Private RAG, validated ML, BI analytics and governance for regulated life science. Your R&D spend and SOPs get analysed where they live — not in a vendor's logs. Your prompts don't train someone else's model. Your auditors get evidence they can point to, not vibes.
Monthly spend · €K
BudgetThese are not predictions. They're the last 24 months of enterprise AI in regulated industries.
When your scientists paste synthesis routes, clinical drafts or batch records into ChatGPT, Gemini or Claude.ai, that text becomes training data unless you explicitly opt out. OpenAI retains prompts for 30 days; others keep them longer. "We don't use your data" on the marketing page is a preference you set — not a default state.
21 CFR Part 11 demands signed, reviewed, traceable actions. EU Annex 11 demands validated systems with change control. A chat log dumped into a vendor's opaque database is neither. When an auditor asks "who approved this prompt, and where is it retained?", a consumer LLM can't answer.
Your clinical data must stay in the EU under GDPR. Your IP is governed by the contracts you signed. Consumer AI sends prompts to US servers, often with unclear subprocessor chains. For pharma, that isn't a compliance gap — it's a notifiable breach waiting to happen.
Nine concrete use cases we've built or are actively building with pharma, biotech, and medtech clients. All of them run inside your tenant.
Point an AI at your SOPs, batch records, and deviation logs — without those documents ever leaving your tenant. Questions get answered with citations. No training, no retention, no leakage.
AI drafts investigation proposals and CAPA steps from your historical quality data. QA approves, rejects, or edits — humans stay in control. Every AI action logged, signed, and auditable.
Continuous monitoring of FDA, EMA, Swedish MPA, and EudraLex publications. The AI surfaces what matters for your pipeline, products, and indication — in plain English, with source links.
IQ/OQ/PQ adapted for machine learning. ALCOA+ extended to AI outputs. Model change control, re-validation triggers, explainability docs — everything your quality function needs to actually deploy AI in a GxP process.
Automated analysis of R&D spend, trial cost-per-site, and programme budgets. The AI surfaces variance, flags anomalies, and drafts management summaries — all running where your financial data already lives, not in a SaaS vendor you have to sign another DPA with.
AI triages adverse-event reports and drafts ICSR narratives from source documents. Cross-case signal detection on your AE database. Human review and e-sign before anything goes to regulators — compliant with your existing PV SOPs and GVP Module VI.
AI helps draft, update, and redline SOPs, WIs, and validation protocols against your house style and regulatory expectations. Tracked changes, version control, and full diff history — not a wholesale rewrite.
Continuous monitoring of your critical suppliers, vendors, and subprocessors. Breach disclosures, Schrems-II posture changes, SOC2 lapses, GxP-relevant news — all flagged before your legal or QA team hears about it elsewhere.
Protocol-deviation tracking, site-query drafting, and enrolment signals across your study portfolio. The AI reads your TMF and CTMS and surfaces what needs attention — your CRAs focus on exceptions, not inbox.
Same outcomes, different trade-offs on sovereignty, cost, and capability. We pick the pattern that fits your data, your risk posture, and your ops.
Everything stays on your hardware. No cloud, no API, no outbound network call. Maximum sovereignty — you provide the GPUs.
Trade-off Your ops team runs it. Capability ceiling below frontier models.
Frontier-model capability with contractual data boundaries. Inputs stay in your cloud tenant, governed by your existing BAA/DPA.
Trade-off Vendor processes your prompts — within the tenant boundary you already trust.
Your documents are embedded and indexed locally. Only the minimal context needed for a query is sent — via a zero-retention API — to a frontier model.
Trade-off Most practical for most pharma. Requires careful prompt-scrubbing & query-audit design.
The artifacts your QA, IT, and Legal teams need to let AI into a regulated process. Built from our template, calibrated to your specific data classes, tools, and workflow.
What's in scope, what's not. Which tools, data classes, and roles — the thing that stops shadow-AI.
For each data classification (public → regulated), which AI patterns are allowed — cloud, tenant, or on-prem.
Every model and AI service in use: owner, data scope, validation state, last review. Your GxP system-inventory pattern, applied to AI.
