isospinsGenerative Content Creation
Draft and adapt content using approved material, tone, and review rules. Evaluate accuracy, source use, and editorial quality before publishing or sending output.
Generative AI Solutions
Create new possibilities.
Give them a purpose.
Design useful generative AI experiences for content, knowledge, product design, and document workflows. Scope large language models (LLMs), synthetic data, fine-tuning, and agentic AI around approved information, bounded actions, evaluation, and human review.
What we offer
Specialist capabilities.
A practical focus.
Scope the right combination of capabilities for your organization, data, and intended outcome.
Synthetic Data Generation
Explore generated examples for development and evaluation when appropriate. Test representativeness and disclosure risk; synthetic data still needs a documented quality and privacy assessment.
AI-Powered Product Design
Prototype product concepts, requirements, and design variations with a team. Keep decisions connected to user evidence, feasibility, and review by the people responsible for delivery.
Large Language Model (LLM)
Build language-model experiences around retrieval, structured output, or bounded tools. Compare model options against your task, information access, response time, and operating cost.
Creative AI for Visuals & Media
Explore visual concepts and media workflows with brand guidance and editorial review. Clarify usage rights, provenance expectations, and approval responsibilities within the engagement.
Custom Fine-Tuning & Deployment
Assess prompting, retrieval, and fine-tuning against the same evaluation set. Choose customization and hosting only when the observed behavior and operational requirements justify them.
Current developments · 2026
What is changing.
What to plan for.
Research reviewed October 27, 2026. Recent standards, research, and platform developments connected to practical project decisions.
Ground useful answers in inspectable sources.
Current grounding tools connect generated responses to documents, search indexes, or external search APIs. Google Cloud documents several retrieval options and source references, making information access and retrieval design part of the application architecture.
What this means for your project
Define the approved knowledge sources, access rules, update process, and answer checks. Test whether retrieved material supports the response; a citation alone is not a quality guarantee.
Primary sources
- Grounding overview — Google Cloud documentation Oct 26, 2026
Evaluate the agent's actions and the final answer.
Google's July 2026 evaluation release covers task completion, tool use, grounding, and execution traces during development and on production traffic. Agentic applications need evidence about the workflow they perform, as well as the text they produce.
What this means for your project
Create representative tasks, expected tool behavior, failure cases, and human-review checkpoints. Track quality alongside latency and operating cost before expanding an agent's responsibilities.
Primary sources
Source dates indicate publication or last update. Drafts, regional examples, and product announcements are identified in context. The project implications are Isospins’ recommendations for scoping an engagement.
A practical starting point
One focused project.
Evidence for the next step.
An illustrative engagement to shape around your organization, systems, and priorities.
Discuss a first projectAnswer one team's knowledge questions
Connect a small set of approved documents to a source-linked assistant. Start with read-only answers, a realistic question set, and a clear route to a person when evidence is missing.
- What we need to begin
- An approved document collection, access rules, real question examples, and reviewers.
- What to evaluate
- Answer correctness, source support, permission enforcement, abstention behavior, latency, and cost per task.
- What to take forward
- An evaluation set, content refresh process, integration scope, and action-approval design.
From opportunity to implementation
Technology shaped
around your business.
Design useful generative AI experiences for content, knowledge, product design, and document workflows. Scope large language models (LLMs), synthetic data, fine-tuning, and agentic AI around approved information, bounded actions, evaluation, and human review.
Define the use case, relevant industry context, integrations, evaluation criteria, and operating responsibilities before implementation. Our consulting work connects strategy, engineering, adoption, and handover.
Find your industryA defined engagement
What the work can include
- Use-case and information-access specification
- Scoped content, assistant, design, or synthetic-data prototype
- Model, fine-tuning, and output-evaluation report
- Deployment plan with action limits and human-review steps
Before we begin
Better questions.
A stronger brief.
Bring these questions to the first conversation. We can work through the unknowns together.
Start a conversation- 01
Which task should the system complete, and which actions require a person's approval?
- 02
What approved documents, examples, or synthetic data can support retrieval, evaluation, or a fine-tuning experiment?
- 03
What evidence would justify moving from a prototype to a production workflow?
Let's build something useful
Big idea. Complex challenge.
Let's find your next step.
Start a conversation about your application, AI opportunity, or cloud environment.