Get validated answers about your data in seconds.
Data Intelligence & AI Chatbot
Your lab sits on years of valuable data — SOPs, LIMS records, run histories, instrument outputs, validation packages — scattered across systems.
We custom-build an AI-powered knowledge assistant around your specific data, environment, and scientific domain so your team can ask questions in plain language and get answers that trace back to the source.
These are also known as RAGs: Retrieval-Augmented Generation.
5 hrs / week
avg. time knowledge workers spend searching for information
$350K / year
value recovered by a 50-person lab saving 30 min/scientist/day
Significant
reduction in incorrect AI responses. A 2025 study in radiology found that a RAG eliminated hallucinations entirely in their test set (0% vs. 8% baseline) under controlled conditions.
The Problem
Your most valuable answers are trapped across disconnected systems
The questions that matter most in a lab — "Has this instrument anomaly happened before?" "Why did this assay underperform last quarter?" — can't be answered from any single source. They require someone to manually piece together run logs, deviation reports, operator notes, and SOPs.
Additionally, senior scientists carry critical institutional knowledge in their heads — and their desktops. When they leave, it leaves with them.
The Solution
A knowledge assistant built specifically for your lab
We custom-build your solution into a single, searchable foundation tuned to your terminology and your scientific domain. Every answer links to the exact source it came from, so scientists can verify it and your quality team can stand behind it.
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Connect All Your Data Sources: Documents, databases, run histories, LIMS records, spreadsheets are connected into a constellation of organizational knowledge. |
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Tuned to Your Lab's Language: The system is built around your terminology, your instruments, and your scientific domain. So, it answers questions the way your team uses them, which is not a feature general AI chatbots can accomplish. |
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No hallucinations. Every answer cites its source: The system can only respond from your actual source material, and it always shows exactly where that answer came from. |
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Deployed in your environment: Your cloud, your private region, or your on-premises servers. Patient data, IP, run history, and proprietary records never have to leave your infrastructure. |
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Stays current and audit-ready: New and updated data flows in automatically, and every query, response, and access event is logged the way your inspectors expect to see it. |
Why It Matters to You
Immediate Organizational Value across disconnected systems
Hours back in people’s week
Team members get answers in seconds. New hires ramp up without interrupting senior staff. Investigations move faster because the relevant data is one question away.
Answers that cross system boundaries
Connect LIMS records with unstructured notes with instrument reference data — and answer questions that previously required three people and an afternoon.
Defensible answers for regulated environments
Every response is grounded in your actual data. That structural guarantee is what makes the difference between an interesting demo and a system your quality team will approve.
Institutional knowledge that stays
Capture the undocumented reasons behind decisions, workarounds for recurring instrument quirks, and context behind SOPs.
Phased Implementation
Every engagement is custom. We start by understanding your data landscape, your team's workflows, and where the most valuable answers are currently buried. From there, we build, integrate, and expand.
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Phase 1 |
Discovery & Scoping: We map your data sources, identify the highest-value questions your team needs to answer, and define the initial build scope. |
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Phase 2 |
Initial Build & Integration: We ingest and connect a defined set of your data sources, tune the system to your domain, and deploy it in your environment. |
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Phase 3 |
Production Build: We scale to the full data landscape — additional systems, additional users, additional use cases — building on the foundation already in place.
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Ongoing |
Compliance Configuration Add-On: For organizations operating under GxP, 21 CFR Part 11, HIPAA, ISO 17025, or similar frameworks, we offer a separately scoped compliance configuration layer covering audit logging, role-based access controls, validated deployment documentation, data residency controls, and change management documentation. |
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Ongoing |
Optional Continued Support: Ongoing support for data ingestion, system tuning, and capability expansion as your organization grows and your needs evolve. |
The Bigger Picture
Document Ingest/AI Chatbots Are the Foundation for Future AI Implementation
The ingest and organization work we do unlocks more ambitious AI projects: predictive models trained on your historical data, analytics across studies and submissions, instrument-specific troubleshooting tools, and decision-support that draws on your real history rather than generic best practices.
For organizations thinking about digital lab transformation, this is frequently the right place to start because this foundational work makes your data findable, trusted, and properly organized for more complex AI workflows.
Frequently Asked Questions:
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We already have a LIMS — does this replace it?
No. We connect to your LIMS (and every other system you rely on) as a data source. The knowledge assistant sits on top of all of it, giving your team a single place to ask questions across everything at once.
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How do we know the answers will be accurate?
Every response is grounded in your actual data and links to the source document or record. The system can't generate answers beyond what's in the source material, which is what makes it viable in regulated environments. In scientific industries, this approach has eliminated hallucinations and significantly reduced incorrect answers.
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Where does our data live?
In your environment: your cloud, your private region, or your on-premises servers. Patient data, IP, run history, and proprietary records never leave your infrastructure.
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What does the build process look like?
A phased engagement tailored to your organization: discovery and scoping, an initial build on a defined set of data sources, then expansion to a fully integrated lab-wide assistant — with optional ongoing support afterward. You see real value early.
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How long does it take?
That depends on the scope and complexity of your data landscape, which is what we assess in the discovery phase. We'll give you a realistic timeline before any build begins.