The review that used to take a week now takes minutes
Pharmaceutical MLR teams still reconcile every promotional PDF against an Excel claims matrix, the required ISI, and a reference list, by hand. ARK runs those three checks in one pipeline and attaches evidence to every flag.
≤ 2 min per document · 100% holdout precision · full decision trace · managed secrets
Page 2 of 6
Findings · 3 of 14
“Clinically proven fat reduction” is not in the approved matrix.
Efficacy statement on p.2 has no resolving citation marker.
Required safety block matches the consumer / digital template.
The problem
Three checks. One spreadsheet. A calendar week.
A reviewer opens the PDF, the approved claims matrix, the ISI required for that product and tactic, and the reference list. They hunt for off-matrix claims, missing safety language, and citations that do not resolve. One miss delays a launch or invites a letter. At 20 to 40 assets a week, the work does not scale.
Promotional PDF
- Headline claim
- Body copy · p.2
- ISI block
- References
Claims matrix.xlsx
- 412 approved rows
- Audience · qualifier
- Off-matrix = reject
ISI + citations
- HCP vs consumer
- Channel × tactic
- Does ¹ resolve?
Human MLR first pass
Ctrl+F, email threads, 5 to 7 days in flight
Claims vs. the matrix
Every promotional sentence is hunted against hundreds of approved rows in Excel, including wording, audience, and qualifiers.
ISI vs. the required template
Safety language must match the template for product, audience, channel, and tactic. Multi-product assets multiply the checklist.
Citations that actually resolve
A superscript that does not land on a real reference is still a finding. Reviewers walk the list by hand.
How we solved it
Parse once. Retrieve the matrix. Reason once. Leave a trace.
ARK 2.0 is a measured pipeline, not a chatbot pasted onto a PDF. Datalab parses the asset once. Hybrid retrieval finds the closest approved claims before the model speaks. The model reasons once. Every finding keeps a paper trail a reviewer can defend.
Stage 01 · Ingest
PDF lands in object storage. FastAPI validates, writes the document row, and enqueues an ARQ job on Redis.
Hybrid matrix retrieval. The model never starts cold.
The value
Hours back to the reviewer. A paper trail for the record.
ARK is decision support. A human still signs off. What changes is the first pass: measured, evidence-linked, and fast enough to keep up with 20 to 40 assets a week.
Holdout set · May 2026 · Gate 2 passing
0%
Precision
0.0%
Recall
0.0%
F1
Scored on a held-out validation split that is never used to tune prompts. Production target remains ≥95% precision and recall. The harness existed before the pipeline did.
Manual first pass
Before
5 to 7 days
After
minutes
Reviewers used to reconcile PDF, Excel, ISI, and refs in flight.
Vs. ARK 1.0 processing
Before
5 to 10 min
After
≤ 2 min
Design targets: p50 ≤ 90s, p95 ≤ 180s end-to-end.
Reasoning architecture
Before
6 agents
After
1 pipeline
One coordinated pass plus targeted validation. No fragile handoffs.
Why it is different
Not a chatbot on a PDF. A retrieval-backed review engine.
Generic models summarize. Spreadsheets do not scale. ARK 1.0 fragmented the job across six agents. This stack retrieves the approved matrix first, reasons once, and stores the evidence.
Claims vs approved matrix
ISI vs required template
Citation integrity
Evidence / audit
Processing time
Architecture
The stack. Built to wait efficiently on parse, embed, and reason.
Interface
- Next.js 16
- React 19
- Tailwind + shadcn
- pdf.js workspace
API & workers
- FastAPI · Python 3.12
- ARQ on Redis
- Pydantic v2
- async end-to-end
Intelligence
- LLM reasoning
- Voyage embed + rerank
- Datalab Accurate
- Langfuse traces
Data & ops
- Postgres 18 + pgvector
- S3-compatible storage
- Railway
- Managed secrets
System context. Reviewers stay in ARK; models stay behind the API.
ARK 2.0 platform
Next.js workspace → FastAPI → ARQ workers → Postgres + object storage
- Datalab Accurate
- LLM reasoning
- Voyage AI
- Langfuse
The three MLR checks, in one workspace
Built for teams who live in the claims matrix, ISI footnotes, and reference lists, and need every flag to jump back to the source.
Single AI pipeline
One coordinated review pass plus targeted validation. Fast, consistent results without fragile handoffs between tools.
Claims matrix alignment
Every promotional claim matched to approved copy or flagged as ad-hoc, with confidence and source text preserved.
ISI you can defend
Extract ISI blocks and compare to the required template for product, audience, channel, and tactic, including multi-product assets.
Reference integrity
Verify citations for claims that need substantiation and surface references that never appear in the body.
Traceable findings
Click any flag to jump to page and paragraph. Accept, reject, or comment, then export a clean report for the record.
Modern parsing
Structured document ingestion with durable storage of originals and parsed output for replay and audits.
From upload to a defensible sign-off
Agency submits, the pipeline runs, the reviewer decides. Designed for p50 ≤90s and p95 ≤180s processing. The human still owns the approval.
Ingest
Upload PDFs up to 100 MB, text-native or scan-ready. Originals and parsed structure are stored for audit.
Analyze
High-accuracy parsing and coordinated analysis extract claims, ISI, and references in one pass.
Review
Side-by-side document and findings. Jump to evidence, resolve flags, and collaborate with clear ownership.
Report
Export asset-level reports and feed dashboards for trends by product, channel, and rejection reason.
Security that passes IT review
ARK 2.0 is designed for environments where marketing operations meet strict infosec and compliance expectations.
Enterprise auth
SSO via SAML or OIDC with role-based access: reviewer, agency, admin, and read-only. Every action is attributed in the audit log.
Secrets & data
No hardcoded keys: credentials live in a managed secret store. Encryption in transit (TLS 1.3) and at rest (AES-256).
Logging and traces
Structured logs, request tracing, and a named audit log for reviewer and admin actions. Every finding keeps a decision_trace.
Bring the matrix, the ISI, and the citations into one pass
Same regulatory rigor. Minutes instead of a week. A decision trace reviewers can stand behind. ARK does not approve assets on its own.