Turn unstructured documents into a searchable, private knowledge base. Core-AI builds end-to-end document search systems that transform PDFs, wikis, and databases into structured retrieval your firm can query by meaning, not just keyword.
What We Build #
Most firm content lives in formats a search box can’t consume directly — scanned PDFs, version-locked wikis, document-management silos, correspondence exports. We build the pipeline that bridges that gap:
- Ingestion — connectors for the systems where your documents already live.
- Processing — OCR, layout-aware parsing, and a chunking strategy tuned to your content type.
- Indexing — representations that let the system search by meaning, not just by matching words.
- Storage — a private index sized to your corpus and query volume, kept entirely on your infrastructure.
- Retrieval — hybrid search (meaning + keyword + metadata filters) for accurate, sourced results.
Everything runs on your infrastructure. Your documents and their index stay inside your network.
Outcomes Our Clients Realize #
- Faster knowledge discovery — Reduce the time spent searching for information by up to 90%.
- Automated report generation — Summarize and synthesize raw documentation into briefs, contracts, and audit reports.
- Semantic search across silos — Move beyond keyword matching to true conceptual understanding of your content.
- Compliance-grade traceability — Every retrieved chunk is auditable back to its source document, page, and section.
Processing Pipeline #
Docs
Processing
Index
Storage
Response
Content Types We Handle #
- PDFs — including scanned documents (OCR), forms, technical manuals, and contracts.
- Wikis & docs — Confluence, Notion, SharePoint, internal Markdown repositories.
- Code & technical content — Git repositories, API documentation, runbooks.
- Structured data — CSV, JSON, SQL exports — indexed for hybrid retrieval.
- Email & messages — archived correspondence indexed for compliance lookups.
Related Services #
- Enterprise AI Chatbots — the conversational interface over your knowledge base.
- AI Agents — assistants that draw on the retrieved content.
- AI Infrastructure Deployment — the infrastructure your pipeline runs on.
Frequently Asked Questions #
What document formats do you support for ingestion?
We ingest PDFs (including scanned documents via OCR), Microsoft Office files (Word, Excel, PowerPoint), Confluence and Notion pages, SharePoint content, Markdown repositories, CSV and JSON exports, and archived email. If your content lives somewhere, we build a connector for it.
How does search understand meaning rather than just keywords?
The system grounds its answers in your actual documents rather than a model’s general training. Instead of relying on memory — which can be outdated or simply wrong — it retrieves the relevant passages from your own knowledge base in real time and uses them as context. The result is accurate, up-to-date answers with a traceable source.
How do you ensure retrieved information is accurate and not invented?
Grounding the model in retrieved content reduces the risk of an invented answer. We also implement source attribution — every answer cites its source document and section — and evaluation before launch that measures retrieval accuracy and answer faithfulness.
Can the system handle documents in multiple languages?
Yes. We configure the system so documents in different languages are represented in a shared space, enabling cross-lingual search. Users can query in one language and retrieve relevant content from documents in another.