We don't know if AI fits our process — where do we start?

With a scoped feasibility spike on one real workflow. It produces a working prototype and an honest go/no-go recommendation, usually within two weeks.

How do you keep AI costs under control?

Every deployment ships with token budgets, response caching and model-tier routing — cheap models handle easy tasks, stronger ones run only where quality demands it.

Enable — practice 04

Google AI Integration

Gemini-era AI wired into your actual workflows.

Practical applied-AI work using Google AI Studio and the Gemini family: document understanding, vision tasks, custom assistants and automations — deployed with evaluation, not vibes.

What you get

01
Use-case assessment and feasibility spike
02
Model integration via AI Studio / Vertex APIs
03
Vision and NLP task pipelines
04
Evaluation harness and cost controls
05
Team enablement documentation

Step 1

Scope

A short discovery cycle produces a fixed scope, timeline and price. No open-ended retainers by default.

Step 2

Execute

Work happens in reviewable increments on staging — you always know where things stand.

Step 3

Measure & hand over

Outcomes are reported against the agreed metrics, then everything is handed to you with documentation.

Common questions

Often combined with

Talent Acquisition Support

Have this problem?

Send us the context — you'll have a scoped answer within one business day.

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