at room temperature. You revised that document eleven times before you shipped it.

Recorded

Outcome

The instrument, the LIMS, and the analysis pipeline all capture what came out. You find out that something failed.

Discarded

Execution

It was forty five minutes, on a bench at 26 degrees, by a second year graduate student who had already reused a tip. No system on earth wrote that down.

Intent and outcome together tell you that something failed. Only execution tells you why, and only execution tells you what to change.

Your kit is fixed for two years. Change control makes sure of that. The guidance layer wrapped around it does not have to be, and neither does what you know about how it performs.

The problems we hear again and again.

Real conversations with life science manufacturers. Hover over a card to see what we heard.

01 · Execution ambiguity

How do you know whether a failed result came from the product or from execution?

"If we assume they did everything perfectly, then yeah, the results are the results. But boy, do we really believe that?"
Sr. Dir. of Application Support, Life Sciences Manufacturer
02 · Bad support data

How do product teams get visibility into what is actually happening at the bench today?

"Product managers don't get reliable data from support tickets."
Product Manager, Tier 1 Life Sciences Manufacturer
03 · Documentation is not enough

Why are customers still asking questions that are already answered in the protocol?

"Not a single customer has ever read their user guide start to finish."
Technical Support Lead, NGS Reagent Manufacturer
04 · Silent churn

How often do customers try your product and disappear without ever asking for help?

"We're sending out free samples and it's like, 'Have you tested it?'"
General Manager, Life Sciences Reagent Manufacturer
05 · Long optimization cycles

How many weeks or months are troubleshooting and optimization cycles adding to adoption?

"It could be months or it could be weeks."
Commercial Lead, Reagent Kit Manufacturer
06 · Support burden

How much of your support burden comes from users who are not experts?

"Half our customers are not experts, and those are the ones that require the most extensive support."
Sr. Dir. of Application Support, Life Sciences Manufacturer

How it works

A seamless data pipeline from the scientist's benchtop directly to your product team's dashboard.

01
Real-world data

Capture at the bench.

Our zero-friction copilot guides scientists through your protocols step by step. As they work, the app captures real-time execution data, dwell times, and customer voice queries ("Why is this cloudy?").

  • ✓ Exact timing per step
  • ✓ Deviations and pauses
  • ✓ Step completion rate
Adept mobile app: DNA Library Prep protocol screen with past runs
Explore the live dashboard

Six manufacturer personas, sample data, opens in a new tab.

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Best viewed full-screen on a larger display.
02
Proactive insights

Analyze in the platform.

Field data from your deployment is aggregated into interactive dashboards. Instantly spot step-level bottlenecks, track lot-to-lot quality variance, and monitor early-warning churn signatures across your accounts.

  • ✓ Protocol success rate tracking
  • ✓ Weekly active experiments by SKU
  • ✓ Early-warning signals on at-risk accounts
03
Real outcomes

One platform. Many outcomes.

Stop guessing what goes wrong after the kit leaves your warehouse. Use hard telemetry to proactively update user guides, deploy targeted field alerts, and resolve support tickets faster.

In practice: a single flagged reagent lot shows up automatically in both Support's ticket queue and Quality's watchlist. The same signal, seen by both teams, without anyone connecting the dots by hand.

Customer Success

One health score, combining success rate, deviations, and query volume, flags which accounts need a call today.

Technical Support

A ranked triage queue traces this week's ticket spike straight to the lot causing it.

Product

A step-by-step confusion heatmap shows exactly which page to fix next in the user guide.

Quality

Lot-level success rates flag a failing lot for quarantine before it becomes a complaint.

Marketing

Adoption curves show which assay is taking off months before published research confirms it.

Explore all six dashboards

View them now

Why partner with Adept?

Make data-driven product improvements based on trustworthy real-world execution data collected at scale, while providing white-glove, real-time product support to every customer.

Today's reality

  • ✗ Support tickets are fragmented and incomplete.
  • ✗ Field visits are infrequent, so you miss the majority of what actually happens at the bench.
  • ✗ Feature priorities are based on best guesses rather than hard utilization data.
  • ✗ There is no way to compare performance or support burden across SKUs or customer segments.
  • ✗ You have no early warning system for accounts that are quietly at risk.
  • ✗ Support burden keeps climbing even as your documentation gets more thorough.
  • ✗ Slow root-cause diagnosis delays adoption and time to value on new SKUs.

