LLM integration

Integrate an LLM into a business tool without creating a black box

Wiring an LLM to an application is easy. Building a reliable AI feature for real users takes more care: context, prompts, data, permissions, logs, cost, errors, UX, and quality criteria. Gatien can help turn an AI capability into a usable feature.

Share my need See the method

What can be built

Concrete deliverables, not a vague AI promise

Every project starts from a real workflow, real examples, and an output the team can check.

AI features in an app

Generation, summary, classification, extraction, or recommendation inside an existing interface.

LLM pipelines

Robust processing chains with steps, validation, retries, and logging.

Augmented internal tools

Assistants that complete files, prepare replies, or analyze information.

Guardrail architecture

Format checks, validation, permissions, logs, alerts, and behavior when the model is unsure.

Use cases

Example workflows to test

These examples are a starting point. The right scope depends on your data, your users, and the business risk.

Summary and synthesis

Turn conversations, documents, or tickets into actionable summaries.

Structured extraction

Produce JSON fields, CSV, or database records from free text or documents.

Classification

Prioritize, route, or categorize requests according to business rules.

Assisted generation

Prepare a draft the user can review, correct, and approve.

How a mission runs

A structured engagement that learns quickly

Scoping

Clarify the business problem, the users, the available data, the constraints, and the expected result.

Prototype

Build a first testable version on real examples, without overbuilding the architecture.

Business tests

Check answers, errors, edge cases, permissions, and how much human review is needed.

Integration

Connect the tool to existing systems: internal app, CRM, Slack, Teams, email, document store, or API.

Documentation

Hand over a maintainable base: code, configuration, known limits, success criteria, and next steps.

Stack

Possible technologies

OpenAI, Anthropic, or another LLM API based on the need
TypeScript, Next.js, Node.js, and Python
Supabase, PostgreSQL, pgvector, or other vector stores
n8n, Make, webhooks, and business APIs
Slack, Teams, Gmail, Outlook, Notion, SharePoint, or internal tools

Deliverables

What can be handed over

LLM integration architecture
AI feature integrated into the product or workflow
Prompts, output schemas, and regression tests
Logs, error handling, and monitoring recommendations

Scope

Included and not included

Included

Functional and technical scoping
An architecture that is simple to understand and maintain
Development of the prototype or first version
Tests on real examples provided by the team

Not included

ROI promises without real data
Automation without human review when business risk is high
Use of sensitive data without clear access rules
Fake testimonials, fake numbers, or invented demos

FAQ

Questions before you start

Want to add an AI feature to a product or internal tool? Prepare the user flow and examples of the expected output.

Contact Gatien

Which LLM should you choose?

The choice depends on the use case, expected quality, cost, confidentiality, latency, and the existing technical stack.

How do you keep API costs under control?

By cutting unused context, caching what can be cached, picking the right model, and measuring real volume.

Should everything be automated?

No. For many cases, the best product is a draft or a recommendation that the user reviews.

Freelance AI developer

Go from an AI idea to a concrete tool, wired into the company’s real workflows.

AI agent for companies

AI agents connected to precise tasks, with guardrails and review matched to the risk.

RAG and internal document search

An assistant that answers from your documents, with visible sources and stated limits.

← AI services

LLM integration

Integrate an LLM into a business tool without creating a black box

Share my need See the method

What can be built

Concrete deliverables, not a vague AI promise

Every project starts from a real workflow, real examples, and an output the team can check.

AI features in an app

Generation, summary, classification, extraction, or recommendation inside an existing interface.

LLM pipelines

Robust processing chains with steps, validation, retries, and logging.

Augmented internal tools

Assistants that complete files, prepare replies, or analyze information.

Guardrail architecture

Format checks, validation, permissions, logs, alerts, and behavior when the model is unsure.

Use cases

Example workflows to test

These examples are a starting point. The right scope depends on your data, your users, and the business risk.

Summary and synthesis

Turn conversations, documents, or tickets into actionable summaries.

Structured extraction

Produce JSON fields, CSV, or database records from free text or documents.

Classification

Prioritize, route, or categorize requests according to business rules.

Assisted generation

Prepare a draft the user can review, correct, and approve.

How a mission runs

A structured engagement that learns quickly

Scoping

Clarify the business problem, the users, the available data, the constraints, and the expected result.

Prototype

Build a first testable version on real examples, without overbuilding the architecture.

Business tests

Check answers, errors, edge cases, permissions, and how much human review is needed.

Integration

Connect the tool to existing systems: internal app, CRM, Slack, Teams, email, document store, or API.

Documentation

Hand over a maintainable base: code, configuration, known limits, success criteria, and next steps.

Stack

Possible technologies

OpenAI, Anthropic, or another LLM API based on the need
TypeScript, Next.js, Node.js, and Python
Supabase, PostgreSQL, pgvector, or other vector stores
n8n, Make, webhooks, and business APIs
Slack, Teams, Gmail, Outlook, Notion, SharePoint, or internal tools

Deliverables

What can be handed over

LLM integration architecture
AI feature integrated into the product or workflow
Prompts, output schemas, and regression tests
Logs, error handling, and monitoring recommendations

Scope

Included and not included

Included

Functional and technical scoping
An architecture that is simple to understand and maintain
Development of the prototype or first version
Tests on real examples provided by the team

Not included

ROI promises without real data
Automation without human review when business risk is high
Use of sensitive data without clear access rules
Fake testimonials, fake numbers, or invented demos

FAQ

Questions before you start

Want to add an AI feature to a product or internal tool? Prepare the user flow and examples of the expected output.

Contact Gatien

Which LLM should you choose?

The choice depends on the use case, expected quality, cost, confidentiality, latency, and the existing technical stack.

How do you keep API costs under control?

By cutting unused context, caching what can be cached, picking the right model, and measuring real volume.

Should everything be automated?

No. For many cases, the best product is a draft or a recommendation that the user reviews.

Integrate an LLM into a business tool without creating a black box

Concrete deliverables, not a vague AI promise

AI features in an app

LLM pipelines

Augmented internal tools

Guardrail architecture

Example workflows to test

Summary and synthesis

Structured extraction

Classification

Assisted generation

A structured engagement that learns quickly

Scoping

Prototype

Business tests

Integration

Documentation

Possible technologies

What can be handed over

Included and not included

Included

Not included

Questions before you start

Which LLM should you choose?

How do you keep API costs under control?

Should everything be automated?

Related pages

Freelance AI developer

AI agent for companies

RAG and internal document search

Integrate an LLM into a business tool without creating a black box

Concrete deliverables, not a vague AI promise

AI features in an app

LLM pipelines

Augmented internal tools

Guardrail architecture

Example workflows to test

Summary and synthesis

Structured extraction

Classification

Assisted generation

A structured engagement that learns quickly

Scoping

Prototype

Business tests

Integration

Documentation

Possible technologies

What can be handed over

Included and not included

Included

Not included

Questions before you start

Which LLM should you choose?

How do you keep API costs under control?

Should everything be automated?

Related pages

Freelance AI developer

AI agent for companies

RAG and internal document search