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AI Services

AI services for practical automation, smarter workflows, and product innovation

We take a real AI use case from idea to a governed, working prototype and into production, with data privacy, human-in-the-loop and a clear integration path.

Discuss an AI use case Start with AI discovery
Use cases

Concrete AI use cases, not just AI solutions

We build for specific problems with measurable results, not isolated demos.

AI assistants for internal teams

Assistants that answer questions and act over your internal knowledge and tools.

Content & editorial automation

Drafting, tagging, summarizing and routing content at scale.

Document processing

Extract, classify and validate data from documents and forms.

Knowledge-base assistants

Grounded answers from your docs with citations and guardrails.

Recommendation systems

Personalized recommendations across content and products.

Data extraction & classification

Turn messy inputs into structured, usable data.

AI analytics

Summaries, anomaly detection and insight on top of your data.

Support automation

Deflect and assist on support requests with a human handoff.

AI features in existing products

Embed AI capabilities into the product you already run.

Discuss an AI use case
How we frame a use case

Every scenario, the same six questions

Document processing

Problem
Manual handling of invoices, forms or contracts is slow and error-prone.
Data / input
PDFs, scans and structured exports from existing systems.
AI capability
Extraction, classification and validation with confidence scores.
Business result
Faster processing and fewer manual errors.
Integration
Into your ERP, CMS or workflow via API.
Risks
Low-confidence items routed to a human review queue.

Internal knowledge assistant

Problem
Teams waste time searching scattered internal knowledge.
Data / input
Docs, wikis and tickets, with access controls preserved.
AI capability
Retrieval-grounded answers with citations.
Business result
Faster answers and less repeated work.
Integration
Into chat, intranet or your product.
Risks
Answers grounded in sources to limit hallucination.
From AI idea to working prototype

Seven steps to a governed capability

  1. 01

    AI discovery

    Frame the use case, value and feasibility together.

  2. 02

    Data review

    Assess the data, inputs and access you have.

  3. 03

    Approach & design

    Choose models, retrieval and guardrails for the job.

  4. 04

    Prototype

    Build a working prototype against real inputs.

  5. 05

    Evaluate

    Measure quality, accuracy and edge cases with you.

  6. 06

    Integrate

    Connect it into your systems and workflow.

  7. 07

    Productionize

    Harden, document and ship with monitoring and review.

Quality, security & communication

AI without unnecessary risk

Governance is part of the build, not an afterthought.

Data privacy by design
Access control
Human-in-the-loop review
Clear model limitations
Hallucination risk managed
Auditability
Documentation by default
Safe integration
Proof

Relevant work

Metrics are illustrative until confirmed with each client.

Media & Publishing · USNDA

Editorial platform automation

AI-assisted classification and review with a human review queue.

View case study
Healthcare · EUNDA

Secure internal healthcare tool

AI-assisted workflow with access controls and an audit trail.

View case study
Enterprise · USNDA

Enterprise platform modernization

Automation shipped alongside platform modernization.

View case study
FAQ

AI services FAQ

What AI use cases do you actually build?

Internal AI assistants, content and editorial automation, document processing, knowledge-base assistants, recommendation systems, data extraction and classification, AI analytics, support automation, and AI features inside existing products.

How do you start an AI engagement?

With AI discovery: we frame a concrete use case, the data and inputs, the AI capability, the business result, the integration path and the risks, then move to a working prototype.

How do you handle data privacy and risk?

Data privacy, access control, human-in-the-loop review, explicit handling of model limitations and hallucination risk, auditability, documentation and safe integration are part of every AI engagement.

Can you add AI features to our existing product?

Yes. We integrate AI into existing products and workflows with a clear path through your stack, rather than building isolated demos.

What does a typical AI result look like?

A governed, working capability in production, with a measurable business result and a human review path where it matters, not a one-off proof of concept.

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Have an AI use case in mind?

Start with AI discovery and we will frame the problem, data, capability and a path to production.

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