Enterprise AI
Turnkey AI products, built for you
You have the data. What is missing is the layer that makes it answerable. We scope it, prototype it on your real data within weeks, and deliver a production system your team owns.
Enterprise engagements at a glance
- Service
- Custom, turnkey AI-enabled products designed, built and delivered by Oogwai
- Typical builds
- Natural-language query layers, document and invoice automation, forecasting and anomaly detection, internal copilots, agent workflows, supporting data pipelines
- Deployment
- Our cloud or yours — AWS, Azure or GCP — including private networking and customer-managed keys
- Model approach
- Model-agnostic; frontier, smaller or self-hosted models chosen per workload, with the model layer kept replaceable
- Timeline
- Discovery in 1–2 weeks, working prototype on real data in a further 2–4 weeks, production by agreed scope
- Team
- The same engineers who build and operate Oogwai’s own products
- Handover
- Documentation, monitoring, evaluation harness and training so your team can run and extend it
- Start here
- Contact us or book a working session
What we build
Six shapes most engagements take
Natural-language query layer
Ask the warehouse, ERP or product database a question in English and get a checked answer. The pattern behind our own products, applied to your schema and your definitions.
Document and invoice automation
Read what arrives — bills, POs, contracts, claims, forms — extract the fields, match them against masters and file them into the system of record with approval and audit trail.
Forecasting and anomaly detection
Demand, spend, cash, churn or throughput, modelled on your own history, with alerting when reality departs from the forecast.
Internal copilots
A domain assistant for one team — support, sales, ops, finance — grounded in your documents and data, with the boundaries and refusals the domain requires.
Agent workflows
Multi-step operational tasks executed end to end, with human approval at the points where it matters and a full record of what was done.
Data foundations
The pipelines, models and quality checks that make everything above possible when the underlying data is not yet in a fit state.
Engagement model
How a build runs
Four phases. Each one ends with something you can judge before committing to the next.
Discovery — 1 to 2 weeks
We work through the data you hold, the decisions it should support and the constraints around it. Output: a scoped design, a cost and a definition of what success would look like in numbers.
Prototype — 2 to 4 weeks
A working system on your real data, not a mock. You use it, break it, and tell us where it is wrong. This is where scope gets honest.
Production
Hardening: access control, monitoring, evaluation against a test set, failure handling, deployment into your environment, security review.
Run or hand over
Either we operate it under a support agreement, or your team takes it with documentation, training and the evaluation harness that tells you when quality drifts.
How we work
Principles we do not negotiate on
The answer shows its working
Any AI output that informs a decision carries its source, filter and window. If a number cannot be traced, it does not ship.
It says when it does not know
Systems that answer everything are dangerous. We build in refusal and uncertainty for questions the data cannot support.
Evaluated, not vibes-checked
Every build ships with a test set and an evaluation harness, so quality is a measurement rather than an impression.
Your data stays yours
No customer data trains shared models. Residency, retention and deletion are set by you and written into the agreement.
Questions
Enterprise AI FAQ
What does Oogwai build for enterprises?
Turnkey AI-enabled products on your own data and inside your own environment. In practice that means one or more of: a natural-language query layer over a warehouse or ERP, document and invoice processing, forecasting and anomaly detection, internal copilots for a specific team, agent workflows that carry out multi-step operational tasks, and the data pipelines underneath all of it.
The deliverable is a working product your team can run, not a strategy document.
How is this different from hiring a consultancy?
We ship product because we run product. The same engineering that built Oogwai Analytics and Prism Books builds your system, which means the hard parts — schema understanding, query reliability, evaluation, handling the cases where the model should decline to answer — are solved patterns for us rather than research for you.
How long does a build take?
A scoped discovery takes one to two weeks. A working prototype on your real data typically follows within two to four weeks of that. Production hardening — access control, monitoring, evaluation harness, handover — depends on scope and integration surface, and is agreed before work starts.
Can it run in our own cloud?
Yes. Deployment into your AWS, Azure or GCP account is standard for regulated customers, including private networking and customer-managed keys. Where data cannot leave a boundary, we design for that from the start rather than retrofitting it.
Which AI models do you use?
We are model-agnostic and choose per workload — frontier models where reasoning quality matters, smaller or self-hosted models where cost, latency or data residency dominates. The architecture keeps the model layer replaceable, so you are not locked to a vendor whose pricing or capability changes.
Who owns the result?
You own your data, and the terms of ownership for the delivered system are set in the engagement contract. Standard practice is that customer-specific code and configuration belong to the customer, with our reusable platform components licensed to you.
What data do you need to start?
Less than people expect for discovery — usually a schema description, a sample extract and access to the people who know what the fields actually mean. Full access is granted at prototype stage, under whatever agreement your security team requires.
What industries have you worked with?
Our products cover advertising and e-commerce, finance and accounting, content and media, and public environmental data. Custom builds follow the same pattern wherever there is structured operational data and people who need to ask questions of it.
How do we start a conversation?
Contact us with a short description of the data you have and the decision you want it to support, or book a session and we will work through it live.
Bring us the data and the decision
Tell us what you hold and what you need it to answer. Discovery starts from there.