OUR EXPERTISE

What AI Lab builds.

Discovery, AI engineering, agents, and the infrastructure they run on. Each one is something we build, not a slide we leave behind.

START A PROJECT →SEE THE SYSTEMS

/ 01PRODUCT DISCOVERY

Product discovery

We ask what the system must do before we choose a model. The output is a plan: the job, the data, the measure of success, and how you can have it built.

The jobThe task, the user, and the result that counts as done.

The data and systemsWhich records exist, and which tools the system must read or update.

The measureA test set and a pass line, agreed before build starts.

The planThe sequence of the work, from the first look to handover.

/ 02AI ENGINEERING

AI engineering

Four kinds of systems. We use the one that fits the job.

Machine learning

Predict, classify, and detect from your own data.

✓Supervised and unsupervised learning
✓Regression and classification
✓Deep learning
✓Reinforcement learning

Natural language processing

Search and read the language in your documents.

✓Semantic search
✓Sentiment and entity analysis

Computer vision

Turn images and scans into records you can use.

✓Image classification and object detection
✓Document reading and data extraction

Large language models

Answer from your sources, or take a bounded action.

✓Retrieval-augmented generation (RAG)
✓Custom model fine-tuning
✓Evaluations
✓Prompt design
✓Agents that plan and act

/ 03AI AGENTS

AI agents

An agent does a task with the tools you already have. It stops and asks a person when the case is outside its rules.

Customer supportCloses routine requests. Escalates the rest with the context attached.

Data extraction and entryReads a document and posts a checked record.

Data analysisAnswers a question from your warehouse, and shows the query.

Finance, accounting, and operationsMatches records, flags exceptions, and leaves an audit trail.

Procurement and supply chainCompares quotes, drafts orders, and watches stock levels.

Software development and quality assuranceReviews changes, writes tests, and checks a release.

/ 04INFRASTRUCTURE AND MLOPS

Infrastructure and MLOps

What keeps the system reliable after the first demo.

Cloud architectureDesigned for your AWS, Google Cloud, or Azure account.

CI/CDA change that fails the test does not ship.

MLOps and data pipelinesNew documents and records update the system without a manual reload.

MonitoringYou can see speed, cost, errors, and whether answers are drifting.

Site reliabilityLoad, failover, and a queue when a provider is down.

Security and access controlPrivate deployment. Access follows the permissions you already use.

/ 05WHY AI LAB

Why AI Lab

Four things that are true on every engagement.

YOU OWN THE CODEThe code, the tests, and the model files are yours under the project agreement.

IT RUNS IN YOUR CLOUDDeployment is in your account. Your data is not used to train public models.

WE MEASURE BEFORE LAUNCHWe agree on a test set and a pass line. Launch waits on that result.

COMMERCIAL MODELSet per project. Time and materials is one option. Written down before work starts.

Tell us the job. We will tell you if we can build it.

START A PROJECT →HOW WE WORK →