Pharma IT Consolidation
Enterprise Catalyst Offering — From fragmented cloud spend to one owned platform
The Problem in Numbers
Based on internal market research. Figures are directional estimates informed by industry analysis and may vary by organisation.
5–8
Departments Renting GPUs Independently
15–30%
Avg. GPU Utilisation per Dept.
~Zero
Visibility into Total AI Spend
5–8x
Duplicate Governance Overhead
The Fragmentation Tax
Independent Cloud Rental
Each department negotiates its own cloud contract. No volume leverage. No shared infrastructure. Total enterprise spend is 2-3x what a consolidated model would cost.
GPU Over-Provisioning
Each department provisions for its own peak demand. Average utilisation is 15-30%. The remaining 70-85% is paid for but idle.
Duplicate Governance
Every department undergoes its own security review, compliance audit, and vendor assessment. The same work is repeated 5-8 times across the enterprise.
Shadow AI
Ungoverned workloads on unapproved cloud accounts. No audit trail, no compliance coverage, no visibility. A regulatory inspection waiting to happen.
No Cross-Dept. Reuse
Models trained by one department are invisible to others. No shared model registry. No knowledge transfer. Redundant effort across the enterprise.
CFO Blindness
No single line item for "enterprise AI inference." Costs scattered across departmental budgets. Strategic investment decisions are impossible without visibility.
The Solution
One owned platform. Namespace-isolated per department. Every workload. One investment.
Workload
Onboarding
Onboarding
→
Shared
Intelligence
Intelligence
→
Enterprise
Ops Twin
Ops Twin
→
Department
Copilots
Copilots
STAGE 1
Workload Onboarding
A new department goes from "we need AI inference" to "we have a production namespace" in days — not the 4-8 weeks of cloud procurement, security review, and compliance approval.
Namespace Provisioning
Access Governance
Workload Profiling
Resource Allocation
STAGE 2
Shared Intelligence Layer
Multi-tenant model serving across the enterprise. NLP for PV, VLMs for R&D, generative for Medical Affairs, classification for Quality — all on shared infrastructure with silicon-aware scheduling.
Multi-Tenant Serving
Shared Model Registry
Silicon-Aware Scheduling
Cross-Dept. Reuse
STAGE 3
Enterprise Operations Twin
The CIO sees one platform. The CFO sees one cost center with clear departmental allocation. Compliance sees one audit trail. No more manual aggregation across cloud accounts.
Centralised Monitoring
Cost Allocation
Utilisation Dashboards
Capacity Planning
STAGE 4
Department Copilots
Each department gets its own interface tailored to its workflow — PV gets safety triage, R&D gets experiment queues, Medical Affairs gets content workflows, Quality gets batch review pipelines.
PV Triage
R&D Experiments
MedAffairs Content
Quality Review
Proven Architecture
40–50B
Tokens / Day (Platform Capacity)
124 → 48
Pod Optimisation Journey
72–100+
Enterprise Target Pods
The 4-tier environment model (Dev → UAT → Prod → DR) with namespace isolation operated at a Fortune 25 healthcare company — 40-50B tokens/day across 124 GPU pods. The same architecture that served one department at Fortune 25 scale now serves the entire pharma enterprise.
The 5-Layer AI Cake
Who delivers what — at enterprise scale
Layer 5: AGENCY (Department Copilots)
PV triage, R&D experiment queues, MedAffairs content, Quality batch review — tailored per department, governed centrally
INTICS
Layer 4: INTELLIGENCE (Shared Model Serving)
Multi-tenant vLLM / SGLang / Triton, shared model registry, silicon-aware scheduling across all departments
INTICS
Layer 3: IRON (Multi-Rack Enterprise Infra)
Multi-rack GPU server deployment, network micro-segmentation for dept. isolation, centralised lifecycle management, enterprise switching fabric
HW PARTNER
Layer 2: SILICON (Full Portfolio)
H100 (demanding models), A100 (general), NVIDIA Hopper (cost-optimised), Xeon / EPYC (preprocessing). Per-workload selection.
HW PARTNER
Layer 1: ENERGY (Enterprise-Scale Power)
150-300kW+ for 72-100+ pods, colocation with pharma-grade SLAs, redundant power architecture
HW PARTNER
At enterprise scale, every layer intensifies. Energy needs serious power planning. Silicon requires a mixed portfolio. Iron means multi-rack deployments with enterprise-grade micro-segmentation. Intelligence serves dozens of concurrent workloads. Agency delivers department-specific copilots on one governed platform.
