Governed, grounded AI on Google Cloud.
Lanthos connects Gemini to your real documents, data, and records — so every answer traces back to a source, and every agent stays within your guardrails. Senior engineering, from the first diagram to production.
A senior engineer replies within one business day — not a sales queue.
New parents are eligible for 12 weeks of paid leave after 90 days of service.1
- 1Employee_Handbook.pdf
- 2bigquery: hr.policies
- 3drive: Leave-2026.docx
The demo was the easy part.
Most AI pilots impress in a sandbox, then stall against real data, real permissions, and real accountability. The gap isn’t the model — it’s the grounding, governance, and engineering that make a system dependable in production.
From scattered knowledge to answers you can trace.
Grounding isn’t a feature we bolt on at the end. It’s how the system is built — every answer connected to the source that justifies it, and declined when there isn’t one.
- Documents
- Data
- Records
- Systems
- Permission-aware retrieval
- Citations & provenance
- Evaluation
- Guardrails
12 weeks of paid leave after 90 days.1
traceable to sourceSix ways to put grounded AI to work.
Each engagement is grounded in your data and built to be operated, not just demoed.
- 01
Gemini Enterprise & Agentic Workflows
Production agents that do real work — scoped, observable, and accountable to a human.
Explore - 02
Enterprise Knowledge Systems & RAG
Grounded search and answers across your documents, data, and systems — with citations.
Explore - 03
Intelligent Document Processing
Extract, classify, and structure high-volume documents with review built in.
Explore - 04
Data Foundations on Google Cloud
The clean, governed data layer that grounded AI actually depends on.
Explore - 05
Secure Google Cloud Modernization
Move to Google Cloud with security, cost, and reliability designed in.
Explore - 06
AI Governance, Security & Compliance Readiness
Adopt AI with controls, evaluation, and an audit trail — not blind trust.
Explore
Why build this on Google Cloud and Gemini?
Because the pieces — data, retrieval, agents, and controls — are designed to work as one system, not stitched together after the fact.
- 01
Grounding is the default, not an add-on
Gemini Enterprise and Vertex AI are built to ground answers in your data and cite sources — the discipline that turns a demo into something you can rely on.
- 02
One platform from data to agent
BigQuery, Document AI, Vertex AI Search, and Agent Builder fit together, so the data foundation and the AI on top of it are designed as one system.
- 03
Enterprise controls you already need
Identity, network controls, and governance are part of the platform — so security and compliance teams have real visibility, not promises.
A delivery model designed to last past launch.
Frame
Define the work, the users, and what 'good' means — before any model is chosen.
Ground
Connect and govern the data and documents the system will rely on.
Build
Ship the agent, search, or pipeline in your environment, on real systems.
Evaluate
Measure accuracy, coverage, and refusal behavior. Quality is tested, not assumed.
Operate
Monitor, log, and improve — with runbooks and an operating model your team owns.
Engagements, not embellishments.
Grounded policy answers for a distributed workforce
A permission-aware knowledge system over scattered HR and policy documents, answering in plain language with citations to the source passage.
Contract intake and obligation tracking
A Document AI pipeline that classifies incoming agreements, extracts key terms with confidence scoring, and routes low-confidence cases to review.
A governed data layer for AI features
A modeled BigQuery foundation with lineage and access controls — the reliable base a grounded assistant was then built on.
Engagements are anonymized to respect client confidentiality. Detailed outcomes and references are shared under NDA.
Questions we get before the first call.
Will our data train the model?
No. Your content is used to ground answers at request time, not to train models. Retrieval respects your existing access controls, and what the system can see is scoped to what you configure.
What stops it from making things up?
Grounding. Answers are tied to passages in your approved sources and cite them; when the sources don't support a question, the system declines rather than guesses. We evaluate accuracy, coverage, and refusal behavior before launch.
Can security and compliance review it?
Yes. We design with least-privilege IAM, customer-managed encryption, audit logging, and NIST/FedRAMP/CMMC-aligned controls, and we document the authorization boundary. Google Cloud maintains its own authorizations; the compliance status of a deployment depends on its configuration and boundary.
Who operates it after launch?
Your team — with our help. We deliver evaluation, monitoring, logging, and runbooks so the system is operated, not just demoed, and hand over an operating model you can own.
Can you work alongside our Google Cloud account team?
Yes. Account teams and agencies bring us in as the senior Gemini and Google Cloud delivery team — to scope, build, and operate the work alongside the relationship you already have.
A specialized delivery team you can put on the work.
Account teams and agencies bring us in as the senior Gemini and Google Cloud delivery partner — to scope, build, and operate the AI work alongside your relationship.
Let’s scope the work after the demo.
Tell us what you are trying to ground in AI. We will tell you the honest path to production.