With Adept

  • ✓ Structured failure data: every protocol step generates actionable field data.
  • ✓ Real-time signal: surface product issues as they occur, not weeks or months later.
  • ✓ Faster iteration: go from months of guesswork to weeks of data-driven improvements.
  • ✓ Confidence at scale: consistent, automated customer support across your portfolio.
  • ✓ Accelerate R&D: real-world data means every experiment informs the next product cycle.
  • ✓ Portfolio benchmarking: compare success rates across your own SKUs, lots, and customer segments, and against your own history.
Cost savings calculator

Support costs scale with every product you ship.

See your Year 1 impact.

Start from our conservative assumptions and adjust them to match your portfolio. Every number below updates as you move the sliders.

Your portfolio

Adept is priced per product family, not per SKU.

We default low. Move it up if your ticket mix is mostly repeat protocol questions.

Your Year 1 impact

Support cost today
$900,000
Support cost with Adept
$360,000
What Adept costs you
$20,000
Net Year 1 savings
$520,000
Return on every dollar spent
26x
Get a custom ROI analysis

Today, without Adept

Annual tickets across portfolio
3,000
Total annual support cost
$900,000

With Adept

Questions resolved at the bench
1,800
Tickets still reaching your team
1,200
Residual support cost
$360,000
Adept platform and usage
$20,000
Total cost with Adept
$380,000

Estimates only, based on the figures you enter. Adept cost is modelled as an annual platform fee per product family, which includes an allowance of answered questions, plus a per-question rate beyond that allowance. It excludes the intelligence tier, which is priced separately per end user. Your actual savings depend on your ticket mix, your support cost structure, and deployment scope. Talk to us for a tailored analysis.

The compounding asset

Support savings are what you notice first. They are not the reason to do this.

The calculator above measures what you stop spending. This is about what you start knowing. Every guided run produces a protocol execution fingerprint: a structured record of how one scientist executed one protocol from one of your SKUs. Collected across your install base, inside your deployment and nobody else's, those fingerprints become an asset you cannot buy anywhere else.

Three things you cannot know today

What you hear now

"Customers are reporting low yield with this kit."

What you would know

That step 7 is where users stall, that the median dwell time there is three times your documented estimate, and that the failure concentrates in first-time users rather than in a reagent lot.

What you hear now

"Our protocol success rate looks acceptable."

What you would know

Acceptable compared to what. Your own SKUs against each other, this lot against the last twelve, experienced labs against first-time users, this quarter against the one before the revision shipped.

What you hear now

Nothing. The account simply stops reordering.

What you would know

That their last four runs failed at the same step and that nobody opened a ticket. Silent churn becomes a signal you can act on months before the renewal conversation.

The dataset is worth more every month it runs.

Support deflection is roughly flat from day one. Intelligence is not. It is the only part of this that gets better while you do nothing.

Month 1
Questions get answered at the bench instead of in your ticket queue. You see which steps generate the most confusion in a single product family.
Month 6
Enough runs to separate signal from noise. Lot-level differences become visible. You can tell a documentation problem from a chemistry problem, and revise the user guide against evidence rather than intuition.
Month 18
Longitudinal history across your SKUs and customer segments. Now you can answer the question that has never been answerable: did the change we shipped actually improve execution in the field?
Year 3
Execution data becomes an input to design. Next-generation kits get specified against how the last generation was actually run, not against how it was supposed to be run.
Why not build it yourselves

You could build the assistant. Getting it opened is the hard part.

We will say the quiet part first: a capable engineer could wire together a voice-guided protocol assistant with modern tooling in a few months. The software layer is not the hard part, and it gets easier every quarter.

The hard part is that your customer's afternoon is not yours alone. One scientist uses an extraction kit from one vendor, an instrument from another, and a library prep kit from a third, in a single workflow. If every manufacturer ships their own assistant, that scientist juggles five apps and opens none of them. An assistant nobody opens generates no execution data, which means the internal build produces an empty dashboard.

There is a second cost. Running a live voice AI product means a permanent software organization: continuous UX work, cloud infrastructure, uptime, security review, and model updates. That is not a project with an end date, and it is not what your R&D budget exists to fund. Manufacturers buy software rather than build it for the same reason they always have.

