Hospitals
Imaging support and ward signals that give specialists time for the cases that need them.
- Urgent studies surface ahead of routine reads
- Ward flags before the morning round
- Results sit in the chart, not a second system
Clinical platforms
Healthcare AI Development and Clinical-System Integration
Hospitals, pharma, research, and healthtech.
Platforms for diagnostics, drug discovery, and patient care. They sit inside the clinical systems you already run. A clinician or scientist stays the decision.
- ImagingRadiology, pathology, dermatology
- ResearchScreening, targets, trials
- PrivateModels and data you control
Clinical worklistSample
Illustrative list. A specialist confirms every result. Nothing here is a diagnosis.
🩺Clinician signsSoftware does not
🔒Private by designHIPAA · PHIPA · GDPR
⚡Up to 60%shorter diagnostic cycles
Diagnostics✦Radiology✦Pathology✦Dermatology✦Drug discovery✦Remote monitoring✦PACS✦EMR✦EHR✦Diagnostics✦Radiology✦Pathology✦Dermatology✦Drug discovery✦Remote monitoring✦PACS✦EMR✦EHR✦
- 🛡️SecurityISO 27001
Encryption and access control are part of the release, from an ISO 27001 certified team.
- 🔒Health privacyHIPAA, PHIPA, GDPR
The platform is designed for these rules. Patient data stays under your control.
- 🏥Clinical systemsPACS, EMR, EHR
Results land in the worklist and the chart people already open.
- 🔑OwnershipModels you keep
Custom models are built for your organization to own.
Up to 60%shorter diagnostic cycles with imaging support
8builds, each with a person accountable
3privacy frameworks in the design: HIPAA, PHIPA, GDPR
100%of results confirmed by a clinician or scientist
ISO 27001certified delivery team
Who it is for
Built for the people who deliver care and run the science
Hospitals, pharmaceutical companies, research institutions, and healthtech teams. The platform has to live in a reading room, a lab, a trial, or a care program.
Pharma
Molecule screening, targets, toxicity flags, and help around the trial record.
- A ranked list before a full lab cycle
- Toxicity signals with the source attached
- Trial documents searchable, sourced and checked
Research
Literature, multi-omics, and study files in one place a scientist can check.
- Questions over an approved corpus
- Papers and datasets named in every answer
- Models the institution owns
Healthtech
A private model and a product surface, with the privacy design buyers will ask about.
- One workflow, built to be validated
- Privacy design written down early
- A clinician review step in the product
What the platform covers
Eight builds, each with a person accountable
None of these replace the clinician or scientist who signs the result. Filter by area, and see what goes in, what comes out, and who decides.
Try it · imaging
Review stays with the specialist. The queue gets smarter.
Move the confidence threshold to see how studies are ordered. Low-confidence results are not hidden or decided by software. They wait in a review queue for a person.
Chest radiographConfidence 0.91
Illustrative image. Not a diagnosis. A specialist confirms every finding.
Confidence threshold for priority
0 priority · 0 held for review
Core capabilities
The healthcare work we deliver
Choose a capability. The worklist in the hero follows the same choice.
Try it · discovery
From thousands of compounds to the few worth a lab cycle
Screening narrows the field and ranks what is left, each with evidence a scientist can open and challenge. Counts shown are illustrative.
The care journey
Where the platform helps, and who stays in charge
Follow one patient through the system. Select a stage to see what the platform does, which system it touches, and who decides.
Try it · patient care
See how an early signal gets triaged
Move the signals and watch how a deterioration flag is routed. This mirrors how a model would be tuned to a care plan.
Vitals drift from baseline
Abnormal lab results
Days since discharge
Care plan on file?
Illustrative routing logic · your model is tuned to the care plan and data you provide
Where it lands
The same platform, shaped to the setting
Pick a setting to see what changes, the first build we would scope, and how success is measured.
Integration
It lands in the systems your clinicians already open
Results are written in the shape those systems already accept, so there is no second chart. Select a connection to see what flows. Standards shown are typical and confirmed per site.
Privacy by design
Data stays where your rules say it stays
Access control, encryption, audit trails, and federated learning when the data should not move.
