GIVE AI THE RIGHT DATA — AND ONLY THAT.
Cleansing, structuring and access control for the data your first use cases need, plus knowledge bases that answer from your documents and respect who may see what. No year-long centralization project.
THIS DOMAIN INCLUDES //
DATA AUDIT & CLASSIFICATION
WHAT IT IS //
A data audit inventories what data you hold, where it lives, who owns it, how sensitive it is and how good it is — the basis for deciding what AI may touch and where it may run.
WHAT WE DO //
Starting from the data classes set in the audit, we map the data your target use cases depend on, label it down to the field and score its quality and accessibility. The cleanup scope then follows from what we find.
DATA CLEANSING & STRUCTURING
WHAT IT IS //
Data cleansing removes duplicates, errors and inconsistencies; structuring turns scattered files, emails and records into consistent, machine-readable data.
WHAT WE DO //
We clean and structure only the data your first use cases depend on, and use AI itself to extract structure from material that was unusable before, such as scans, call recordings and scattered documents.
DOCUMENT DIGITIZATION
WHAT IT IS //
Digitization turns paper archives, scans, PDFs and recordings into searchable, structured knowledge an AI system can work with.
WHAT WE DO //
We digitize and structure document libraries with text recognition, extraction, tagging and deduplication, then index them so an assistant can answer from them with a link back to the source.
ACCESS CONTROL & DATA POLICY
WHAT IT IS //
Access control for AI means the system only retrieves and shows what the person asking is already allowed to see. A data policy defines who owns which data, how it is classified and how long it is kept.
WHAT WE DO //
We put role-based, permissions-aware access in front of the data, write the data-management rules with your teams (ownership, classification, retention, quality) and keep an audit trail of what was accessed.
KNOWLEDGE BASES
WHAT IT IS //
A knowledge base lets an AI assistant answer from your own policies, contracts, manuals and project history, with a link to the source, instead of from general training data.
WHAT WE DO //
We index your documents, keep them in sync with the systems where they live, and ground every answer in a cited source, inside the deployment model you chose.
PIPELINES & DATA PLATFORM
WHAT IT IS //
Data pipelines and connectors move data from your CRM, ERP, document stores and databases into the AI layer in a controlled, repeatable way. A central data platform is the next step where one shared layer for many use cases is justified.
WHAT WE DO //
We build the connectors and sync schedules, keep sensitive fields out of scope or masked, and monitor the pipelines. Where the use cases justify it, we build a central data platform in stages, one use case at a time.
PROBLEM //
- Data sits in silos (CRM, ERP, shared drives, email, paper archives) in formats AI can't use.
- Nobody knows which data is sensitive, so either everything is blocked or too much is exposed.
- A "clean the data first" project runs for a year and delivers nothing the business can use.
OUR APPROACH //
We clean what the use case needs, not what the estate contains. We start from the target workflow, trace the data it depends on, classify and fix that slice, and wrap access control around the rest. Where a central data platform is justified, we build it in stages, behind real use cases.
WHAT WE DO //
DATA AUDIT
- Inventory and ownership map
- Field-level sensitivity labels
- Quality and accessibility scoring
CLEANSING & STRUCTURING
- Deduplication and standardization
- Extraction from scans, documents and calls
- Master data where it blocks the use case
ACCESS CONTROL & POLICY
- Role-based, permissions-aware retrieval
- Sensitive fields masked or excluded
- Data-management rules and audit trail
KNOWLEDGE BASES
- Document indexing and sync
- Answers grounded in cited sources
- Runs in the environment you chose
WHAT YOU GET //
- A classified data map for your target use cases
- Use-case data cleaned, structured and access-controlled — not the whole estate
- A knowledge base that answers from your documents and respects permissions
- Data-management rules your teams can follow
- Pipelines your team can run and extend
DATA// Only ~6% of deployments started with AI-ready data — winners built access layers over scattered systems instead of waiting for clean, centralized stores. — Stanford Digital Economy Lab, 2026
PATH// this service is scoped in DESIGN and opens IMPLEMENT whenever the audit finds the data isn't ready — and feeds every phase after it — see all services and how it lands on the calendar.
- AI Audit & Strategy — where the data work is scoped
- Private & On-Prem AI — where the knowledge base runs
- AI Automation & Workflows — document processing on clean inputs
- Why 95% of AI pilots fail — what the audit is designed to prevent