AI-Driven R&D on Azure
Executive Summary
A global process manufacturing enterprise partnered with Synaptron to modernize its fragmented R&D environment. The objective was to reduce cycle time for new product development, enable reuse of scientific knowledge, and improve lab-to-market translation. Synaptron built a secure, Azure-native R&D acceleration platform integrating generative AI, document intelligence, and workflow automation. The result:
- 35% reduction in time-to-insight across formulation and test cycles
- 70% faster access to historical experimental data
- 25% more efficient cross-team collaboration between formulation, testing, and regulatory units
- Full compliance with IP protection and data security protocols
This platform now powers AI-assisted decision-making and experimentation across multiple research teams globally.
Challenge
Siloed Research Data, Manual Workflows, and Knowledge Loss
The client’s R&D division was dealing with significant bottlenecks:
Scattered Document Storage Hindering Reuse
Unstructured Technical Documents (test reports, lab notes, patents, regulatory filings) were stored across local systems and folders, making reuse nearly impossible
Redundant Experiments from Poor Discoverability
Repeated experiments occurred due to inability to find prior attempts or formulations quickly
Manual Data Extraction Slowing Insights
Manual synthesis of data from PDFs, scanned lab books, and emails took hours per query
Lack of Unified Research Repository
No single source of truth across formulations, test outcomes, and literature reviews
Compliance Risks from Missing Traceability
Compliance risk due to lack of traceability and version control for research records
The R&D leadership wanted a solution that would unlock historical insights, support intelligent experimentation, and integrate into Microsoft Azure cloud ecosystem for scalability, security, and enterprise alignment.
Solution
Azure-Based R&D Acceleration Platform with AI & Automation
Synaptron designed and deployed a secure, modular, containerized solution stack hosted on the client’s Azure tenancy. The solution combined document AI, LLMs, and search to drive real-time intelligence and compliance in research operations.
- Document Intelligence Pipeline
- Deployed Azure Form Recognizer and Synaptron’s proprietary classifiers to extract structured metadata (formulation, test method, outcome, parameters) from over 150,000 PDFs including patents, lab reports, and trial summaries
- Enabled semantic indexing and intelligent tagging for downstream AI models
- LLM-Powered Research Assistant (Private GPT)
- Used Azure OpenAI with fine-tuned prompts to let scientists ask natural language questions (e.g., “Show all corrosion tests using surfactant X in alkaline pH range”)
- Delivered curated, cited answers in seconds—drastically reducing knowledge discovery time
- Secure Containerized Workflow Automation
- Built containerized services for ingesting and routing documents, updating formulations, triggering alerts on conflicting test data
- Integrated with SharePoint, Teams, and internal LIMS (Lab Information Management Systems)
- Role-Based Access and Audit Logging
- Enforced strict access controls, data masking, and audit logging aligned to global R&D and IT compliance standards
- Azure Key Vault and Defender used to secure AI endpoints and sensitive content
Outcome
Faster Innovation with AI-Augmented Research Workflows
The platform unlocked immediate and measurable value:
- 35% Faster Time-to-Insight: Literature and internal trial review cycles reduced from days to hours
- 70% Faster Historical Data Access: Researchers accessed 5+ years of legacy results instantly without needing manual coordination
- Improved Decision Accuracy: Scientists reported fewer redundant trials and higher confidence in cross-referencing prior findings
- Collaboration Efficiency Boost: Multi-location teams collaborated on shared insights and formulations via integrated dashboards and LLM outputs
- Compliance and IP Traceability: Every research object is now timestamped, versioned, and linked to user action trails—meeting internal and external audit norms
- End-User Satisfaction: Researchers rated the platform 4.8/5 for usability and relevance, citing it as “a personal R&D assistant”
Future
Toward Autonomous R&D Environments
Building on this success, Synaptron and the client are now co-developing:
Digital Twin of R&D Processes
to simulate experiment outcomes before execution
Federated Model Learning
to continuously improve AI with multi-tenant, privacy-preserving learning
Integration with Generative Design Systems
to auto-suggest new formulations and test matrices
Automated Regulatory Dossier Generator
using AI-extracted references and output summarization
Real-Time Sensor Integration
from pilot plants to close the feedback loop between lab and field