Adept is the one assistant a scientist will actually use, because it guides the whole bench and not just your part of it. Your data stays isolated in your deployment. The adoption is what you cannot replicate.

Talk about the intelligence tier See what the data looks like
Adoption evidence

A support agent nobody opens is worth nothing.

The risk in bundling software with your kit is not whether it works. It is whether scientists use it. Here is what bench scientists told us after running their own protocols with Adept.

"It is like having a lab notebook without having to write and type one, and I very much like that."
Early Access Scientist
"I love the ghost of the previous and future step display, giving a reminder of what was just done and what to look forward to."
Early Access Scientist
"I particularly love the fact that it does the calculations for me."
Early Access Scientist
"Adept offers a solution that cuts the planning time in the lab substantially. For a new kit, or even a kit I've been using, interacting with the protocol and creating documentation in real time changes the game."
Scientist, Private Biomedical Research Org
"I think having an AI trained to the level of a senior scientist on a specific workflow and its underlying molecular biology would be a real asset, a force multiplier, for a team, assisting a technician or research assistant every time they run the workflow on a plate of samples."
Scientist, NGS Company
"I've spent the last 15 years managing teams and executing complex NGS protocols on large cohorts of samples myself. I've had numerous moments where a tech would have to leave the lab, come find me, and ask a question that pauses the whole workflow."
Scientist, NGS Company
"I loved trying out the app; it was nice that it was self-paced. I think I would use this every time or close to it, especially for a more complicated procedure."
PhD Candidate, Biology

Your data never leaves your deployment.

Every engagement runs on the same standard terms. No pooling, no sharing, no exceptions negotiated case by case.

See exactly what we capture, and what we never capture

Questions? Answers.

Everything a commercial or product leader wants to know before working with Adept.

How does Adept integrate with our existing kits or instruments?

Adept is embedded as a branded field support agent alongside your protocol. There is no re-engineering of your kit or instrument required. Scientists access step-by-step, voice-guided instructions through a lightweight companion app tied to your SKU.

What happens to our proprietary usage data?

Your data stays yours. Each deployment gets a strongly isolated, siloed knowledge base and analytics that are never shared across customers, encrypted in transit and at rest, with role-based access and MFA for privileged users. We do not use your documentation or data to train third-party models. Transcripts and usage stats are used only to deliver and improve your own deployment. At contract end, your data is returned or deleted per the agreed retention window, and data governance is worked out before any agreement is signed. Full detail on our security page.

How is this different from our field application scientists or a tool like Zendesk?

Field application scientist teams provide expert support but do not scale and capture little structured data. Generic support platforms handle tickets, not multi-step protocol execution. Adept guides the protocol itself, in real time, and turns every run into structured, SKU-level intelligence, which is something neither approach produces today.

What does getting started involve, and how long does setup take?

Deployments are scoped around a single flagship product family so you can validate value quickly. Since there is no engineering lift required on your side, most manufacturers are live within weeks.

What does Adept cost?

Pricing is structured per product family, so you only pay for the SKUs where support burden is highest. An annual platform fee covers setup and an allowance of answered questions, with usage-based pricing beyond that. Use the calculator above to estimate your savings, then talk to us for a quote tailored to your portfolio.

Which manufacturers is Adept built for?

Adept is built for manufacturers selling complex, workflow-intensive products, including NGS reagent kits, analytical instruments, and liquid handling automation, where protocol execution is high-stakes and support burden is acute.

Kristina Fontanez, PhD, Founder and CEO of Adept

Founder-led. Built by scientists.

Adept is led by Kristina Fontanez, PhD, a repeat founder who co-invented the single-cell technology powering Illumina’s Billion Cell Atlas. She previously co-founded Fluent BioSciences, acquired by Illumina for $85M in 2024.

After 12+ years building molecular tools, she's now solving the real-time support bottleneck for genomics and life science products at scale.

She's building Adept alongside Drew Bryant, PhD (Head of Software Engineering — ex-Google, ex-Amazon) and Jason Callina (CTO — ex-Woebot Health), bringing 30+ years of combined experience shipping production software and scientific AI/ML.

Kristina Fontanez signature

Founder & CEO, Adept Scientific

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