AI for pharma — built around how your scientists and clinical teams actually work.
Custom software platforms for pharma and biotech — research workflows, clinical operations tooling, and AI-assisted analytics — engineered to sit alongside the systems your scientists, clinical operations, and diagnostic teams already run.
Drug Discovery & R&D Software
Custom software for research workflows — data lakes, knowledge graphs, ML-assisted analytics, and laboratory data management. We engineer the platform; your scientists direct the science and decide how the outputs are used.
Clinical Operations Software
Software supporting clinical research operations — cohort discovery on de-identified data, monitoring dashboards, workflow automation, and document tooling. Designed to coexist with your existing clinical systems via documented APIs your IT team controls.
Healthcare AI Software
Custom AI and ML software for healthcare and life-sciences applications — imaging pipelines, decision-support interfaces, and analytics. We build the platform with explainability, confidence reporting, and clinician-in-the-loop patterns. Clinical use, regulatory pathway, and device classification are decided by your team.
Document & Knowledge Tooling
NLP and retrieval-augmented software over your documents — protocols, internal SOPs, scientific literature, correspondence. Returns answers grounded in your sources with citations, supporting your team’s review work without replacing it.
Research Data Foundation
A data fabric across your research and operational sources — schema-level lineage, role-scoped access, and controlled-vocabulary support, so AI and analytics workloads run on data with documented provenance.
MLOps & Model Governance
Model cards, evaluation harnesses, drift detection, retraining gates, and change-control logs — engineering artifacts your QA, IT, and regulatory teams can review as part of your own validation work.
Clearer Signals for Clinical Operations
Software that sits alongside your clinical operations stack, surfacing enrollment, monitoring, and data quality signals as they happen — supporting your trial team’s decisions, not replacing them.
Diagnostic Software Built Transparently
Pathology, imaging, and decision-support software shipped with explainability hooks, confidence reporting, and clinician-in-the-loop workflows — engineered transparently so your clinical and regulatory teams can review the model behind the screen.
Engineered with audit, validation, and security awareness for regulated pharma work
Audit-Trail-Aware Engineering
We design platforms so that logging, lineage, and approval gates are first-class engineering features. Your QA team executes the validation; the platform supplies the engineering evidence.
Data Discipline That Supports Your Audit Work
Schema-level lineage, immutable audit records, controlled-vocabulary support, and database-level constraints make research and operational data traceable for your QA reviewers.
ML Lifecycle Artifacts Your QA Can Review
Model cards, training data fingerprints, evaluation harnesses, drift monitoring, and change-control logs — every model arrives with a documented lifecycle your reviewers can read.
Three pillars of pharma software on one engineering foundation.
Discovery, clinical operations, and healthcare AI software — each purpose-built — sharing the same engineering core, so your scientists, trial teams, and clinical reviewers all work from the same foundation.
Drug Discovery & R&D Software
Custom platforms for research workflows your scientists direct
- Search across literature and internal experiments
- ML-assisted analytics for property and assay data
- Knowledge graphs over your research sources
- Research data lake with documented lineage
Clinical Operations Software
Tooling that sits alongside your clinical stack
- Cohort discovery on de-identified data
- Monitoring dashboards and operational alerts
- Workflow automation for repetitive review work
- APIs and standard formats for system integration
Healthcare AI Software
Decision-support interfaces, engineered transparently
- Imaging pipelines and analytics
- Decision-support interfaces with confidence reporting
- Explainability hooks and clinician-in-the-loop patterns
- Model lifecycle artifacts your reviewers can read
One engineering foundation, three software pillars
- Shared data layer with lineage and controlled vocabularies
- Common MLOps practice: model cards, evaluation, drift monitoring
- Patient and subject data segmented and role-scoped at the application layer
- One engineering foundation across discovery, clinical, and diagnostic software
Compliance by design
Engineering artifacts for your validation work
We structure the build so your QA team has the documentation, traceability, and test evidence they need to execute their validation work. We do not perform validation on your behalf.
Data discipline as an engineering default
Schema-level lineage, immutable audit logs, controlled-vocabulary support, and database-level constraints — applied so research and operational data stays attributable and contemporaneous as your QA team reviews it.
PHI / PII segmentation
Patient and subject data is tokenized at the application gateway, scoped by role, and de-identified or synthesized for training wherever the work allows. PHI is kept out of the model layer by default.
Model lifecycle engineering
Model cards, training data fingerprints, evaluation harnesses, drift monitoring, and retraining gates — engineering practices informed by published guidance on responsible machine-learning lifecycles, including FDA GMLP principles.
Identity & access control
Standards-based identity with enforced MFA, role-scoped access for the user populations the platform serves, and least-privilege defaults across modules and APIs.
Cloud infrastructure for regulated environments
Hosted on cloud regions and configurations commonly used for sensitive data work, with private endpoints, infrastructure defined and reviewed via Terraform, and environment promotion gates your team can sign.
Audit-ready on day one
Every component is engineered with audit-trail logging, role-scoped access, lineage tracking, and lifecycle artifacts your QA, IT, and regulatory teams can use as inputs into their own validation work. Final regulatory submission, validation execution, and any clearance pathway (e.g., SaMD classification, 510(k), De Novo, PMA) remain solely the customer’s responsibility, executed by the customer’s regulatory function. Sorento Software does not represent, attest, or warrant compliance with any regulatory framework on behalf of any customer.
