Pharma · Research & Discovery

Personalized Solutions for Pharma Research & Discovery

We help research and discovery teams accelerate early-stage workflows through tailored organizational, digital and technical solutions.

3× mehr Kandidaten in der Frühentwicklung AI mRNA Facility · German biotech client

01 · the phase

Early research runs on data that lives in a dozen instruments and formats, and on iteration cycles that are slower than the science deserves. We know the typical bottlenecks, siloed lab data, manual handoffs between bench and informatics, slow loops between hypothesis and assay readout, and we prove which one is worth moving with a 4-week PoC.

02 · what we hear

Key challenges, and how AI-driven transformation helps.

  • Research-Workflows
    Challenge

    Ergebnisse landen in einem System, Proben werden in einem anderen verfolgt, und die Auswertung passiert in einer Excel-Datei auf irgendeinem Laptop. Von der Hypothese zum Readout heisst, dieselben Daten mehrfach neu einzutippen, und niemand kann sagen, wo die Woche geblieben ist.

    How we help

    Wir machen greifbar, welche Research-Workflow-Constraints Discovery am stärksten bremsen, und liefern einen praktischen Plan, den grössten Hebel zu beseitigen.

  • Datenintensive Forschung
    Challenge

    Eine Kollegin hat in einem früheren Projekt ein ähnliches Target charakterisiert, aber diese Arbeit zu finden heisst herumfragen statt suchen. Sequenz- und Strukturdaten liegen in projektbezogenen Ordnern, also wird derselbe Assay erneut gefahren, weil niemand wusste, dass es ihn schon gibt.

    How we help

    Wir beschleunigen datenintensive Forschung, indem wir vorhandenes Wissen demokratisieren, Knowledge Graphs aufbauen und AI-gestützte Analytik einsetzen, um Timelines zu verkürzen und die Präzision zu erhöhen.

03 · use cases

What a 4-week PoC could target.

FAIR-ready research data model

Know what data you have, where it sits, and how it should flow.

We run an as-is analysis of current spreadsheets, formats and workflows, surface data gaps and inconsistencies, and design a conceptual to-be data model and end-to-end data-flow framework, agreed with your scientists in alignment workshops and documented ready for digitalization.

What good looks like

  • Shared, FAIR-ready data blueprint across the research workflow
  • Aligned definitions, trusted data and clearer ownership
  • A model designed for structured, AI/ML-ready capture

AI & automation use-case factory

A continuous pipeline of digitalization and automation wins, on a 90-day rhythm.

We stand up a value-based delivery factory: each cycle discovers, prioritizes and delivers high-impact use cases as reusable building blocks. You keep final prioritization authority; a quarterly steering reviews the last 90 days and decides the next.

What good looks like

  • Two enabling layers, data infrastructure for access/search, data intelligence for AI structuring
  • A steady stream of delivered scientist-workflow improvements
  • Transparent, value-ranked prioritization and adoption

Self-learning lab platform

Connect experiments, data and AI to accelerate candidate selection.

We architect and implement a self-learning platform that integrates experiments, data and AI, for example connecting synthetic biology, IVT and immunomodulation, to surface correlations between sequence design and cellular expression and speed up screening.

What good looks like

  • Faster candidate selection in early development
  • More scientist time for creative work vs. repetitive tasks
  • A high-throughput, reusable screening setup

Searchable cross-site sample access

Trace, access and reuse samples across sites without the hunt.

We concept, design and deliver a digital biorepository, a virtual + physical solution to handle, search and retrieve biosamples securely across sites, with an integrated request → register → store workflow and a FAIR-aligned sample data model.

What good looks like

  • Remote, centralized access to samples
  • Faster data access, lower experiment time and cost
  • A standardized, FAIR-aligned sample data model

Research tooling evaluation

Buy, build or skip, a vendor-agnostic read on your lab and data stack.

We assess your current tooling against pre-defined criteria and recommend what to buy, build or leave, with a costed view and no SKU of our own to defend.

What good looks like

  • A clear buy / build / skip recommendation
  • Costed options tied to your bottleneck
  • No vendor lock-in

04 · proof

Work, re-framed for this phase.

AI mRNA Facility · German biotech client

A self-learning lab platform that connected experiments, data and AI, and lifted early-development candidate output. Discovery acceleration, proven in the lab.

Background
A research organisation expanded rapidly through multiple acquisitions, driven by funding and portfolio diversification. The brief: maximise the value of those acquisitions and integrate the teams without losing momentum on candidate selection.
Solution
Personalized Solutions to structure the integration. The flagship project: an mRNA facility for in-vivo screening to accelerate candidate selection. A self-learning platform connecting synthetic biology, IVT and immunomodulation, surfacing correlations between sequence design and cellular expression.
Results · 12 mo. after engagement
300% increase in early-development candidates vs. the year before. 40% of scientists' time freed for creative work rather than repetitive tasks.
inite's role
inite owned full implementation, project lead, engineering and implementation monitoring. Architected the high-throughput solution. Negotiated with stakeholders to find the best path to the goal.

“inite's strength was to create and implement systems and structures that helped us be more efficient and effective. The ability not to force these structures but align with stakeholders made them a trusted advisor for us.”

COO · Global Biotech Company

Digital Biorepository · Swiss pharma client

Making distributed research samples searchable and FAIR, the data backbone that early discovery runs on.

Background
Cross-site collaboration on bio-sample management, biosamples (cells, proteins, plasmids, antibodies) distributed across multiple locations with no clear structure. Tracing, accessing and collecting the right samples was slow; scientific productivity was capped.
Solution
A global virtual + physical solution to handle, search and retrieve biosamples. Secure cross-site sharing, AI-contextualised digital + physical storage, and an integrated request → register → store workflow.
Results · 12 mo. after engagement
Remote and centralised access to bio samples. Faster data access. Lower experiment time and cost. ~35% efficiency improvement.
inite's role
inite concepted, designed and managed the full DBR implementation across life-science, IT and business domains. Delivered a standardised Bio-Sample Data Model aligned with FAIR principles. Aligned multiple sites and functions on a single workflow.

“Inite positively influenced a change of mindset and supported us to approach the task with fresh thinking. Their approach allowed the team to imagine solutions in an unlimited way rather than being held back by constraints and obstacles.”

Global Process Owner, Biosample Management · Global Pharma Company

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