AI Data Readiness
From dark data to AI-connected
Your enterprise AI assistant cannot see your lab data. LIMS, ELN, instrument files, and network drives are typically invisible to it. We inventory scattered data, build a queryable foundation in your cloud, and securely connect Claude, ChatGPT, Copilot, or Gemini to it.
The Problem
Your data is scattered. Your AI assistant is blind.
Before AI can help, you need data it can actually reach.
Scattered Storage
In the teams we work with, important data often lives on laptops, departmental drives, and instruments with no central visibility.
Disconnected Systems
LIMS, ELN, instrument software, and telemetry each hold a piece but typically do not talk to each other.
Missing Metadata
Files exist, but the context—experiment, sample, batch—is often lost or locked in someone's head.
AI Cannot Reach It
Your enterprise AI assistant runs in the vendor's cloud. It cannot see your network drives or query your LIMS unless you build a path.
Copy-Paste Workarounds
People upload files by hand. There is no audit trail. Answers go stale immediately.
Security Says No
Without a governed path, IT often blocks AI access entirely. The license sits unused.
Our Approach
Three stages to AI-ready
Inventory & Assess
Map where your data lives. Score its readiness. Review your AI assistant configuration. Design a target architecture.
Connect & Curate
Link priority data sources into a queryable layer in your cloud. Build your first governed AI assistant connector.
Scale & Govern
Expand to production pipelines, catalog, access controls, and AI connections across multiple data domains.
Engagement Packages
Three ways to start
Each builds on the last. Start with assessment, prove value with a pilot, then scale.
Readiness Assessment
1–2 weeks
For teams who need clarity before building.
• Data inventory across drives, laptops, LIMS/ELN, instruments
• Readiness scorecard: data quality, integration, metadata, governance
• AI assistant configuration review (SSO, connectors, audit logging)
• Data-source risk map for AI connection
• Target architecture diagram
• 90-day roadmap
Outcome: Know what you have, what to connect, and how.
Connected Data Pilot
4–6 weeks
For teams ready to prove value on one domain.
• Connect 2–3 priority data sources
• Cloud warehouse or lakehouse in your account
• Documented data model with linked records
• One read-only MCP connector to your AI assistant
• Per-user OAuth, gateway logging, audit trail
• Answer one real business question with AI
Outcome: Working AI connection over real data—proof the approach works.
AI-Ready Platform
8–12 weeks
For teams ready to operationalize across the organization.
• Production pipelines for critical sources
• Data catalog with lineage tracking
• Role-based access controls
• Multiple AI assistant connectors across data domains
• SIEM integration and access review procedures
• Team training and operational runbooks
Outcome: A production-grade data and AI connection platform you own.
Talk to us about pricing
Every engagement is scoped to your data landscape and AI goals. Book a call to discuss.
Who This Is For
Growing companies without a data platform team
Many consulting firms target large pharma and enterprise manufacturing. We work with organizations where one or two people wear multiple hats and solutions need to be simple enough for your existing staff to operate.
Life Sciences
Biotech, diagnostics, digital pathology, clinical research—organizations generating lab and instrument data.
IoT & Industrial
Companies with sensor networks, edge devices, or equipment telemetry flowing into analytics.
Lean Technical Teams
Large enough to have real data complexity, lean enough that you cannot dedicate a platform team.
Security & Governance
Built for regulated environments
Read-Only AI Access
AI tools query curated views. They do not write to systems of record.
Per-User SSO
Every request authenticated through your identity provider.
No Inbound Access
Connectors run in your cloud. Your lab network stays behind the firewall.
End-to-End Audit
Gateway and assistant logs flow to your SIEM with user identity.
Residency Options
US-only inference where the vendor offers it, or models running inside your cloud account.
GxP-Aware Design
Controlled copies, audit trails, and documented intended use for QA review. Not regulatory advice.
Client Work
Making a petabyte of images searchable
That searchable index is what an AI assistant should query—not a raw petabyte share.
The situation
A digital pathology company had roughly 1 PB of whole-slide images on a network drive, disconnected from every other system. Finding a slide meant navigating folders or asking the one person who remembered.
What we did
We parsed image headers for technical metadata, derived context from storage paths, linked each image to its LIMS record, and indexed everything in AWS OpenSearch.
The outcome
Staff search slides by stain, tissue type, or customer. The company had been quoted a $50,000 license for similar functionality—which still would have required the same engineering. Our solution costs under $1,000 a year to run in AWS, and they own it.
Common Situations
The problem we see most often
If this sounds familiar, the Connected Data Pilot is designed for this situation.
Data lives everywhere except one place
A common pattern: an R&D team uses an ELN to document experiments. The actual data—plate-reader exports, field results, instrument outputs—lives on laptops and shared drives. The ELN knows what was done. The files hold what happened. Nothing joins them.
The fix is simpler than it looks
Inventory what exists. Land one program in a warehouse keyed to experiment IDs. Make it searchable. Connect the AI assistant through a read-only, logged connector. Weeks of work, not quarters.
FAQ
Common questions
What qualifies as "dark data"?
Dark data is information your organization collects but does not actively use—often because it is scattered across laptops, network drives, instruments, or legacy systems without a unified way to access it. Common examples include historical experiment files, instrument outputs, sensor logs, and data trapped in departmental silos.
Do we need to replace our existing LIMS or ELN?
No. We integrate with your existing systems rather than replace them. Our goal is to connect data where it lives and bring it into a central, queryable layer without disrupting day-to-day operations.
What cloud platforms do you work with?
We build on AWS, Google Cloud, and Azure—and support hybrid or on-premises deployments when data must stay local. We are cloud-agnostic and choose tools based on your existing infrastructure, compliance needs, and team expertise.
Which AI assistants do you support?
We connect Claude Enterprise, ChatGPT Enterprise, Microsoft 365 Copilot, and Gemini Enterprise to your data. The same governed data layer can serve more than one assistant, so you are not locked into one vendor.
Can't we just turn on the SharePoint or Google Drive connector?
Yes, and for documents that is often step one. Native connectors typically see what each user can already see—review over-sharing first. They also do not reach your LIMS, instrument files, or network drives. That usually requires a curated layer and a governed MCP connector.
Does our data leave our network?
Query results the assistant retrieves are sent to the assistant vendor to generate answers. That is how SaaS assistants work. We minimize what is exposed, keep the source data in your cloud, and document flows for your DPA review. For data that must stay in your cloud or region, we can run the model inside your cloud account.
How do you handle regulated environments (GxP, 21 CFR Part 11)?
We design for auditability: controlled copies, read-only AI access, and documented intended use. The assistant is treated as decision support over those copies, not as a system of record. We help QA document the position. We do not give regulatory advice, and validation remains your QA team's decision.
Do we need a dedicated data team?
Not to start. A common pattern among the growing companies we work with is no internal data platform team. We build systems designed for lean teams to operate, and we provide training so your staff can maintain and extend what we deliver.
How long before we see results?
The Assessment delivers a roadmap in 1–2 weeks. The Pilot produces a working data integration and your first AI assistant connection in 4–6 weeks. A full Platform typically runs 8–12 weeks.
What do we own at the end?
Everything: code, infrastructure as code, and documentation in your repositories and cloud account. No Shannon Alliance license required to keep it running.
Ready to make your data AI-ready?
Book a 30-minute call to discuss your data landscape and AI goals.
Ready to start?
Book a Consultation