Solutions
Generative AI pays off when it is anchored to your real data. BigAI deploys RAG systems and agents inside your infrastructure, with every answer traceable to the source document.
Signals
Policies, procedures and technical docs live across SharePoint, Drive, email and shared drives.
HR and IT spend most of their time answering things already written down.
Fluent, confident, wrong — and with no source to verify, nobody trusted it.
Capabilities
For nearly every internal-knowledge problem, RAG is the right answer: cheaper, instantly current when documents change, and — most importantly — auditable. Fine-tuning makes sense when you need a specific answer style or output format, not as a way to load in knowledge.
BigAI always starts with RAG and only proposes fine-tuning when there is measured evidence that RAG is not enough.
Retrieve the relevant passages first, then generate — always with document name and page cited.
Agents read a request, call internal APIs and finish repetitive steps such as ticket creation, lookups and reconciliation.
Open-weight models served internally — no data leaves your environment.
Users only get answers from documents they are entitled to see, synced from AD/LDAP.
Outcomes
Ranges aggregated across delivered BigAI projects. Specific targets are agreed during the assessment phase.
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| Metric | Before | After | Improvement |
|---|---|---|---|
| Time spent searching internal information | ~2.5 hours/person/week | Under 30 minutes | −80% |
| Repeat questions to HR and IT | 38% of all requests | Under 12% | −58% |
Case studies
From 14 disconnected sources and manual reconciliation to a single governed lakehouse serving both regulatory reporting and internal analytics.
Read case studyReplacing store-manager intuition with per-SKU forecasting that accounts for seasonality, weather and the promotion calendar.
Read case studyFAQ
A BigAI solution engineer will review your current data estate with you, identify the highest-value problem to solve and sketch a realistic roadmap. No commitment.