AI Search & RAG
Semantic Search and Grounded Answers Over Your Own Data
Search That Understands Meaning, Over Your Own Data
Most business data is unsearchable in any useful way. Documents, contracts, tickets, recordings, scans, and archives pile up faster than anyone can find anything in them. AI search fixes that: it indexes your content by meaning, not just keywords, and lets people ask plain questions and get answers grounded in your own material, with citations. We have built and run search by meaning over a video archive of about two petabytes, and we build it at every size below that.
Semantic Search, Not Just Keywords
Traditional search matches the exact words you type, so it misses anything phrased differently. Semantic search converts your query and your content into embeddings (numeric representations of meaning) and ranks by similarity, so it finds the right result even when the wording does not match. The strongest systems do both: semantic search for recall, keyword and full-text search for precision, blended into one ranked result. That hybrid retrieval is the default we build, on PostgreSQL with pgvector and tsvector working together.
RAG: Answers Grounded in Your Documents
Retrieval-augmented generation connects a language model to your data. A question first retrieves the most relevant passages from your own content, and the model answers from those passages with links back to the source. You get an assistant that actually knows your knowledge base instead of guessing from generic training data. Crucially, how it is built determines whether you can trust it: on the largest system we have built, the chat layer extracted structured filters from a question rather than letting the model author raw queries, a deliberate reliability and safety choice. On every system we build, each answer cites its sources.
Search Only What Each Person May Read
Most retrieval systems search everything and trust the model to be discreet. The systems we build check permissions before the search runs: each folder carries an ordered list of allow and deny entries, and a passage the person asking may not read never reaches the model. PremAgentic, the open-source knowledge server we built, adds three more checks on every search. Superseded versions are held back unless someone asks for history, machine-written documents wait for a person to review them, and documents past their stale date are held from assistants. For what goes wrong when a permission filter meets an approximate vector index, see permission filters and pgvector.
Multimodal: Text, Images, Audio, and Video
Your data is not only text, so the index should not be either. We build multimodal pipelines that index:
- Audio and video - speech transcription (Whisper) so spoken content becomes searchable text
- Images and frames - visual embeddings (CLIP-class models) so you can search by what a picture shows
- Scanned and image text - OCR that turns documents and screenshots into searchable content
- People and objects - optional face and object recognition for tagging and retrieval
Everything lands in one vector store, so a single query reaches across formats at once.
What We Build
- An embedding pipeline tuned to your content and update cadence
- A hybrid vector + full-text index (PostgreSQL / pgvector) with faceted filtering
- A natural-language query layer that is safe by construction
- Permission checks that run before retrieval, not after
- A clean API your existing apps and a custom intranet can call
- The data architecture to keep the index current as your content grows
On-Premises or Cloud
The whole stack can run on hardware you control: embeddings, index, and the model itself, so confidential data never leaves your environment, and heavy workloads stop racking up per-token cloud bills. See local and on-premises AI for that path. When cloud or hybrid fits better, we build it on AWS, including Bedrock, with the same architecture.
Frequently Asked Questions
What is the difference between AI search and regular keyword search?
Keyword search matches the exact words you type. Semantic (vector) search matches meaning: it finds the right result even when the wording is different, because both the query and the content are converted into embeddings and compared by similarity. The best systems use both, semantic for recall and keyword for precision, and blend the rankings. That hybrid approach is what we build.
What is RAG (retrieval-augmented generation)?
RAG connects a language model to your own data. Instead of answering from training data alone, the model first retrieves the most relevant passages from your documents, then answers grounded in them, with citations back to the source. It is how you get an AI assistant that actually knows your contracts, manuals, tickets, or archive, without retraining a model.
Will the model just make things up?
That is the risk RAG is designed to reduce, and how you build it matters. On the largest deployment we have built, the natural-language layer extracted structured filters from a question rather than letting the model write raw database queries, a deliberate safety and reliability choice. On every system we build, answers cite their sources so a person can verify them. Grounded retrieval plus citations is the difference between a useful assistant and a confident guesser.
Can it search images, audio, and video, not just text?
Yes. We build multimodal pipelines: speech transcription for audio and video, visual embeddings for images and frames, OCR for scanned and image text, and optional face or object recognition. Everything is indexed into the same vector store, so one query can reach across text, image, and media at once.
Does this have to run in the cloud?
No. The entire pipeline, embeddings, vector index, and the language model, can run on hardware you control for privacy and cost. See our local and on-premises AI page. We also build cloud and hybrid versions when that fits better. The architecture is the same; only where it runs changes.
Have a Pile of Data Nobody Can Search?
Tell us what your content is and what people need to ask of it. We will tell you whether semantic search, RAG, or a multimodal index fits, and what it would take to make your own data answerable.