Financial Impact
From single-department pilot to enterprise-wide platform
Entry: H100 — 48 Pods (Single Department Pilot)
$240K
Cloud / Month
$79K
Captive / Month
Mo 8–9
Break-Even
~$4.0M
3-Year Net Savings
| 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 | 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 |
Enterprise Scale: 72–100+ Pods (Multi-Department Platform)
Enterprise-scale projections based on multi-department consolidation. Per-pod economics are proven at Fortune 25 scale. Enterprise-wide figures are modelled from those proven unit economics applied across 5-8 departments.
$1.5–3M
Fragmented Cloud / Year
$850–950K
Consolidated / Year
70–85%
GPU Utilisation (vs 15-30%)
$1.75–3M+
Hardware Investment
| Line Item | Fragmented Cloud (5-8 Depts) | Consolidated Captive |
|---|---|---|
| Total enterprise AI inference spend | $1,500,000–$3,000,000 / yr | $850,000–$950,000 / yr |
| GPU utilisation rate | 15–30% | 70–85% |
| Governance overhead | Multiple accounts, contracts, reviews | One platform, one posture |
| Hardware Capex | $0 (rented) | $1,750,000–$3,000,000 |
| Hardware on balance sheet | $0 | $1,750,000–$3,000,000 depreciable |
| Break-even | Never (perpetual Opex) | Month 8–11 |
| Enterprise Financial Outcome | Value |
|---|---|
| Annual savings vs fragmented cloud | $550,000–$2,050,000 / yr |
| 3-year net savings | $1,650,000–$6,150,000 |
| GPU utilisation improvement | 2–5x (from 15-30% to 70-85%) |
| Hardware investment | $1,750,000–$3,000,000+ (largest of all offerings) |
The consolidation multiplier: The hidden cost of fragmented cloud GPU rental is not just the per-hour price — it's the governance overhead, the duplicate security reviews, the wasted utilisation, and the lack of visibility. Consolidation solves all of these simultaneously. And the hardware sale is 2-3x larger than a single-department deployment.
Time Savings
The biggest savings are in friction that disappears
Weeks → Days
New Workload Provisioning
Weeks → Hours
Compliance Audit Prep
Months → Days
Dept. AI Onboarding
| Process | Before | After | Improvement |
|---|---|---|---|
| New AI workload provisioning | 4–8 weeks (procurement, security, compliance per dept.) | Days (namespace allocation) | Weeks → Days |
| Cross-department model sharing | Not possible (siloed accounts) | Enabled (shared model registry) | Zero → Enabled |
| IT governance reporting | Manual aggregation across accounts | Single platform dashboard | Weeks → Real-time |
| Compliance audit preparation | Weeks (logs from multiple providers) | Hours (centralised audit trail) | Weeks → Hours |
| New department onboarding to AI | Months (full cloud setup cycle) | Days (namespace + access governance) | Months → Days |
The biggest time saving is invisible: elimination of the 4-8 week procurement cycle every time a new department wants AI inference. With a consolidated platform, it's a namespace allocation — done in days. This removes the friction that prevents departments from adopting AI at all.
Volume & Throughput
One platform serving every inference workload in the enterprise
5–8
Departments Served
40–50B+
Tokens / Day (Enterprise)
72–100+
Enterprise Pod Count
Dozens
Concurrent Pipelines
| Metric | Capacity |
|---|---|
| Departments served | 5–8 (R&D, PV, Quality, Medical Affairs, Sales, Supply Chain, IT, Commercial) |
| Total enterprise token throughput | 40–50B+ tokens / day |
| Pod configuration (entry) | 48 pods (single department pilot) |
| Pod configuration (enterprise) | 72–100+ pods (multi-department platform) |
| Namespaces | Dev / SIT / UAT / Perf / Prod / DR per department |
| Concurrent inference pipelines | Dozens (each dept. running independent models) |
| Model diversity | NLP (PV), VLM (R&D), Generative (MedAffairs), Classification (Quality), Analytics (Commercial) |
| Silicon mix | H100, A100, NVIDIA Hopper, Xeon, EPYC — per-workload selection |
Compliance Framing
One platform, one governance model, one audit trail — across the entire enterprise
Enterprise Governance
One platform = one security posture, one audit trail, one compliance framework. Eliminates shadow AI and ungoverned cloud GPU usage across departments.
Cross-Department Isolation
Namespace isolation ensures R&D data never touches PV data, which never touches Sales data — even though they share the same physical infrastructure.
GxP per Department
Each department's namespace can be independently validated to its specific GxP requirements. PV (safety-critical) gets Platinum tier. R&D (experimental) gets Silver tier. Same farm, different compliance levels.
CFO Visibility
Single infrastructure cost center with clear departmental allocation. No more invisible cloud spend surprises across fragmented accounts.
