How is this different from AI integration?

Integration connects a model to systems you already run. Development means building the application itself — interface, data model, business logic and the AI layer together. If you need a model wired into your existing CRM, that is integration. If you need a new tool that did not exist before, that is development.

Do we need our own model, or is an API enough?

An API is enough for almost everyone. Training a model from scratch is rarely justified outside research budgets. Where general models genuinely fall short on your domain, fine-tuning or careful retrieval usually closes the gap for a fraction of the cost, and we will tell you which applies before you commit.

How do you stop an AI feature from being unreliable?

By treating model output as untrusted input. Responses are constrained to a schema, validated against business rules, retried or repaired on failure, and measured against a test set of real examples so a prompt or model change cannot silently degrade quality.

What does the handover include?

Source code in your repository, infrastructure in your accounts, documentation covering architecture and prompts, and the evaluation set used to test quality. Nothing depends on us continuing to be involved.

AI Development

AI Development Company: Software Where the Model Is the Product

Vedixx builds custom AI applications — internal tools that reason over your data, AI features inside existing products, and pipelines that turn unstructured input into structured records. We engineer them like software: validated output, evaluation sets, monitoring, and a repository you own.

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What does an AI development company do?

An AI development company builds custom software in which an artificial intelligence model performs part of the core function, rather than adding a model to an existing product as a feature. The work includes designing the data model, building the application and interface, engineering the model layer with validation and evaluation, and deploying the result into infrastructure the client owns.

What's included

Discovery & feasibility

We establish whether a model can do the job reliably enough to be worth building, tested against your real data before anyone writes application code.

Application development

The full product: data model, business logic, interface and API. Built with React, Next.js and Node so it is maintainable by any competent team, not just us.

Model layer engineering

Prompt and context architecture, structured output, retries and repair passes, plus fallbacks for when the model is unavailable or returns nonsense.

Evaluation harness

A scored test set of real inputs and expected outputs, run against every change, so you can prove a new prompt or model version improved things rather than hoping.

Deployment & infrastructure

Shipped into your cloud accounts with environments, secrets management and observability configured, not left as a prototype on someone’s laptop.

Documentation & handover

Architecture notes, prompt rationale, evaluation results and runbooks. The test of a good handover is whether another developer can safely change it without us.

How we build an AI product

The feasibility step is the one most projects skip, and it is the cheapest place to discover that a model cannot do the job reliably enough.

1
Define & measure
What the feature must do, and what "good enough" means as a number. Without this, AI projects have no finish line.
2
Feasibility test
A throwaway prototype against your real data, scored on the metric above. Cheap to run, and it kills bad ideas before they cost a quarter.
3
Build the application
Data model, logic, interface and API — with the model layer isolated behind an interface so it can be changed independently.
4
Harden
Validation, retries, fallbacks, rate-limit handling, cost caps and the regression evaluation set.
5
Ship & observe
Deployed to your infrastructure with logging of quality, latency and cost per feature so regressions surface early.

Where it pays for itself

Internal knowledge and research tools

The problem: Expertise is locked in documents, tickets and past projects. Answering a routine internal question means interrupting whoever has been there longest.
What we build: A permission-aware application that indexes your internal sources and answers questions with citations back to the source document.
The result: Institutional knowledge becomes searchable rather than dependent on tenure.

AI features inside an existing product

The problem: Customers expect AI capability in your product, and a bolted-on chat widget does not fit the workflow or the data model.
What we build: A feature designed into your product properly — using your data, respecting your permissions model, and matching your existing interface.
The result: A capability that fits the product rather than sitting beside it.

Unstructured-to-structured pipelines

The problem: Critical information arrives as PDFs, emails and transcripts, and turning it into database rows is somebody’s full-time job.
What we build: A processing pipeline that extracts to a defined schema, validates against business rules, and routes anything uncertain to a review queue.
The result: Clean structured data from messy input, with exceptions surfaced rather than buried.

Content generation at scale

The problem: Thousands of product descriptions or listings need writing to a consistent standard, and doing it manually is not economic.
What we build: A generation pipeline grounded in your real product data with brand-voice constraints, validation, and human approval before publication.
The result: Catalogue-scale content that stays consistent and factually tied to your own data.

How it works

1
Define the metric
What the system must achieve and how accuracy will be judged. AI projects without a defined success measure cannot be finished, only abandoned.
2
Prove feasibility cheaply
A rough prototype against real data, scored against the metric. If it cannot reach a useful score here, no amount of engineering will rescue it later.
3
Design the architecture
Data model, permissions, and a model layer isolated behind an interface so providers can change without touching the application.
4
Build and harden
Application, validation, evaluation harness, cost controls and observability, developed together rather than bolted on at the end.
5
Deploy and hand over
Into your infrastructure, with documentation, runbooks and the evaluation set so your team can maintain and extend it.

