Home Programs AI-Ready Data & Knowledge
Program
Give AI data and knowledge it can trust
Most AI failures are data failures. We make your structured data, documents and multilingual content usable, governed and measurable for AI assistants and agents.
Last reviewed: 29 September 2026
One quarter
to AI-ready priority knowledge sources
Every
production use case backed by an evaluation set
Answer accuracy
tracked against agreed thresholds
Typical target ranges, agreed at baseline.
Your AI is only as good as what it can read
Critical knowledge lives in PDFs, email threads, legacy systems and people’s heads. Master data differs between ERP and CRM. Product and service content exists in several languages, maintained unevenly. When AI assistants and agents draw on this material, they produce confident answers that are sometimes wrong — and nobody measures how often.
- Critical knowledge locked in documents, email and legacy systems
- Inconsistent master data across ERP and CRM
- Multilingual content maintained unevenly across markets
Fixing everything first is not the answer. Fixing what each use case needs is.
Why data programs don’t unlock AI
| Where it stalls | What it costs | Our answer |
|---|---|---|
| Big-bang data lake projects | Years of effort before any AI value | Use-case-led readiness, source by source |
| No evaluation | Silent quality decay and lost trust | Test sets and accuracy thresholds for every use case |
| English-only thinking | AI that fails in other markets and languages | Multilingual content, terminology and quality management |
Where we typically start
| Area | Business KPI | Where it runs |
|---|---|---|
| Document intake and classification | Hours per document, error rate | Document stores, ERP |
| Technical and product documentation for AI search | Time to answer, first-time-right rate | Knowledge bases, service tools |
| Master data clean-up for agents | Duplicate and error rates | SAP, Salesforce |
| Multilingual knowledge bases | Answer accuracy by language | Knowledge bases, CRM |
| Evaluation and human review programmes | Accuracy, escalation rate | All AI use cases |
A heritage in quality-critical content and data
Multilingual by design
Our team comes from years of running enterprise multilingual content and data operations.
Human review at scale
We know how to combine automation with expert review without losing speed.
Terminology and domain accuracy
Consistent terms across languages and systems, so AI answers stay precise.
How the program runs
1
Data Readiness Check
2–3 weeks
Sources, quality, access and gaps for the priority use cases.
2
Foundation design
2–4 weeks
Target structure, metadata, access rules and evaluation approach.
3
Build
6–12 weeks
Ingestion pipelines, clean-up, knowledge bases and evaluation sets.
4
Operate
Quality monitoring, content updates and human review, in-house or managed.
Data governance for AI
- Access controls that follow the source system’s permissions
- Personal data identified, minimised and protected
- Source traceability for AI answers
- Version control and freshness rules for knowledge content
- Evaluation results recorded for every release
Find out which of your data sources are holding AI back.
See it in practice
Client Zero
AI-assisted multilingual content operations
How we run AI in our own production pipeline for structured technical content, with measured quality at every step.
Read the story
How to start and how it grows
1
Data Readiness Check
2–3 weeks
A gap report and plan for your priority use cases.
2
Foundation build
2–4 months
Priority sources AI-ready, evaluation in place.
3
Next
Feeds directly into the Enterprise AI Transformation Program.
Commercial options: fixed-fee check · milestone-based build · managed content and evaluation services.
Questions about AI-ready data
Do we need a data lake before we can use AI?
No. Most AI use cases need a small number of well-prepared sources, not a complete data platform. We prepare the sources each use case needs, in priority order, and build on what you already have.
How do you measure whether AI answers are accurate?
We build evaluation sets — representative questions or tasks with verified answers — for each use case. AI output is tested against them before release and after every significant change, with results reported against agreed thresholds.
Can you handle content in several languages?
Yes. Multilingual content is our heritage. We manage terminology, translation quality and consistency across languages so that AI answers are equally reliable in every market you serve.
How do you protect personal data in documents?
We identify personal data during ingestion, apply minimisation and access rules, and keep sensitive content out of AI indexes unless there is a clear lawful basis and need.
Show us the use case. We’ll show you what the data needs.
- 30 minutes with a senior lead
- No sales script — a working conversation
- A clear view of where AI can pay back first