Audit Trail Consolidation
One centralised audit system for the entire enterprise. Regulatory inspectors see one platform, one trail, one governance model — not 5-8 fragmented cloud accounts with inconsistent logging.
Shadow AI Elimination
When departments have easy access to a governed platform, they stop spinning up ungoverned cloud workloads. Compliance risk from shadow AI is structurally eliminated.
"A pharma enterprise with 6 departments running AI on 6 different cloud accounts has 6 different audit trails, 6 different security postures, and 6 potential gaps. A consolidated captive platform reduces that to one."
Compliance positioning for pharma CIO
Who Delivers What
The largest hardware deployment. Intics' most complete platform delivery.
Your Hardware Partner — Energy, Silicon, Iron
Energy
Enterprise-scale data center partnership. 72-100+ pods require 150-300kW+ of power. The hardware partner facilitates colocation with pharma-grade SLAs (99.999% uptime). Thermal design for multi-rack GPU-dense deployments.
Silicon
Mixed silicon strategy across the enterprise: H100 for demanding models, A100 for general inference, NVIDIA Hopper for cost-optimised steady-state workloads, Xeon/EPYC for preprocessing. Silicon selection is per-workload, not per-department.
Iron
Multi-rack GPU server deployment — the largest hardware footprint of all three offerings. Enterprise-wide network micro-segmentation (department-level isolation that maps to GxP validation). Centralised lifecycle management. Deterministic switching fabric.
Enterprise-scale deployment: A single-department pilot is 48 pods. An enterprise consolidation is 72-100+ pods with multi-rack networking, storage, and lifecycle management. This is a $1.75M-$3M+ hardware investment with multi-year support.
Intics — Intelligence, Agency
Intelligence (Shared Model Serving)
Multi-tenant model serving — NLP for PV, VLM for R&D, generative for Medical Affairs — all on the same shared infrastructure. Silicon-aware scheduling ensures each model runs on optimal hardware. Shared model registry enables cross-department reuse. Serving layer handles hundreds of concurrent inference streams.
One platform serves the entire enterprise. Proven at 40-50B tokens/day across 124 pods at Fortune 25 scale.
Agency (Department Copilots)
4-tier environment model (Dev → UAT → Prod → DR) with per-department namespace isolation. Centralised prompt governance and audit logging. Queue priority across departments (safety-critical PV gets priority over batch R&D). Department-specific copilot interfaces. Turnkey handover — Intics builds, pharma owns.
The GTM motion is: Land with one department → Prove the economics → Expand to adjacent departments → Platform deal for the enterprise.
This is not "buy GPUs and figure it out." Intics delivers a production-grade multi-tenant inference platform with namespace isolation, prompt governance, audit trails, queue priority, and department-level copilots — proven at Fortune 25 scale. The pharma company owns and operates it. Intics hands over the keys.
The GTM Motion
Land → Prove → Expand → Platform
Phase 1
Land
Single department pilot (PV or Clinical Docs). 48-pod deployment. Prove the economics.
Hardware: 48-pod deployment ($1.75M)
Intics: Platform license + implementation
Phase 2
Prove
3-6 months of production operation. Demonstrate break-even, cost savings, compliance readiness.
Hardware: Support + maintenance revenue
Intics: Operational support + optimisation
Phase 3
Expand
Adjacent departments onboard. Namespace allocation, not new procurement.
Hardware: Potential expansion deployment
Intics: Additional namespace licenses
Phase 4
Platform
Enterprise-wide consolidation. CIO-level decision. 72-100+ pods. All departments on one farm.
Hardware: Full enterprise deployment ($3M+)
Intics: Enterprise license + turnkey handover
Why this motion works: The pilot proves the economics with real production data. The expansion is frictionless — namespace allocation, not procurement. The platform deal is a CIO-level decision backed by 3-6 months of proven ROI. Each phase de-risks the next.
The Pitch
Direct to the pharma CIO / CTO
"You have 6 departments each renting cloud GPUs independently — no governance, no reuse, 15-30% utilisation, and no visibility into what you're actually spending. There is a better way: one owned platform, namespace-isolated per department, 70-85% utilisation, full audit trail, and 50-65% lower total cost. We build it, prove it, and hand you the keys."
Key Numbers to Remember
50–65%
Cost Reduction vs Fragmented Cloud
70–85%
GPU Utilisation (vs 15-30%)
Mo 8–11
Break-Even
$1.75–3M+
Hardware Investment
$1.65–6.15M
3-Year Net Savings
72–100+
Enterprise Pods
4 Phases
Land → Prove → Expand → Platform
1
Platform. Audit Trail. Governance.