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06AI & automation

We build AI into the products and workflows your people already use — language models integrated where the work happens, connected to your own content through retrieval, fed by data pipelines, and evaluated so you can see how well each feature works.

  • 10Services
  • 4Disciplines
  • 1Method
  • 5Languages
Start the auditSee how it works
  • LLM integration
  • RAG
  • Data pipelines
  • Evaluation
  • Retrieval
  • Model choice
  • Prompt design
  • Refinement

[ DEFINITION ]

What AI Development means here.

AI Development is building AI capabilities into products and workflows: integrating language models, connecting them to a business's own content through retrieval, building the data pipelines that feed them, and evaluating the results. At ELIXIR it covers four capabilities: LLM integration, RAG, data pipelines and evaluation.

  • AI feature or AI agent?

    An AI feature does one job inside a product — answering, summarizing, classifying or extracting — when someone uses it. An AI agent takes action across tools and chooses its next step within set limits. AI Agent Development is its own ELIXIR service, and many products use both.

  • Why retrieval?

    A language model only knows what it was trained on. Retrieval (RAG) gives it the relevant parts of your own content at the moment it answers, so its answers can draw on your documents and data rather than on general knowledge alone.

[ IN SHORT ]

  • Inside the product — AI built into the software and workflows people already use.
  • Your own data — retrieval connects language models to a business's own content.
  • Data comes first — the content a feature relies on is prepared and kept current by a pipeline.
  • Measured, not assumed — evaluation checks how well a feature works, before and after launch.
  • Separate, connected services — agents, automation and the software around a feature stay their own services.

[ WHERE AI FEATURES FALL SHORT ]

Most AI features don't fail on the model — they fail on the data and the checking.

Where they fall short

  • A demo, not a feature — impressive in a demo, but never built into the product people use.
  • General answers — the model knows nothing about the business's own content.
  • Stale or messy data — the content it draws on is out of date or unstructured.
  • No way to tell if it works — quality judged by impression rather than checked.
  • The model first — a model picked before the task it should do is clear.
  • The wrong tool — AI used where fixed rules would be simpler, or where the work needs an agent.

How we approach it

  • LLM integration — language models built into the product, where people already work.
  • RAG — answers drawn from your own content through retrieval.
  • Data pipelines — content prepared, structured and kept current for the model.
  • Evaluation — results checked against real examples before launch and when things change.
  • The task first — the model chosen for the task, the data and the cost involved.
  • The right approach — fixed rules point to Automation; actions across tools point to AI Agent Development.

[ SCOPE ]

AI features built into products — grounded, fed and evaluated.

AI Development at ELIXIR covers four capabilities: LLM integration, RAG, data pipelines and evaluation — applied AI integrated into products and workflows.

  • 01

    LLM integration

    Language models built into an application or workflow, with structured inputs and outputs.

  • 02

    RAG

    Retrieval that brings the relevant parts of your own content to the model when it answers.

  • 03

    Data pipelines

    The content and data a feature relies on, collected, cleaned, indexed and kept current.

  • 04

    Evaluation

    Test sets built from real examples, so quality is checked rather than assumed.

  • 05

    Model choice

    The model chosen per feature, for the task, the data and the cost involved.

  • 06

    Prompt design

    The instructions and structure that shape a model's answers, designed and tested with the feature.

[ WHAT IT CAN BE ]

Kinds of AI features we build — examples, not client work.

  • 01

    Search over your own content

    Questions answered from a business's documents, pages and knowledge.

  • 02

    Summaries

    Long documents, threads or records condensed to what matters.

  • 03

    Classification

    Incoming items sorted, tagged or routed by what they contain.

  • 04

    Extraction

    Structured information pulled out of unstructured text.

  • 05

    Writing assistance

    Drafts and suggestions offered inside the tools people already use.

  • 06

    AI inside a product

    An AI capability built into an application or a SaaS product.

[ FOUR CAPABILITIES ]

Four capabilities behind every AI feature.

LLM integration puts a model where the work happens. RAG grounds it in your own content, data pipelines keep that content ready, and evaluation shows how well the result works.

01LLM integration

Language models, built into the product.

  • Model chosen for the task
  • Inputs and outputs structured
  • Built into existing software

[ IN EVERY AI FEATURE ]

What every AI feature includes.

  • Use case

    The job the feature does and what a good result looks like, defined first.

  • Data review

    The content and data the feature can draw on, and its state today.

  • Model choice

    A model chosen for the task, the data and the cost involved.

  • Retrieval & pipeline

    How content reaches the model, and how it is prepared and kept current.

  • Evaluation set

    Real examples the feature is checked against before launch.

  • Refinement

    Adjustments as models, data and usage change, as part of the ELIXIR Method.

[ RANGE ]

What an AI feature looks like from the outside.

An AI feature inside a product, a retrieval pipeline, a grounded answer and an evaluation set each show a different side of AI development. These layouts illustrate the kinds of views the work involves — they are archetypes, not client work.

  • 01AI feature in a product

  • 02Retrieval pipeline

  • 03Grounded answer

  • 04Evaluation set

Case studies are published here only with our clients' permission.

[ STACK ]

The building blocks of AI features.

