Where we help
Generative AI Solutions
Agents that take work off your queue, with authority you define and an audit trail you can show. Nothing reaches a user before it passes evaluation.
Evaluated before it ships
AI that operates, within limits you set
Most teams use AI as a tool that helps a person finish a task. The teams getting real returns treat it as a system that runs the task: agents with bounded authority, governed orchestration, and results measured sprint by sprint. We build that on Google Cloud, and because the wrong agent in production costs more than no agent at all, we test every idea against return and governance fit before any code is written.
Our Generative AI Services
- Retrieval grounded in your own governed data, with citations
- Agent workflows with bounded authority: permissions, data scopes, tool allow-lists
- Evaluation sets and confidence thresholds before launch
- Guardrails on every model call, on Vertex AI
- Every decision and tool call logged, audit-ready from the first request
- Agent metrics your CFO can read: task success, escalation rate, cost per run
Why it matters
Plenty of teams have run a generative AI pilot. Far fewer have one in production, because the hard parts are accuracy, cost, and knowing whether the thing actually pays for itself. A demo that impresses in a meeting still has to survive real users and a real budget.
Move past the demo
We build generative AI that survives real data and real load, with the evaluation to show it works before it ever reaches a customer.
Grounded, checkable answers
Retrieval keeps responses tied to your own content, and evaluation measures how often the model is right, so you can verify what it says before you rely on it.
Built on Google Cloud
We work with Vertex AI and Gemini models, so your data stays inside your own project and you are not shipping it off to a third party.
AI Use Cases
Customer Support
Chatbots that handle support around the clock and talk like a person, not a script.
Content Creation
Automated generation of marketing copy, product descriptions, and documentation.
Data Analysis
Plain-language answers and reporting pulled from complex datasets.
Code Generation
Write, test, and document code faster for your development teams.
Five gates to production
We treat generative AI as an engineering problem, not a science experiment. Each project moves through a set of stages that keep risk low early and cost visible throughout.
Qualify
Most agent ideas should die at this gate. We test each one against return, governance fit, and team readiness before any code is written.
Bound
Authority is mapped before a model is ever invoked: permissions, data scopes, tool allow-lists, and escalation paths.
Build
Reference patterns on Vertex AI, guardrails on every model call, retrieval grounded in your governed data.
Watch
Every decision logged and traced. Evaluation runs beside production traffic, and drift triggers a human review.
Measure
Task success, escalation rate, and cost per run, baselined on day one and reported every sprint.
Every phase ends with an exit. If the work is not worth doing, we say so in writing and you walk away with the findings.
Reference architecture
Your data
- Documents
- BigQuery
Retrieval
- Embeddings
- Vector search
Model
- Gemini on Vertex AI
Guardrails
- Evaluation
- Human review
Proof
Consumer Electronics
Product intelligence from a connected appliance fleet
A leading global consumer appliances company
A global appliance brand made a fleet generating 70,000 telemetry records a minute queryable in plain English, and put live pricing intelligence across 160 retailers and 1,150+ SKUs into production.
Consumer Electronics · IoT · Google Cloud
Read the study
Healthcare
Illustrative exampleHealthcare analytics platform, rebuilt on Google Cloud
A regional healthcare network serving 3 million patients
Traded a slow on-premise warehouse for a HIPAA-ready analytics platform on Google Cloud, cutting query times from over 24 hours to under 30 seconds while handling more than 1M patient records a day.
Healthcare · Data Analytics · Google Cloud
Read the study
Frequently asked
What makes an agent different from a chatbot?
A chatbot answers questions. An agent takes an item off your queue and completes it, inside an authority boundary you set. That boundary is the work: what it may read, what it may change, and when it must hand off to a person.
What kinds of problems is generative AI actually good for?
It works well for summarizing documents, answering questions over your own content, drafting text that a person then edits, and pulling structured data out of messy inputs. It is a poor fit when you need a guaranteed-correct answer with no human in the loop. We will tell you when a simpler rules-based approach would serve you better.
Where do we start if we have never shipped AI before?
We start with one use case that has a clear owner and a measurable outcome. A short proof of concept shows whether the accuracy and cost work for you before you commit to a full build. If the numbers do not add up, it is better to learn that in a few weeks than a few quarters.
What happens to our data when it goes through these models?
On Vertex AI, your prompts and data stay within your Google Cloud project and are not used to train the base models. We can keep everything inside your own environment and region, and we set retention and access rules to match your policies.
Do we need our own trained model or a huge dataset?
Usually not. Most business problems are solved by connecting an existing model to your data through retrieval, with some prompt design and evaluation on top. Training or fine-tuning a custom model is worth it in specific cases, and we say so only when it earns its cost.
Let us talk about generative AI.
A senior engineer replies, usually within one business day.