Documented triggers for re-validation. Which model or prompt changes need QA sign-off — and which don't.
For every AI action: inputs, outputs, approver, model version, retrieval source. Evidence pack, not chat logs.
Hallucinated outputs, prompt-based data leaks, model drift, vendor breach — distinct playbooks because the failure modes are different.
The cross-functional body (Quality, IT, Legal, Clinical) that approves use cases, reviews incidents, sets policy. Membership and cadence included.
From "what's even happening?" to "ship it" to "keep it governed." A predictable rhythm — adapted to your scope, not a slideshow.
Discover what AI is (and isn't) already in use, which data classes it touches, and where the governance gaps are.
Turn findings into a defensible strategy and the governance documents that let you actually move forward.
Build and deploy. Private RAG, validated ML, BI analytics — whatever the strategy prioritised. Iteratively, with QA in the loop.
We stay on as governance stewards — cadence reviews, incident response, new use cases as they emerge.
Straight answers to the questions we get most often before an engagement.
For personal productivity on non-regulated data, yes. For anything touching SOPs, batch records, clinical data, IP, or patient information, no. The reason isn't paranoia — it's that consumer-grade AI ships your prompts to vendor infrastructure with retention periods, unclear subprocessor chains, and no audit trail a regulator accepts.
Enterprise-tier equivalents (ChatGPT Enterprise, Claude for Work) improve the data position but still don't give you the GxP-compatible audit trail or tenant isolation regulated workflows require. We build the version that does.
M365 Copilot is a special case — it runs inside your Microsoft 365 tenant, which solves the data-residency question for content already in M365. Good for: summarising Teams meetings, drafting Outlook replies, working inside Word/Excel on already-classified content.
Not good for: anything you need to prove didn't leak, cite its source, or pass a Part 11 audit. It also reads Graph data broadly — if your Intune hygiene isn't perfect, it can surface documents to users who technically have access but shouldn't. We help audit your Copilot deployment and set the guardrails.
A first working version — one document source, answering with citations — is typically 4–6 weeks. A production-ready deployment with permissions, audit trail, and QA review workflow is 2–4 months depending on scope.
The long tail is never the model — it's the data pipeline (which SharePoint sites? which SOPs are current? who should see what?) and the evaluation harness (how do you know the answers are right?). We time-box both explicitly.
It depends on what the AI is doing, where your data lives, and what your auditor will accept. For on-prem regulated workloads: Llama 3.1/3.3 70B or Mistral Large. For tenant-isolated cloud: Claude 3.5 Sonnet, GPT-4o, or Gemini 2.5. For embeddings: BGE, nomic-embed-text, or OpenAI text-embedding-3-large.
We benchmark 3–5 candidates against your actual use case before committing — and re-benchmark quarterly, because the leaderboard moves.
For Patterns B and C (tenant-isolated cloud, hybrid), no — inference runs on vendor infrastructure. For Pattern A (fully isolated), yes, but smaller than you'd expect.
A single server with 2×H100 or 4×L40S can run a 70B-class model at useful throughput for a few hundred users. We size the hardware to actual concurrency, not a glossy brochure spec.
A change-controlled model card; an IQ/OQ/PQ package adapted for ML (hardware qualification, functional tests against a frozen eval set, performance tests on representative queries); an ALCOA+ audit trail capturing input, retrieval context, output, approver, and model version for every AI-generated action.
Plus a re-validation trigger policy (when does a new model version or prompt change require re-qualification?) and a use-case-specific explainability document. We build all of these from templates we've already run past auditors.
Pricing follows the phases. Phases 01 Assessment and 02 Strategy & Policy are fixed-price engagements — scoped and signed before we start. Phase 03 Implementation is scope-dependent; we quote after phases 01–02 give us real numbers, either fixed or milestone-based. Phase 04 is a quarterly retainer.
No hourly billing games — you know the number before we begin. We quote after a short scoping call.
A 30-minute intake call, a two-week assessment, a defensible strategy by week six, working systems by week ten. No "AI transformation" slideshows.
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