Top asset
ROI measured, not promised.
Before deployment, we quantify the expected gain. After deployment, we measure it. If the ROI isn't there, we stop.
AI integrations.
AI in your workflows: agents, generation, qualification, research.
Definition · AI integrations
Studjoow designs and deploys custom AI agents and automations: content generation, lead qualification, support, semantic search, moderation. Claude (Anthropic) and GPT (OpenAI) stack, with security and cost guardrails. Measurable, secure, deployed in production.
Why it matters
in 2026
AI in 2026 is no longer a promise — it is a daily tool. Claude 4 and GPT-5 models respond in under a second, read your documents, and reason about complex cases. But the gap is widening between businesses that integrate AI into their workflows and those still using it as a gimmick. Real impact comes from agents and automations running 24/7: lead qualification, personalised content generation, documentary search, moderation. Studjoow designs these custom integrations with guardrails (costs, quality, security, GDPR) and ROI measurement. No AI for show: only cases where the return on investment is demonstrated.
AI integrations · 1
Studjoow approach
We start by identifying high-volume repetitive tasks. We prototype quickly with Vercel AI SDK, evaluate quality on real cases, then deploy with cost and quality monitoring. No AI for the sake of AI: only cases where ROI is demonstrated.
Method
The project, step by step.
5 mapped steps, from brief to launch. Each step produces a deliverable validated with you before moving to the next.
Process audit.
Identifying high-volume repetitive tasks, measuring current time spent, estimating potential gain. Not everything is a candidate for AI.
Rapid prototype.
POC in 1 to 2 weeks with Vercel AI SDK, tested on 50 to 100 real cases. Quality validation before scaling.
Quality evaluation.
A/B tests on representative cases, precision measurement, edge case detection. Iterative prompt adjustments.
Scaling.
Cloudflare Workers or Vercel deployment, error handling, automatic fallback between models, caching to reduce costs.
Monitoring & costs.
Quality dashboard + tokens consumed. Alerts on quality drift or cost spikes. Continuous optimisation.
Everything included
The scope, no surprises.
- Conversational agents (Claude, GPT, Gemini)
- Content generation (articles, emails, product descriptions)
- Automated lead qualification
- Automated content moderation
- Semantic search (RAG on your data)
- Automated SEO/GEO audits (see Sk:vr)
- Extraction and structuring pipelines
- Integration with existing tools (CRM, mailing, e-commerce)
- Security and cost guardrails
AI integrations · 2
What you receive
Typical deliverables.
Deliverable · 01
Agent or pipeline deployed in production
Deliverable · 02
Versioned and tested prompts
Deliverable · 03
Quality + cost monitoring
Deliverable · 04
User and technical documentation
Deliverable · 05
Training for the internal team
The Studjoow difference
Why here over anywhere else ?
- 02
Cost guardrails.
Caching, lightweight cascading models, intelligent fallback. A well-designed AI integration costs 5 to 10 times less than you might expect.
- 03
Data sovereignty.
Paid Claude and GPT APIs do not use your data for training. 30-day retention for moderation, deletable on Enterprise.
- 04
Open stack.
Modular architecture: we can switch Claude for GPT for Mistral as the landscape evolves. No dependency on a single provider.
Toolbox
Stack & technologies.
Tools I use daily for this discipline. Chosen for performance, maintainability, and portability.
- Anthropic Claude
- OpenAI GPT
- Vercel AI SDK
- LangChain
- Pinecone
- Supabase Vector
- Cloudflare Workers AI
- Next.js
- TypeScript
For whom
This expertise applies to you if :
- Profile 01
High editorial volumes (e-commerce, media)
- Profile 02
Saturated support teams
- Profile 03
Commercial qualification processes
- Profile 04
Document bases to exploit (RAG)
AI integrations · 3
Myths & reality
Common misconceptions.
Myth 01
AI will hallucinate and give wrong answers.
Reality
In 2026, hallucinations are rare when the system is well designed (RAG, guardrails, validation). Good agents are more reliable than junior staff.
Myth 02
AI will replace my team.
Reality
AI replaces repetitive low-value tasks, freeing your team for high-impact work. Well integrated, it augments more than it replaces.
Myth 03
AI is too expensive for my organisation.
Reality
Claude and GPT APIs bill per token. A well-sized agent costs 50 to 500 € per month for most uses — less than half a day of human support.
FAQ
Questions I get asked.
What is the difference between Claude and GPT?
Claude (Anthropic) excels at long reasoning and document reading. GPT (OpenAI) is more versatile and has a larger ecosystem. The right model depends on the use case: we test both when relevant.
Will my data be used to train the AI?
No by default on paid Claude and GPT APIs: data sent is not used for training. 30-day retention for moderation purposes, deletable on request for Enterprise accounts.
How much does it cost to run an AI agent in production?
Highly variable by volume: between 50 and 500 € per month for most cases (several thousand interactions). Agents are designed to be efficient: caching, lightweight models, intelligent fallback.
What is RAG?
Retrieval-Augmented Generation: the AI first searches your documents, then generates a response grounded in what it found. Used for document bases, enriched FAQs, and customer support.
Do I need to rebuild my agent for each new model?
Not if the architecture is modular. Studjoow designs agents with an abstraction layer: switching from Claude 4 to Claude 5 or GPT-4 to GPT-5 takes an hour, not a month.
Go further
Other expertise areas.
Let's go ?
First conversation is free, no commitment. If ai integrations is your need, I'll get you a detailed recommendation within 48 hours.