Service

Data & retrieval pipelines

We turn scattered documents and records into a knowledge base that returns precise, cited answers, built on vector search and retrieval that ranks the right source first.

Book a discovery call Cited answers · Ranks the right source first · Stays current as content changes

Who it's for

This is for small and mid-sized businesses sitting on documents, records, and wikis that hold the answers but are painful to search. It suits teams whose people and agents keep hunting for information that already exists somewhere, and who want answers grounded in that content, with sources attached.

How it works

How a pipeline gets built

We build the full path from raw content to a grounded answer, then keep it current as your source material changes.

Stage 1

Ingest and chunk

We pull content from wherever it lives and split it into passages sized for accurate retrieval, tuned to your document types.

Stage 2

Embed and index

We build the embeddings and vector index that find the most relevant passages for any question, quickly and at scale.

Stage 3

Rank and ground

We rank retrieved passages and feed the best into the model, so answers cite real sources instead of inventing them.

Stage 4

Refresh

We build a refresh process that re-ingests and re-indexes as content changes, so answers reflect what is true now, not last quarter.

Why not just prompt-stuff:a model can only read so much at once, and cramming everything in is slower, costlier, and less accurate. Retrieval finds the few passages that actually matter.

What you get

Each piece below connects your existing knowledge to answers people and agents can check.

Retrieval pipeline

The full path connecting your knowledge sources to search and grounded answers, end to end.

Ingestion and chunking

A process that pulls from your document stores, wikis, ticketing, and databases and splits content into passages the index can search.

Ranked vector index

A vector index with ranking that surfaces the right passages first for each question.

Cited answers

Responses grounded in retrieved content with citations back to the source, so anyone can check where an answer came from.

Refresh process

A re-ingest and re-index routine that keeps the index current as source content changes.

Outcomes

What changes after

Questions get precise answers drawn from your own content, with sources attached.

Staff and agents stop hunting through documents for information they need.

Answers stay current because the index updates as source material changes.

Not sure retrieval is what you need?

A short call is enough to tell whether a retrieval pipeline fits your content, or whether a simpler search setup would do.

Book a discovery call

6 mo

To CJIS compliance

100%

Data isolation

Case study

A compliant AI assistant for law enforcement agencies

The public-safety assistant Evertech built answers from each agency's own data with complete isolation, which is exactly what a well-built retrieval pipeline makes possible: grounded, cited answers from your content.

Read the case study

FAQ

Questions buyers ask

What is RAG, in plain terms?

Retrieval-augmented generation. Instead of relying on a model's training, the system first retrieves the most relevant passages from your own content, then has the model answer using them. Evertech builds that retrieval layer, so answers are grounded in your documents and records, with citations, rather than generated from the model's general memory.

Why not just put everything in the prompt?

Because it does not scale and it degrades accuracy. A model can only read so much at once, and stuffing it with everything makes it slower, costlier, and more likely to miss the point. Retrieval finds the few passages that actually matter for each question, which is faster, cheaper, and more precise.

How do answers stay accurate over time?

The pipeline re-ingests and re-indexes content as it changes, so retrieval reflects your current documents rather than a stale snapshot. Evertech builds the refresh process into the system and grounds answers with citations, so anyone can check the source. When source material updates, the answers follow it.

Where does the source content come from?

Wherever your knowledge already lives: document stores, wikis, ticketing systems, databases, PDFs, and internal tools. Evertech builds ingestion that pulls from those sources and normalises them into passages the index can search. If a source has an API or an export, it can almost always feed the pipeline.

Related services

Pairs well with

LLM integration & tuning

Evertech embeds language models into your stack and tunes them on your data for accuracy, tone, and safe, predictable behaviour

AI agents & assistants

Evertech builds AI agents and in-app assistants that complete real tasks by calling your tools, with human review at the points that matter

Let's find what's worth building

A short discovery call to understand your business and show you where software and AI would pay off first.