We connect your internal data sources with generative AI through RAG orchestration, security guardrails and continuous quality evaluation. The delivery is packaged into clear stages: Discovery, pilot, production pipeline and a follow-on operating model ready for audit.
Vertex AI · RAG orchestration · security · evaluation · FinOps guardrails
Your AI pipeline becomes an auditable product: data is isolated, access controlled and quality tracked from day one.
We do not sell “something with AI”. Each package has a defined scope, output and next step into production. That is how AI becomes a real commercial offer instead of a hype topic.
The entry package for leadership and the delivery team. In 1–2 weeks we define the use case, data sources, guardrails and what should move into a pilot.
4–6 weeks for the first controlled use case on documents, CRM or the internal knowledge base. The pilot is ready for stakeholder demos and further investment decisions.
6–10 weeks for a Vertex AI pipeline ready for production traffic, audit and FinOps control. Includes observability, incident model and handover.
A follow-on monthly model for quality monitoring, prompt/version governance, costs and AI incident response around the pipeline.
For teams that need to quickly decide whether a GenAI use case has real business value and how to get it safely into a pilot. No long audit. One sprint, one decision-ready output.
Best for: knowledge assistants, CRM copilots, enterprise search, support assistants.
Not included: pilot implementation itself, large-scale integrations and custom app development.
We never skip discovery or security. The pipeline is treated as a product with clear guardrails, governance and success metrics.
The diagram shows how we connect knowledge sources, security layers and Vertex AI so the pipeline stands production traffic.
Iterative delivery – every sprint ships a tangible outcome for your stakeholders.
Align business priorities, data availability and define quality as well as compliance metrics.
Design the architecture, security model, access roles and data contracts for each team.
Build the RAG pipeline, implement evaluation, integrations and monitoring including cost guardrails.
Deliver runbooks, training, FinOps reporting and an adoption plan across teams.
Impact on sales and operations – safely on your data.
Personalised offers and follow-ups with guardrails on sensitive data.
Runbooks, mitigation suggestions and generated post-mortem summaries.
Knowledge-base chat, evaluated and iterated with your team.
Questions your CTO, CISO and business owners ask before shipping AI.
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.
FinOps guardrails, quotas and dashboards are part of the delivery. We model expected usage, set alerts and tune orchestration so inference stays cost‑efficient.
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.
In 30 minutes we review the key use cases, available data and decide whether you should start with Discovery, a pilot or a production package. The call also sets the guardrail checklist for internal data.