Typical timeline

Phase	Duration	What you get
Discovery	1 week	Defined scope, success metric, data assessment and technical approach.
Feasibility prototype	1–2 weeks	Scored prototype against real data, with a go or no-go recommendation.
Application build	4–10 weeks	Working product with the model layer, interface and integrations.
Hardening & evaluation	2–3 weeks	Regression suite, validation, cost controls and monitoring.
Deploy & handover	1 week	Production deployment in your accounts plus full documentation.

Build a custom AI application, or buy a tool?

Building is not automatically right. The question is whether your requirement is genuinely specific to your business or simply feels that way.

	Off-the-shelf tool	Custom build
Best when	Your process resembles everyone else’s and a product already covers it.	The advantage is in your own data, workflow or domain rules.
Time to value	Days. Configure and go.	Weeks to months, depending on scope.
Cost shape	Per-seat subscription that grows with headcount.	Build cost up front, then infrastructure and model usage.
Fit to your workflow	You adapt to the tool.	The tool matches how you actually work.
Ownership	Vendor holds the roadmap and your data.	You own the code, the data and the direction.

Technologies we build on

Application

Next.js
React
TypeScript
Node.js

AI layer

OpenAI (GPT)
Anthropic (Claude)
Open-weight models
Vector search

Data

PostgreSQL
Supabase
Object storage
Queues

Quality

Evaluation harnesses
Schema validation
Cost logging
Observability

Systems we connect

Internal REST & GraphQL APIsPostgreSQL & MySQLGoogle WorkspaceSlackNotionHubSpotSalesforceStripeS3-compatible storage

Who this is for

SaaS & technology

AI capability built into an existing product to the same standard as the rest of the codebase.

Professional services

Research, drafting and document review tools shaped around how the firm actually works.

Finance & accounting

Extraction and reconciliation pipelines where a wrong figure is worse than no figure.

E-commerce

Catalogue enrichment, description generation and review analysis at scale.

Healthcare admin

Documentation and intake tooling built with the data-handling those obligations require.

Media & publishing

Editorial tooling, tagging and archive search across large content libraries.

What drives the cost

Custom AI development is priced like software development, because that is what it is. The model API is a small line item; the application around it is the work.

Scope of the application

A single-purpose internal tool is a different project from a multi-user product with permissions, billing and an admin surface.

Reliability bar

A tool used by five colleagues who can spot a bad answer needs far less hardening than a customer-facing feature acting unattended.

Data readiness

If the data the model needs is already accessible and clean, the build starts immediately. If it is scattered across systems, that comes first.

Interface complexity

A single screen is quick. Dashboards, review queues, admin tooling and permissions each add real design and build time.

Evaluation depth

Building a proper scored test set takes time and is the difference between knowing quality and guessing at it.

Ongoing ownership

Whether we hand over completely or stay involved changes how much documentation and simplification is warranted up front.

Feasibility sprint

Prove a model can hit the required accuracy on your data before committing to a full build.

Product build

Scoped development with hardening, evaluation and deployment into your infrastructure.

Retained development

Continuous iteration for teams treating AI capability as an evolving part of the product.

What you end up with

An application your team owns, in your own repository
A measured accuracy figure instead of an impression
Model output validated before it reaches users or records
A regression suite that catches quality drops on every change
Infrastructure and secrets under your control
Documentation good enough for another developer to take over

Key takeaways

Define the success metric before writing code. AI projects without one cannot be finished, only abandoned.
Prove feasibility on real data with a throwaway prototype — the cheapest possible place to discover the idea does not work.
Almost nobody needs to train their own model. An API plus good retrieval closes most domain gaps.
Treat model output as untrusted input: schema-validate, retry, repair, fall back.
Without an evaluation set you cannot tell whether a change improved or degraded the product.

Related services

AI Automation

Automations that keep running after the engagement ends.

AI Integration

Connecting AI models to the systems you already run.

Workflow Automation

Multi-step processes that run without supervision.

Business Process Automation

End-to-end process redesign, then automation.

CRM Automation

Pipeline hygiene and routing that maintains itself.

Marketing Automation

Lifecycle campaigns triggered by real behaviour.

FAQ

Questions? Answered.

Custom software where a model is part of the product rather than an add-on: internal tools that reason over your data, customer-facing features that generate or classify content, and pipelines that turn unstructured input into structured records. The deliverable is an application you own, not a subscription to somebody else’s.

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