Vertex AI pipelines that keep your proprietary knowledge safe.
We connect your internal data sources with generative AI through RAG orchestration, security guardrails and continuous quality evaluation. You get a secure PoC, production pipeline and an operating model ready for audit.
Vertex AI · RAG orchestration · security · evaluation · FinOps guardrails
What we deliver in the first 8 weeks
Your AI pipeline becomes an auditable product: data is isolated, access controlled and quality tracked from day one.
How we build the pipeline
We never skip discovery or security. The pipeline is treated as a product with clear guardrails, governance and success metrics.
- Prioritise use-cases and metrics with business stakeholders
- Secure data integration (RAG, feature store, data contracts)
- Evaluation, guardrails and governance ready for audit
What we deliver
- Discovery & AI strategy workshop with leadership
- Reference architecture (Vertex AI, BigQuery, Cloud Run/GKE)
- Security & compliance model (IAM, VPC-SC, DLP)
- Pipelines for training, evaluation and runtime RAG
- Runbooks, observability and FinOps reporting
Reference architecture
The diagram shows how we connect knowledge sources, security layers and Vertex AI so the pipeline stands production traffic.
- Vertex AI Pipelines, Model Garden and prompt management
- BigQuery, Dataproc/Dataflow and knowledge embedding
- VPC Service Controls, IAM guardrails and DLP
- Observability, audit trail and AI incident model
- Role-based access, audit logs and DLP policies
- Sensitive data policies, retention and legal hold
- FinOps dashboards, quotas and alerting
How we work
Iterative delivery – every sprint ships a tangible outcome for your stakeholders.
Use-case & data discovery
Align business priorities, data availability and define quality as well as compliance metrics.
Architecture & governance
Design the architecture, security model, access roles and data contracts for each team.
PoC & pilot
Build the RAG pipeline, implement evaluation, integrations and monitoring including cost guardrails.
Rollout & enablement
Deliver runbooks, training, FinOps reporting and an adoption plan across teams.
FAQ – AI pipeline in practice
Questions your CTO, CISO and business owners ask before shipping AI.
How do you stop the model from leaking data?
We work with isolated projects, VPC Service Controls, granular IAM and encryption. Sensitive data stays inside defined boundaries, every access is audited and DLP policies are preconfigured.
Will AI spend spiral out of control?
FinOps guardrails, quotas and dashboards are part of the delivery. We model expected usage, set alerts and tune orchestration so inference stays cost‑efficient.
How do you prove answer quality and relevance?
We build an evaluation dataset, define metrics (BLEU/ROUGE/BERTScore or custom scoring) and add human review where needed. Before full rollout we run A/B tests and continuous drift monitoring.
Let’s map an 8-week roadmap for your AI pipeline.
In 30 minutes we review key use-cases, available data and define the guardrails your pipeline must meet.