Beyond call recording: getting real ROI from Gong
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Read the full post → The Practice
Gemini is the right call when your business already runs on Google.
Model choice should follow your architecture, not the other way around. For organizations already on Google Cloud or living in Google Workspace, Gemini on Vertex AI is often the path of least resistance and most value: the data is already there, the identity and governance are already there, and the integration surface is shorter. We help you decide whether that is true for you, and then build it well.
What we actually do
We deploy Gemini through Vertex AI, build Gemini-powered applications, and integrate them with the Google estate that makes them useful, BigQuery for the data, Workspace for where people work. Around that we do the production engineering, grounding, evaluation, and governance, that makes Gemini reliable inside a larger AI architecture instead of a clever one-off.
Where Gemini projects go wrong
The usual failures: treating the model as the project and skipping the data and grounding work underneath it, integrations to BigQuery or Workspace that are shallow enough to demo but not to depend on, and no evaluation, so no one can say whether it is actually working. We build the data foundation and the guardrails first, so Gemini runs on real context and you can trust what it returns.
Why Abstrakt Solutions
We are platform-honest. We recommend Gemini when your Google footprint makes it the right fit, and we have the AI engineering depth to deploy it as a production system, not a proof of concept. The goal is the same regardless of model: AI that does real work inside the tools your team already uses.
What We Deliver
How we help with Google Gemini.
The engagements we run most often on Google Gemini, from first implementation through optimization.
Vertex AI Deployments
Gemini deployments on Vertex AI with VPC Service Controls, data residency, and audit logging for enterprise workloads.
Gemini-Powered Applications
Custom applications built on Gemini, leveraging long context, multimodal input, and Google's safety features.
BigQuery & Data Integration
Tight integration with BigQuery for retrieval, analytics, and grounding Gemini in your enterprise data.
Workspace AI Integration
Gemini features inside Google Workspace, for organizations standardizing knowledge work on Google.
Multi-Model Architectures
Architectures that combine Gemini with Claude or GPT, using the right model for the right workflow.
Outcomes We Deliver
The metrics we actually move with Google Gemini.
Engagements are measured by movement on the numbers that matter. These are the directions of travel we commit to.
AI workflow accuracy
Improved
Pilot to production rate
Increased
How We Work
The engagement model.
Predictable phases. Clear deliverables. No surprises.
01
Discovery
One to two working sessions to map your current state, business goals, and gaps. We come out with a written scope and recommendation.
02
Design
Documented architecture, realistic timeline, and transparent commercial proposal. No surprises and no hidden scope.
03
Build
Configuration, development, integrations, data migration, and QA, with weekly demos and on-the-fly adjustments.
04
Launch & Optimize
Training, change management, hypercare, and ongoing optimization. We do not disappear at go-live.
Featured Insight
Insights on Google Gemini.
Practitioner-level analysis from the consultants delivering the work.
Revenue Operations