Clinical & Regulatory Document Processing
High Synergy Offering — Universal demand across every pharma enterprise
The Problem in Numbers
Based on internal market research. Figures are directional estimates informed by industry analysis and may vary by organisation.
50K–100K
Documents per Phase III Trial
12–18 mo
Avg. Submission Prep Time
7
Document Types in Scope
Document Types in Scope
Protocols & amendments
The master plan for a clinical trial — defines objectives, design, methodology, and any mid-trial changes.
Case report forms (CRFs)
Structured data collection instruments that capture every patient visit, lab result, and adverse event during the trial.
Informed consents
Legally required documents proving patients understood and agreed to participate in the trial.
Investigator brochures
Comprehensive summaries of all clinical and non-clinical data about the drug, given to trial investigators.
FDA / EMA submission packages
The formal regulatory dossier (eCTD format) assembled from all trial documents for market approval.
Adverse event narratives
Detailed written accounts of every serious adverse event, required for safety reporting to regulators.
eCTD modules
The 5-module electronic Common Technical Document structure mandated by FDA, EMA, and PMDA for all submissions.
How It's Processed Today
| Method | Problem |
|---|---|
| Manual review | Slow, expensive, error-prone |
| Legacy OCR | Low accuracy on handwritten / complex layouts |
| Cloud AI tools | Unpredictable per-token billing, data sovereignty risk, 3x hardware cost |
Every pharma company runs this workload. Every pharma company pays too much for it — either in human time or in cloud GPU rental priced for burst, not sustained use.
The Solution
A four-stage inference platform running on owned infrastructure
Document Asset
Management
Management
→
Document
Twin
Twin
→
Business
Twin
Twin
→
Agency
Copilot
Copilot
STAGE 1
Document Asset Management
Every document is fingerprinted, deduplicated, and version-tracked before any AI processing begins. This is the file-level control plane.
Checksum
Validation
Version Mgmt
Ingestion Governance
STAGE 2
Document Twin
The raw document is transformed into a machine-readable digital twin — a semantically rich, version-managed data representation that preserves lineage to the original asset.
OCR
VLM Extraction
Classification
Structured Data
Version Mgmt
STAGE 3
Business Twin
The document twin is elevated into business-usable intelligence — regulatory-ready summaries, cross-document insights, compliance gap analysis, and generated content.
Search
Summarisation
Generation
Gap Analysis
STAGE 4
Agency Copilot
Regulatory affairs professionals, clinical operations teams, and quality reviewers interact through guided workflows, human-in-the-loop review gates, and AI-assisted decision support.
Guided Workflows
Human-in-the-Loop
Decision Support
Proven Architecture
40–50B
Tokens / Day (Platform Capacity)
124
GPU Pods (Original Deployment)
48
Pods After Optimisation
This is the same multi-model inference pipeline topology operated at a Fortune 25 healthcare company — 40-50B tokens/day across 124 GPU pods, subsequently optimised to 48 pods without performance loss.
Model Right-Sizing Journey
| Phase | Model Size | Outcome |
|---|---|---|
| Initial deployment | 72B parameters | Baseline accuracy established |
| Optimisation round 1 | 14B parameters | Equivalent output quality, lower compute |
| Optimisation round 2 | 8B parameters | Same quality, fraction of the silicon cost |
The 5-Layer AI Cake
Who delivers what — and why both partners are essential
Layer 5: AGENCY (Application)
Prompt governance, audit trails, multi-model pipeline orchestration, compliance hooks, human-in-the-loop
INTICS
Layer 4: INTELLIGENCE (Model Serving)
vLLM / SGLang / Triton, model right-sizing (72B → 14B → 8B), silicon-aware optimisation, batch/stream scheduling
INTICS
Layer 3: IRON (Servers & Networking)
GPU-accelerated server chassis, high-bandwidth switching fabric, centralised lifecycle management
HW PARTNER
Layer 2: SILICON (Accelerators & Processors)
H100 / A100 (inference), Xeon / EPYC (preprocessing). Silicon-agnostic — workload drives selection
HW PARTNER
Layer 1: ENERGY (Power, Cooling, Colocation)
Rack density planning, thermal management, redundant power, colocation partnerships
HW PARTNER
Your hardware partner provides Layers 1–3. Intics adds Layers 4–5 — the operational IP that turns the iron into a production-grade token factory. Without Intics, the customer buys GPUs and runs notebooks. With Intics, the customer owns an enterprise inference platform.
Financial Impact
H100 Configuration — 48 Pods
$240K
Cloud / Month
$79K
Captive / Month
Mo 8–9
Break-Even
~$4.0M
3-Year Net Savings
Cost Comparison
| Line Item | Cloud (Hyperscaler) | Owned Infrastructure |
|---|---|---|
| Monthly inference cost | $240,000 / mo | $79,000 / mo |
| One-time hardware Capex | $0 (rented) | $1,750,000 |
| Monthly operations (power, colo, ops team, software, insurance) | Included in rental | $30,000 / mo |
| Cloud egress / ingress | $15,000–$30,000 / mo | $0 |
| GPU idle billing | Billed even when idle | Marginal electricity only |
Financial Outcomes
| Outcome | Value |
|---|---|
| Monthly savings | ~$161,000 / mo |
| 3-year total cloud spend | $8,640,000 |
| 3-year total captive spend | $2,844,000 + $1,750,000 Capex = $4,594,000 |
| 3-year net savings | ~$4,000,000 |
| Hardware on balance sheet | $1,750,000 depreciable asset |
Cloud GPU rental is economically designed for burst workloads. Clinical document processing is steady-state. The captive model eliminates the structural overpayment that comes from running sustained workloads on burst-priced infrastructure.
