CorLab AI Agent
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AI chatbots, agents, and copilots for healthcare, biotech, and logistics teams, built on data the business already holds.
Have an AI project in mind? Let's start with the data behind it.
Trusted by the biggest platforms
What We’ve Built
An accuracy number means nothing unless the model was tested on data it never saw during training.
Services
Every build starts from the data and the decision it has to support. Sometimes the answer is an existing tool.
Custom AI chatbot development services that answer from your documents and cite the passage used, so a wrong answer can be traced instead of guessed at.
LLM development services built on retrieval first, because a wrong answer gets fixed by correcting a document rather than retraining the model.
Work that crosses systems needs an agent rather than a chatbot. Custom AI agent development services, with the boundaries scoped before production.
Most steps in a business process need a rule, not a model. AI copilot development services that map the process before automating any of it.
Ask a dataset a question in language and get back the chart plus the query that produced it. Dashboard work lives on the data visualization page.
Reports, summaries and submissions drafted from data you already hold. Most of the work is unifying the systems that data has to come from.
Industries We Serve
The model is the same everywhere. What changes is the data it reads and the rules it has to work under.
Copilots inside your product. Churn models that read usage signals.
Contract review and redlining, at partner level, across portfolios nobody reads twice.
Control monitoring that produces its own evidence, not a quarterly scramble.
Ambient visit notes and prior-auth drafting, inside the hospital's own network.
Assay and instrument data made queryable. Study documents turned into structured fields.
AI logistics software and route optimization, trained on your own shipment history.
Earnings and filings synthesis, and reconciliations that trace every exception to source.
Submission triage and claims intake that drafts its own adjuster notes.
Case studies
Data pipeline first, evaluation second, and only then anything a user sees. The order matters more than the model.
Industry / Project
Process
An AI project has a step a software project does not: the data audit. Everything after it depends on what that turns up.
1
The first conversation is about the decision the system has to improve, not the feature list. What it must never do without a human gets agreed here too.
2
Before anything gets designed, the data gets read: where it lives, what condition it is in, and whether it can answer the question at all. Re-scoping here costs a fraction of re-scoping in month three.
3
Two-week sprints. Retrieval or fine-tuning gets chosen against what the audit found rather than decided in advance, and the evaluation set is written alongside the system so accuracy is a number instead of an opinion.
4
Model quality drifts as the data behind it changes, so a live system needs owners rather than a handover. That covers retraining, rerunning the evaluation set, and monitoring that catches a drop before a user reports it.
Why CorLab
CorLab's AI work starts at the data and ends at the evaluation. The model is the part in the middle.
CorLab's AI development team has built the training and evaluation layer itself: class-imbalance handling, cross-validation, holdout discipline. Every model ships with an evaluation set, so accuracy is measured before launch and re-measured when the data moves.
Retrieval is only as good as what it retrieves from. CorLab's AI engineering services cover the stores as well as the models: PostgreSQL and neo4j for relationships, Qdrant and ChromaDB for vectors.
Healthcare and biotech mean the model sees data that carries obligations. Minimum-necessary access, an audit trail on every read, and processing locations chosen to match the regulation in scope.
CorLab's engineering day overlaps European and US-East hours, so a review lands the day it is asked for. The engineer who designs your data layer is the same one who maintains it a year later.
Top-Notch Stack
We design, build, and support your projects with end-to-end software development expertise.
Our Clients' Stories
Our goal is to achieve 100% customer satisfaction by consistently delivering high-quality software solutions.
Thanks to CorLab Tech's help, the end client has received a website suitable for their needs. The team has been supportive, implementing necessary updates.
Antonio Chagoury
CEO at Maxiom Technology
CorLab Tech continues to exceed the client's expectations. They have a quick turnaround for both their development and their ability to staff projects.
Justin Cancino
CEO at Starboard Softworks
The client was highly satisfied with CoRLab Tech’s quality work. They successfully developed the component library, meeting all expectations.
Mark Sutton
VP of Web & Product at Cardinal Financial
Know Us Better
Questions that come up in most first conversations about an AI build.
Custom AI software development means the model works against your data and inside your systems, rather than being a general tool you configure. The work splits roughly three ways: the data layer, the model and retrieval design, and the evaluation that shows whether it improved anything.
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