A generalist builds to the thinnest layer that works everywhere, so you miss the managed services that would have saved you months and money. An AWS-native team builds with the platform, not around it , Bedrock for foundation models, the Well-Architected Framework for security, cost, reliability and performance, and HIPAA-eligible architectures using the services AWS supports under a BAA.

One Line

What we build

What Each One Actually Does.

Prompting & context

The underrated baseline. A good prompt with the right context handles more than people expect. Start here , if it works, you’ve saved yourself months.

What we build

RAG (retrieval)

Gives the model your knowledge at answer time. Retrieve the relevant documents and hand them over so it answers from your truth, not its training. Right when the model needs facts specific to your business, especially ones that change.

What we build

Fine-tuning

Changes the model’s behavior by training on your examples. For teaching a style, a format, or a narrow task it keeps getting wrong - not for teaching facts. Fine-tuning to inject knowledge is a common, costly misunderstanding.

What we build

Agents

Let the model take actions across multiple steps: call tools, query systems, make decisions, chain it together. Powerful, and the most complex and least predictable. Right when the task genuinely needs multi-step autonomy.

What we build

Overview

How To Choose.

If your problem is…	Start with	Why
The model needs your specific, changing knowledge	RAG	Inject facts at answer time; keep them current without retraining
The model’s tone, format, or a narrow task is off	Fine-tuning	Change behavior, not knowledge
The model just needs better instructions	Prompting	Cheapest, fastest; often enough on its own
The task needs multi-step actions and tool use	Agents	Autonomy across steps and systems
Knowledge + consistent format	RAG + light fine-tuning	Combine: facts from retrieval, behavior from tuning

Highlights

The Mistakes That Waste Months.

Reaching for agents first

What it meansMany “agent” problems are really a deterministic workflow with one model call in the middle - far more reliable and cheaper to run.

Fine-tuning to add knowledge

What it meansIt bakes in a snapshot that’s stale the moment your pricing or policies change. Use RAG instead.

Skipping the simple version

What it meansTeams build RAG or fine-tuning before testing whether a strong prompt already solves it, and without evals, you’re guessing whether a change helped.

The status quo

How We De-risk The Choice.

We start from your problem, not the technique - looking at your use case, data, and constraints, then recommending the simplest architecture that hits your accuracy and reliability bar. We prove it with evals before adding complexity, so you can see whether each change actually helped rather than guessing.

↳ The status quo

De-risk

↳ The status quo

Questions

Frequently Asked Questions.

Usually you should. RAG plus light fine-tuning is a common, strong combination. Agents often sit on top of RAG so the model can both retrieve and act.

Can we combine these?

Yes, for behavior: a consistent format, a specific tone, or a narrow task the base model keeps getting wrong. Not for facts.

Is fine-tuning ever worth it?

Start from the problem, not the technique. The architecture review looks at your use case, data, and constraints, and recommends the simplest architecture that hits your accuracy and reliability bar.

How do we know which one we need?

Resources

Insights for tech leaders.

Whitepaper · PDF

How an Energy Retailer Used Agents to Automate 40% of Customer Ops

Whitepaper · PDF

AI-Native Product Architecture Blueprint

Selected work

Selected work.

Zeme · Real Estate Cut development costs 50% and launched 3× faster with dedicated dev teams.

Real Estate

Cut development costs 50% and launched 3× faster with dedicated dev teams.

KW · Real Estate 56M+ workflows automated, saving agents 30% time with AI-powered tasks.

Real Estate

56M+ workflows automated, saving agents 30% time with AI-powered tasks.

Let's build

Get An Architecture Review.

Bring your use case. We’ll tell you which architecture fits, where you can keep it simple, and where the complexity actually earns its place.

Book A Review

RAG, Fine-tuning, Or Agents? Pick The Architecture, Not The Hype.

See Logiciel in action.

The Decision In One Line.

What Each One Actually Does.

Prompting & context

RAG (retrieval)

Fine-tuning

Agents

How To Choose.

The Mistakes That Waste Months.

Reaching for agents first

Fine-tuning to add knowledge

Skipping the simple version

How We De-risk The Choice.

Frequently Asked Questions.

Insights for tech leaders.

How an Energy Retailer Used Agents to Automate 40% of Customer Ops

AI-Native Product Architecture Blueprint

Selected work.

Cut development costs 50% and launched 3× faster with dedicated dev teams.

56M+ workflows automated, saving agents 30% time with AI-powered tasks.

Get An Architecture Review.