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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 working prototype and into production — with data privacy, human-in-the-loop review and a clear integration path.

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Use cases

AI use cases we actually build

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.

Cybersecurity · USNDA

NEXUS: AI threat intelligence platform

LLM extraction pipeline, entity graph and public security-events surface for a media group.

View case study
LegalTech · USNDA

AI tool for insurance legal opinion drafting

Five specialised LLM assistants turning 250-page PDF bundles into structured legal opinions.

View case study
Media & Publishing · USNDA

AI content automation for a multi-brand publisher

~100 AI-assisted product reviews per week with tone panel and publish-quality gates.

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 your engagement models differ?

We offer three core models: Staff Augmentation – you hire individual engineers to join your existing team under your management; Dedicated Team – we build a full cross-functional pod (including PM, QA, and designers if needed) for your product; and End-to-End Outsourcing – we take full ownership of delivery and project management. For AI integrations, we also provide specialised AI pods for rapid prototyping and production-grade deployment.

How do you protect our data and intellectual property?

We sign an NDA before any sensitive details are shared. All contracts include comprehensive IP protection clauses, and we follow strict internal security protocols for access control, data handling, and code confidentiality – without compromising your speed or flexibility.

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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