AI Integration

We implement automated workflows and integrate Artificial Intelligence APIs.

Overview

We integrate language models into existing products where they solve a measurable problem. We start by defining what a correct answer looks like and how it gets checked — without that, an AI feature is impossible to improve and impossible to defend to a customer.

AI Integration

We implement automated workflows and integrate Artificial Intelligence APIs.

Claude APIOpenAI APILangChainpgvector
How we work
01

Discovery

We map the current system, its constraints and the business goals before proposing anything.

02

Architecture

We design the structure, data model and integration points. Decisions get documented, not improvised.

03

Implementation

Delivered in short iterations, with reviewed code and a staging environment you can see at any time.

04

Testing & QA

Automated tests plus performance and security checks before every release.

05

Launch & support

We ship with a rollback plan, then stay on for monitoring and iteration.

DiscoveryLaunch & support

Use cases

Search across internal documentation

Answers with the source cited, over contracts, procedures or ticket history.

Automatic classification and routing

Triage of emails, tickets or requests, escalating to a human when confidence is low.

Structured extraction from documents

Invoices, contracts and forms turned into data you can query.

Key benefits

01

Evaluation before launch

We build a test set and a score. You know how often it gets things wrong, not just that it sometimes does.

02

Cost per request stays visible

We pick the model on price-to-result and monitor consumption like any other infrastructure resource.

03

Your data stays yours

We define explicitly what goes to the model provider, what stays local and what is anonymised first.

Technologies

Claude APIOpenAI APILangChainpgvectorWhisperRAGPython

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