DAIC Labs builds the pipelines, models and cloud foundations that turn scattered operational data into decisions people actually act on. We work in your stack, ship in increments, and hand over something your team can run.
A model is only as good as the pipeline feeding it, and neither survives a cloud platform nobody can operate. We take responsibility for all three.
Ingestion, modelling and a warehouse your analysts trust. Dashboards that answer a question someone actually asked, plus the reconciliation that keeps the numbers defensible.
What this involves →Forecasting, classification, document extraction and retrieval-augmented assistants — scoped against a measurable baseline, evaluated honestly, and deployed with monitoring.
What this involves →Landing zones, migration, cost engineering and the operating model to run it afterwards. Performance and security treated as design constraints, not a later phase.
What this involves →Shipping an AI system creates an attack surface that did not exist before, and most of it is invisible to a conventional security review. A penetration test looks for injection in your web tier; it does not ask whether a document in your knowledge base can instruct the assistant reading it.
We build these controls in while the system is being designed. Retrofitting them after launch means unpicking decisions about trust and authority that are by then load-bearing.
A model reading a document, a web page or a support ticket is reading text an attacker may control. Indirect injection is the harder half, and it is not solved by filtering the user’s own input. We design so a model’s output never carries authority it has not earned.
RAG systems leak when the index forgets who may see what. Permissions have to be enforced at query time and inherited from the source system — not applied as a filter after the documents have already been retrieved.
Training sets, fine-tuning data and retrieval corpora are all attack surface if an outsider can influence what goes into them. We track provenance and put change review on anything that feeds a model.
Third-party weights, adapters and packages, pinned and scanned. Serialised model formats that execute code when loaded are treated as executables — because that is what they are.
What crosses into a prompt, what lands in a log, and what could resurface in an output months later. Redaction at the boundary, and log hygiene treated as seriously as the model itself.
Tool-using agents hold real credentials. Least privilege per tool, an authorisation boundary the model cannot talk its way past, and human approval on anything irreversible.
No six-month discovery phase. We aim to put something working in front of your team inside the first month, then build outward from what it teaches us.
Thirty minutes on the decision you are trying to improve and the data you have. We will say plainly if the problem does not need us.
We measure how the decision is made today. Without a baseline there is nothing to beat, and half of proposed AI work does not survive this step.
A pipeline, a model or a platform slice running against real data, with the evaluation numbers published rather than summarised.
Monitoring, retraining, runbooks and a working session with your engineers. You should be able to fire us without breaking anything.
Replaced a spreadsheet forecast with a model that accounts for seasonality and promotions, cutting stockouts on the top-moving lines.
Twenty years of technical documentation made searchable by meaning, with citations back to the source page so answers can be checked.
A cost model per service, rightsizing, and commitment coverage — then the guardrails that stop the savings eroding.
Thirty minutes with an engineer. Describe the data you have and the call you are trying to get right, and we will tell you whether it is a data problem, a model problem, or neither.