Models are chosen per feature — for the task, the data and the cost involved — so no single provider is listed here. These are the building blocks we work with most often.

  • LLM APIs
  • Embeddings
  • Vector search
  • pgvector
  • PostgreSQL
  • Python
  • Evaluation sets

[ WHY ELIXIR ]

AI built by a studio that also builds the product around it.

AI Development is one of ten ELIXIR services. Next to it sit AI Agent Development, Custom Software Development and Automation — separate services, each with its own scope — so an AI feature can be combined with them when a project calls for it, without blurring what each one does.

  • Inside real products

    AI features built into the software people use, not left as demos.

  • Clear boundaries

    AI features, agents, automation and software are each scoped as their own service.

  • One method

    The ELIXIR Method guides every AI feature, from the first task to refinement.

  • Multilingual by design

    Our own site runs in five languages, right-to-left included.

[ THE METHOD ]

The ELIXIR Method, applied to AI development.

Every AI feature moves through the same five phases — the method behind all of our work.

Explore the ELIXIR Method
  1. 01

    DISCERN

    We look at where AI could help — the task, the people doing it and the content and data it would draw on.

  2. 02

    DEFINE

    We define the feature: what it should do, what a good result looks like and how it will be evaluated.

  3. 03

    DESIGN

    We design how it works: the model, the prompts, the retrieval and the data pipeline behind it.

  4. 04

    DEVELOP

    We build it into the product and evaluate it against real examples before launch.

  5. 05

    DISTILL

    We re-evaluate as models, data and usage change, and refine what doesn't serve.

[ WHERE WE WORK ]

US roots. Working internationally.

ELIXIR Creative is a digital innovation and technology studio operating internationally. Our work began with clients in the USA and Canada; today we are expanding into Morocco, the UAE, Germany and other international markets — and this site is itself built in five languages, right-to-left included.

  • casablancamorexpansion
  • new yorkusapresence
  • torontocanpresence

Languages

[ ENGAGEMENT ]

Scoped to the feature, not to a package.

Every project starts with the audit: we look at the task you want AI to help with and come back with a scoped plan. Most AI development work takes one of three shapes.

  • 01

    A first AI feature

    One feature, built and evaluated around a single task.

  • 02

    AI in existing software

    An AI feature added to an application, with the software work handled as Custom Software Development.

  • 03

    Ongoing evaluation

    Features re-evaluated and refined as models, data and usage change.

AI development at ELIXIR

ELIXIR Creative is a digital innovation and technology studio. Our AI development covers applied AI integrated into products and workflows, through four capabilities: LLM integration, retrieval-augmented generation (RAG), data pipelines and evaluation.

An AI feature starts from a task and the content it needs, not from a model. That keeps it useful, keeps its answers tied to the business's own information, and makes it possible to check how well it works.

Where AI Development meets ELIXIR's other services

An AI feature answers, summarizes, classifies or extracts when someone uses it. When the work calls for an agent that takes action across tools, that is AI Agent Development; when a process follows fixed rules across existing systems, Automation is often the simpler fit.

The application around an AI feature is built as Custom Software Development — or as SaaS Development for a subscription product; when AI is one part of rethinking how the business runs, the wider plan belongs to Digital Transformation.

[ FAQ ]

What people ask before building an AI feature.

01

What does AI Development cover?

Applied AI integrated into products and workflows: language models, retrieval and data pipelines. At ELIXIR it covers four capabilities: LLM integration, RAG, data pipelines and evaluation.

02

How is AI Development different from AI Agent Development?

AI Development builds AI features into products — answering, summarizing, classifying or extracting when someone uses them. AI Agent Development builds agents that take action across your tools and choose their next step within set limits. They are separate services that can be combined in one project.

03

What is RAG?

Retrieval-augmented generation: before a language model answers, the relevant parts of your own content are retrieved and given to it, so the answer can draw on your documents and data rather than on general knowledge alone.

04

Can AI use our own documents and data?

Yes, through retrieval. A data pipeline prepares and indexes the content, and the relevant parts reach the model when it answers. Which content is used, and who can access it, is decided per project.

05

Which models do you use?

It depends on the feature: the task, the data involved and the cost. The model is chosen per project rather than fixed in advance.

06

How do you know an AI feature works?

Through evaluation: a set of real examples with what a good result looks like, checked before launch and again when the model, the data or the usage changes.

07

Can AI make mistakes?

Yes. Language models can produce wrong or incomplete answers. Retrieval, clear instructions and evaluation reduce that risk and make it visible; they don't remove it entirely, which is why evaluation continues after launch.

08

Can you add AI to software we already have?

Yes. AI features can be built into existing software; changes to the application itself are handled as Custom Software Development.

09

How much does an AI feature cost, and how long does it take?

It depends on scope: the task, the content and data involved, and how the feature will be evaluated. We don't sell fixed packages — the audit comes first, and it leads to a scoped plan with cost and timeline.

[ RELATED SERVICES ]

Often paired with AI development.

[ START A CONVERSATION ]

Have a task you'd like AI to help with?

Tell us the task, the people doing it and the content it would draw on. We'll map where AI fits.

Start the audithello@elixircreative.pro