Where does the data go?
Validation and safety
Checked before anyone relies on it
A demo is not validation. Readers and researchers check the work against the success measure from the workflow, and a person stays in the loop at every step.
1
→Suggest
The model proposes a finding, a rank or a flag, with its confidence and its source.
2
→Review
A specialist or scientist opens the evidence. Low-confidence items wait in a queue.
3
→Confirm
A named person signs. The platform never signs the chart.
4
Record
The decision and the access are logged for audit and for model monitoring.
SensitivityMisses
How many real findings the model catches, checked against your readers.
SpecificityFalse alarms
How often a normal case is wrongly flagged, so the queue stays worth reading.
Time to readWorkflow impact
Study-to-first-read time before and after, in your own workflow.
Override rateTrust signal
How often a clinician disagrees. A rising rate is a reason to look again.
DriftStays accurate
Monitoring for changes in scanners, protocols or population after go-live.
Audit coverageAccountability
Every access and every decision traceable for the privacy office.
How we deliver
From the workflow to a system people trust
We start from the workflow and the data you can actually use.
What you get
What teams use the platform for
Shorter diagnostic cycles
Imaging support can help surface urgent studies sooner and free specialists for critical cases.
- Studies surfaced sooner
- Specialist time kept for the hard reads
- The sign-off stays with the clinician
Faster research
Screening and trial support so teams spend less time chasing weak candidates.
- A ranked list before a full lab cycle
- Toxicity and literature in one review
- Evidence a scientist can open
Care that stays private
Follow-up signals and remote check-ins, with patient data protected while the platform runs.
- Signals aimed at the care team
- Check-ins tied to the plan
- Data and models under your control
Estimate
What could come back to your specialists?
A rough estimate to start the conversation. Set the time saved from your own validation, not from ours.
Studies per day
Minutes per study
Share supported by triage
Assumed time saved on those
Specialist time returned0hours per week
0hours per year
0full-time-week equivalents
In every engagement
Settled before a model is trained
01
The task, in writing
Who uses it, what they decide, and what the software must not decide.
02
A privacy read
If the data cannot be used, the build does not start.
03
A design you can challenge
Model, screen, and where the result appears in the workday.
04
PACS, EMR, and EHR
The chart and the worklist stay the system of record.
05
Validation first
Readers or researchers check it before anyone relies on it.
06
A person in the loop
The platform does not sign the chart.
07
Privacy rules
HIPAA, PHIPA, and GDPR, with audit and federated learning when data should not move.
08
Ownership
You keep the models and the application, with notes for your team.
FAQ
Questions, answered
A solution architect will walk through the workflow with you.
Book a free consultationWho is this for?
Hospitals, pharmaceutical companies, research institutions, and healthtech teams.
Does the platform make a diagnosis?
No. It supports the person who is accountable. Low-confidence results wait, and a specialist still decides.
Do you connect to clinical systems?
Yes. Imaging and care workflows are designed for PACS, EMR, and EHR.
Who owns the models and the data?
You do. Custom models are built for you to own. Data stays in your environment when that is the rule.
Which privacy rules are in the design?
HIPAA, PHIPA, and GDPR, depending on where the data and the users are. Acesoft is also ISO 27001 certified.
Is there a patient-facing app?
HealthApp and PostOp Monitor cover care-team and recovery use. This page is the platform behind clinical and research operations.
What happens when the model is unsure?
Low-confidence results are held in a review queue for a specialist instead of being decided by software. You set the threshold with your own readers during validation.
How do you validate it before we rely on it?
Readers or researchers check results against the success measure from the workflow, in short cycles. We track measures such as sensitivity, false alarms, time to read, and override rate.
Can data stay in our own environment?
Yes. We design for where the data lives. When sets should not move, federated learning lets the model learn without the records leaving the site.
Do you handle regulatory clearance?
Whether a tool needs clearance depends on its intended use and where it is deployed. We design validation and documentation that your regulatory team can build on, and they decide the pathway.
Explore more
Related AI services and the care products already on this site.
Let's talk
Ready to get started?
Let's build it together.
Tell us what you are working on. A solution architect will reply with a free consultation, usually within one business day.