Partner agreements in place
Faster Time-to-Decision in R&D
Workflows that compress the read-write-decide loop for your scientists — search across literature, internal experiments, and analytics in one place, with the underlying data lineage preserved.
Clearer Signals for Your Trial Team
Dashboards, alerts, and analytics that surface enrollment, monitoring, and data quality signals as they happen — supporting your trial team’s decisions instead of replacing them.
Transparent Diagnostic and Decision-Support AI
Imaging and decision-support software built with explainability hooks, confidence reporting, and clinician-in-the-loop patterns — so your clinical teams understand what the model is suggesting and why.
PHI / PII Handled as a First-Class Engineering Concern
Patient and subject data is segmented, tokenized, and access-scoped at the application layer by default. Training pipelines use de-identified or synthetic datasets wherever the work allows.
No Rip-and-Replace of Your Core Systems
We sit alongside your existing research and clinical systems via documented APIs and standard data exchange formats — adding software capability without forcing you to displace the systems your teams already rely on.
Our Implementation Process
Discovery, Use-Case Triage & Engineering Framing
We map your research, clinical, or diagnostic workflows; rank candidate software use cases by feasibility and data readiness; and frame the engineering and integration shape before scoping the build. Regulatory pathway decisions stay with your team.
Architecture & Engineering Plan
Design the system architecture, data fabric, model lifecycle, and engineering plan — including audit-trail design, role-scoped access, MLOps practices, and security posture — alongside your IT, security, and QA stakeholders.
Build & Iterate
Iterative full-stack development of the platform — data pipelines, model services, UI, and governance tooling — with engineering artifacts (test coverage, evaluation results, change logs) captured as part of the build.
Integration & Handoff to Your QA / Regulatory Function
Connect to your existing source systems via documented APIs and standard data formats, run end-to-end UAT with your R&D and clinical operations stakeholders, and assemble the engineering documentation set your QA and regulatory teams need as inputs into their own validation work.
Deployment, Hypercare & Lifecycle Operations
Phased rollout to scientists, clinical operations, or diagnostic users. An initial hypercare period covers monitoring, model drift response, retraining considerations, and change-control reviews so the platform stays in a known state as the science evolves.
Frequently Asked Questions
Are the AI outputs you deliver ready for our regulatory submissions?
We are a software engineering partner. Validation execution, submission readiness, and regulatory acceptance are owned by your QA and regulatory functions — we do not perform validation or make submissions on your behalf. What we deliver is the platform plus engineering artifacts your team uses as inputs into their own work: audit-trail logs, data lineage, model cards, evaluation reports, and change-control history. Your QA and regulatory teams decide how those artifacts are used.
How do you handle PHI and subject data in clinical AI workflows?
Subject and patient data is tokenized at the application gateway, segmented by sensitivity, and access-scoped by role. Training pipelines use de-identified or synthetic datasets wherever the underlying work allows; PHI is kept out of the model layer by default. Every action is logged with actor, timestamp, resource, and outcome so your reviewers can trace activity through the platform. The engineering practices we apply are aligned with what customers in regulated environments typically expect, but we make no compliance certifications on your behalf.
How does this fit alongside our existing research and clinical systems?
We build platforms designed to coexist with the research, clinical, laboratory, and data systems you already run — using documented APIs and standard data exchange formats (for example, HL7 FHIR R4, DICOM, and CDISC ODM where applicable). Your IT and integration teams own the actual connections into your validated systems. We do not claim partnerships, certifications, or pre-built integrations with any third-party vendor.
Can you build diagnostic software? What about FDA SaMD considerations?
We do not classify, submit, or seek clearance for medical devices on behalf of customers. What we build is custom AI and ML software for healthcare and life-sciences workflows — decision-support interfaces, imaging pipelines, analytics — engineered transparently with explainability hooks, confidence reporting, and clinician-in-the-loop patterns. Final device classification, regulatory pathway, and any FDA interaction are owned and executed by your regulatory function. Our engineering practice is informed by published guidance on responsible machine-learning lifecycles; the regulatory determinations are yours.
What does a typical engagement look like, and how do you scope it?
Engagement scope, timeline, and investment vary by program and are defined during discovery — we do not quote fixed durations or fixed regulatory outcomes on a public page. Discovery is where we map your workflows, audit data readiness, and frame the engineering and integration shape before any production-bound code is written. After discovery, the build is typically phased so the highest-priority capability goes live first and your team can review the platform before later phases land.
How do you address hallucination and black-box risk for clinical and regulatory work?
Our generative and ML pipelines are grounded in retrieval over your verified internal sources (protocols, SOPs, scientific literature, your own documents), return citations alongside answers, and ship with evaluation harnesses that track factuality and drift over time. Diagnostic and decision-support models ship with explainability hooks, confidence reporting, and clinician-in-the-loop patterns. Every model has a documented lifecycle — model card, training data lineage, evaluation results, change history — that your reviewers can read.
Bringing AI into a regulated pharma workflow?
Book a free 30-minute discovery call. We will review your software needs across discovery, clinical operations, or diagnostic tooling, talk through the engineering and integration shape, and outline a realistic scope. Regulatory pathway decisions remain with your team.