RAG assistant on CRM data
Personalised offers and follow-ups with guardrails on sensitive data.
- 6 weeks PoC → pilot
- -40% prep time per proposal
- 3x more personalised pitches
AI & LLM pipelines
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.
2 weeks
to the first secure PoC
6–8 weeks
to a guarded production rollout
100 %
of prompts and access fully audited
Blueprint
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.
Highlights
- 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
Stack
Vertex AI
BigQuery
Dataflow / Dataproc
Cloud Run / GKE
Cloud Logging/Monitoring
Governance
- 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.
01 · Discover
Use-case & data discovery
Align business priorities, data availability and define quality as well as compliance metrics.
02 · Design
Architecture & governance
Design the architecture, security model, access roles and data contracts for each team.
03 · Build
PoC & pilot
Build the RAG pipeline, implement evaluation, integrations and monitoring including cost guardrails.
04 · Run
Rollout & enablement
Deliver runbooks, training, FinOps reporting and an adoption plan across teams.
AI projects with measurable outcomes
Impact on sales and operations – safely on your data.
AI copilot for incidents
Runbooks, mitigation suggestions and generated post-mortem summaries.
- MTTR -25% in the first month
- 90% auto-generated summaries
- 24/7 coverage without headcount increase
AI self-service for customers
Knowledge-base chat, evaluated and iterated with your team.
- 65% inquiries resolved via self-service
- -35% L1 workload
- +12 pts NPS in customer care
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.