Case Study: Global biopharma cuts time-to-insight from 30 days to under 48 hours with agentic claims analytics

A top-10 biopharma’s oncology unit needed defensible answers from four years of U.S. medical and pharmacy claims—tens of millions of rows spanning treatment lines, care settings, and payer dynamics.

The mandate was straightforward but heavy: size the addressable market; map real-world regimen sequences; understand variation by prescriber specialty, geography, and payer mix; and surface patient-journey frictions that could inform commercial strategy.

Historically, the team stitched these insights together through month-long analyst cycles: extract subsets of claims, run ad-hoc scripts, QC the joins, rebuild visualizations for each new question, and repeat whenever leadership needed a follow-up cut. Business leaders also wanted a way to interrogate the findings themselves—ask “what if?” in plain English without waiting for the next analyst run—while keeping an audit trail that would stand up to internal and external scrutiny.

In short: faster analysis, higher confidence, and self-service exploration, all without sacrificing methodological rigor.

We deployed an agentic workflow that automates the heavy lifting end-to-end. Purpose-built analytics agents ingested the full four-year dataset, orchestrating tasks for cohort definition, line-of-therapy reconstruction, regimen sequencing, prescriber-mix profiling, payer blend analysis, and geographic normalization.

Each agent produced traceable artifacts—queries, intermediate tables, QC checks, and narrative rationales—so teams could see how every metric was derived and confidently reproduce it. On top, we layered an interactive web experience that paired dynamic dashboards with a natural-language Data Q&A assistant.

Stakeholders could click through standard views (market size, regimen pathways, specialty and setting of care, payer and geography) and then ask follow-ups like “show second-line switches for metastatic patients treated in community settings, filtered to commercial payers,” receiving both charts and a plain-language explanation of the method and caveats.

Because the agents retain context across sessions, the system learned which cuts and diagnostics the brand team repeatedly requested and began surfacing them proactively—shortening the path from question to answer with every use. Within the first day, the oncology unit had a story-ready insight pack; within three days, they had a production dashboard and Q&A layer in front of leadership.

Time-to-insight dropped by more than 90%: analyses that previously took ~30 days were reliably turned around in under 48 hours, with most first-pass answers available the same day.

The self-service layer materially reduced back-and-forth cycles; marketers and medical leads could pressure-test hypotheses in meetings, drilling from headline trends into regimen details without adding work to the analyst queue.

Just as importantly, the auditability of the agentic workflow increased trust: every number came with a lineage, every chart with an explanation, and every comparison with a reproducible query.

The oncology unit used the results to refine HCP targeting and reallocate spend toward segments with cleaner access and higher evidence of benefit, while the insights function adopted the approach as a template for adjacent brands. What began as a one-off claims analysis became a durable capability: a context-aware system that compounds value over time—learning the organization’s questions, aligning to its quality bar, and keeping the decision loop measured in hours, not weeks.