Time Savings
From months to hours — across the document processing lifecycle
6–12 mo
Faster Submission Prep
~95%
Narrative Extraction Reduction
Weeks → Hours
eCTD QC Cycle
| Process | Before | After | Improvement |
|---|---|---|---|
| Regulatory submission preparation | 12–18 months | ~6 months | 6–12 months faster |
| Single document classification | Minutes (manual) | Sub-second | Orders of magnitude |
| eCTD assembly quality check | Weeks of manual review | Hours of automated validation | Weeks → Hours |
| Adverse event narrative extraction | 4–6 hours per case | Minutes per case (batch) | ~95% reduction |
The submission preparation acceleration is the headline metric. Faster submissions = faster time-to-market = revenue measured in hundreds of millions for blockbuster drugs.
Volume & Throughput
Industrial-scale inference that justifies dedicated infrastructure
10–15B
Tokens / Day (Single Pipeline)
40–50B
Tokens / Day (Full Platform)
4
Pipeline Stages
| Metric | Capacity |
|---|---|
| Token throughput (single pipeline) | 10–15B tokens / day |
| Token throughput (full platform) | 40–50B tokens / day |
| Pipeline stages | Document Asset Mgmt → Document Twin → Business Twin → Agency Copilot |
| Pod configuration (H100, proven) | 48 pods |
| Serving evolution | 72B → 14B → 8B parameter models with equivalent output quality |
This is not a pilot. This is not a proof of concept. This is industrial-scale inference, proven at Fortune 25 healthcare, that justifies dedicated owned infrastructure.
Compliance Framing
Why captive infrastructure is not optional for pharma — it's mandatory
FDA 21 CFR Part 11
Electronic records and signatures require infrastructure the pharma company owns and controls. Cloud shared tenancy cannot guarantee this. Captive infrastructure is fully auditable by FDA inspectors.
Computer System Validation (CSV)
CSV requires static, documented infrastructure configuration. Cloud auto-scaling groups change dynamically and cannot be validated. Captive farm runs on fixed, validated hardware.
Data Sovereignty
Patient-level clinical data, proprietary trial data, and investigator information never leave the customer's firewall. Zero data egress to third-party cloud providers.
Audit Trails
Every prompt, every response, every model version, every timestamp — logged immutably. Ready for regulatory inspection at any time.
GxP Namespace Isolation
4-tier environment model (Dev → UAT → Prod → DR) with namespace isolation mirrors validated pharmaceutical IT practices. Each environment is independently configurable and auditable.
"A pharma compliance team will never approve sending patient clinical trial data to a cloud AI API. Captive inference solves this permanently."
Compliance positioning
Who Delivers What
Your hardware partner provides the bottom 3 layers. Intics owns the top 2. Together, complete.
Your Hardware Partner — Energy, Silicon, Iron
Energy
Data center power planning, rack density optimisation, colocation partnership facilitation. GPU-dense racks require thermal and power management that commodity setups cannot guarantee.
Silicon
H100 GPUs for inference. Xeon or EPYC CPUs (128-core) for the critical preprocessing tier — OCR, text normalisation, layout detection. Silicon selection is workload-driven, not vendor-locked.
Iron
GPU-accelerated servers as compute hosts. High-bandwidth switching fabric for low-latency east-west traffic between orchestrator nodes and GPU pods. Centralised lifecycle management for remote fleet operations.
Clinical document processing requires deterministic low-latency internal networking — OCR results flowing to VLM inference, flowing to NLP classification, at pod density. Enterprise-grade fabric delivers this where commodity switches cannot.
Intics — Intelligence, Agency
Intelligence (Model Serving)
Production-hardened serving via vLLM, SGLang, Triton. Model right-sizing (72B → 14B → 8B with equivalent output). Silicon-aware optimisation. Batch and stream scheduling for mixed workloads.
No ML infrastructure engineers needed. Serving layer works out of the box, battle-tested at 40-50B tokens/day.
Agency (Application)
Audit logging, prompt governance, concurrency limits, secure API routing, eventing, compliance hooks. Multi-model pipeline orchestration across all 4 stages (Asset Mgmt → Document Twin → Business Twin → Agency Copilot). Distributed orchestration topology (12-18 orchestrators managing 6-8 GPUs each).
Production scars from operating 124 pods at Fortune 25 scale — queue priority, failover, DR, prompt versioning, audit trails. Customer gets all of this on day one.
100 H100 pods reduced to 48 pods without performance loss. That optimisation is baked into the platform.
The Pitch
One sentence to carry into every pharma conversation
"You are processing hundreds of thousands of clinical documents per trial. You are either doing it manually (slow and expensive) or renting cloud GPUs (fast but 3x the cost). There is a third option: own the infrastructure, break even in 9 months, and save up to $4M over three years."
Key Numbers to Remember
$4.0M
3-Year Net Savings
Mo 8–9
Break-Even Point
67%
Cost Reduction vs Cloud
$240K → $79K
Monthly: Cloud → Captive
6–12 mo
Faster Submission Prep
40–50B
Tokens / Day Capacity
4 Stages
Asset → Doc Twin → Biz Twin → Copilot
100%
Data Sovereignty