Product quality is determined by the interaction of raw materials, manufacturing processes, equipment performance, and environmental conditions — data that lives across 8+ disconnected systems at most pharmaceutical manufacturers. Current approaches, including annual CPV reviews, detect quality risks months after they emerge. Celestara changes the equation: AI agents operating on a unified quality ontology continuously assess product robustness, predict quality risks before they materialise, and deliver actionable mitigation intelligence across every product and every site.
Key Highlights
Regulatory Context
Process performance qualification studies for US-market products did not include acceptance criteria for intra-batch or inter-batch variability. PPQ batches were approved without evaluating sources of potential variation in CQAs including assay, impurities, and pH. Sampling consisted of composite results rather than enhanced sampling designed to detect variability — the firm could not demonstrate its processes were capable of consistently meeting quality standards.
From May 2019 to June 2024, approximately 27 OOS results were obtained for a single API during in-process and release testing, leading to reprocessing of multiple batches. Although individual corrective actions were identified, no holistic review or effective assessment was performed to ensure the overall manufacturing process remained under adequate control. OOS investigations failed to capture critical manufacturing dates and product complaint history.
Hardness and thickness specifications for a tablet product were changed based on a challenge study performed on only one of multiple marketed strengths. Trial sizes evaluated were not representative of commercial batch sizes. The effectiveness check evaluated only six batches across limited strengths, with no evaluation performed for several US-marketed strengths — creating unquantified risk for products whose robustness was never assessed.
The firm relied on supplier certificates of analysis for incoming shipments of high-risk components without establishing the reliability of the supplier’s analyses through validation at appropriate intervals. No identity or quantitation testing was performed to detect potential contaminants. Over three years, hundreds of lots of drug products containing these materials were manufactured and distributed with expiration dates of one to four years.
Upstream drug substance manufacturing systems had unmitigated risk in assurance of process performance and product quality. Evidence of eleven contamination events was documented in one building from April 2022 to February 2024, with twelve additional events in another building from January 2020 to November 2023. The firm failed to identify and address systemic deficiencies in a timely manner. This was a repeat observation from a prior FDA inspection.
The firm’s statistical quality control criteria failed to include appropriate acceptance and rejection levels. During batch review, approximately 85 alert excursions and 36 action-level excursions were missed in particle monitoring data. A batch was partially released without justification, and the investigation failed to account for all excursion events. Reject limits for critical defects were never established for multiple sterile products.
The Problem
Product Quality & Risk Assessment in Celestara
Celestara connects to every operational system through a purpose-built integration pipeline that normalizes heterogeneous data — structured database records, time-series sensor data, unstructured batch narratives, and supplier quality documents — into a unified data foundation. Data flows continuously, not in annual retrospective batches.
Celestara maintains a quality ontology — a connected knowledge model that maps every product to its critical quality attributes, process parameters, raw materials, equipment, and historical quality events. When a material lot changes, the ontology traces which products, processes, and CQAs could be affected. This contextual reasoning is what makes predictive quality assessment possible.
AI agents perform continuous statistical analysis across every product — Cpk/Ppk trending, multivariate analysis, control charts — using the ontology to contextualize results. A capability drift in dissolution is automatically correlated with changes in raw material lots, equipment parameters, or environmental conditions. Analysis happens continuously, not annually.
Each product receives a continuously updated robustness score derived from process capability indices, material quality signals, deviation trends, stability data, and manufacturing variability. Celestara aggregates signals across the quality ontology to identify products approaching quality boundaries before they cross them — turning reactive quality events into preventable ones.
Celestara correlates risk signals across products, sites, and supply chains. When a raw material supplier’s quality drifts, every product using that material is flagged. When a process parameter shift at one site affects quality, the system checks whether the same risk exists at other sites. Quality leaders see a single dashboard that ranks products by risk and recommends specific mitigation actions.
The Solution
Purpose-built tools that address both the FDA compliance gaps and the operational barriers to effective product quality assessment and proactive risk management.
Celestara connects every quality-relevant system through an enterprise integration pipeline and builds a continuously updated quality ontology — a connected knowledge model that maps relationships between products, processes, materials, equipment, and quality events. AI agents equipped with domain-specific tools query this ontology to answer questions that no single system can answer alone: which material lots correlate with yield dips, which process parameters predict stability risk, and which products share equipment-related failure modes.
Every product receives a continuously updated robustness score derived from process capability indices, material quality signals, deviation trends, stability data, and manufacturing variability. Risk scoring agents aggregate signals across the quality ontology to identify products approaching quality boundaries before they cross them — enabling proactive mitigation that prevents write-offs, supply disruptions, and regulatory findings.
Deploy consistent batch record templates, in-process control workflows, and quality data collection standards across all manufacturing sites. When every facility captures data in the same format, at the same granularity, and against the same specifications, cross-site quality comparison becomes meaningful and the integration pipeline operates without site-specific data transformations.
Monitor how FDA cites product quality assessment failures across the industry. Understand which process validation gaps, statistical deficiencies, and risk management shortcomings draw 483 observations and warning letters — so your team can proactively align practices with current inspector expectations before the next audit.
Results
Real results from organisations using Celestara for continuous product quality assessment and predictive risk management
Next Step
Stop discovering quality risks after the write-off. Celestara AI agents continuously assess product robustness across your entire portfolio — correlating process data, material attributes, and quality events through a unified quality ontology to predict risks before they materialise. Shift from reactive compliance to predictive quality intelligence.
Get Started