Retrieval tuned for your domain, not a generic vector dump

RAG Pipeline Development

Grounded answers over your own documents, contracts, tickets, and code, with citations, freshness guarantees, and measured retrieval quality.

What this is

Chunk everything, embed it, stuff the top five results into a prompt: that recipe produces a demo that impresses in a meeting and disappoints in production. Real retrieval quality comes from the parts nobody photographs: document structure aware chunking, hybrid keyword plus vector search, reranking, metadata filters that respect permissions, and an evaluation set that tells you when a change made things worse.

What is included

Structure aware ingestion for PDF, DOCX, HTML, Confluence, Notion, and code
Hybrid retrieval: BM25 plus dense vectors plus a cross encoder reranker
Permission filters applied at query time, not after generation
Citation enforcement so every claim maps to a source span
Retrieval evals: recall at k, mean reciprocal rank, answer faithfulness scoring

How we run it

01Audit the corpus

02Build ingestion

03Tune retrieval

04Ground the generation

05Keep it fresh

Audit the corpus
We sample your documents and measure what is actually retrievable before promising anything.
Build ingestion
Parsers per format, structure aware chunking, metadata extraction, deduplication.
Tune retrieval
Hybrid search plus reranking, measured against a labelled question set drawn from your real queries.
Ground the generation
Citation enforcement, refusal behavior when retrieval confidence is low, no silent guessing.
Keep it fresh
Scheduled reindexing, change detection on source systems, alerts when a source goes stale.

What you receive

Ingestion pipeline with incremental refresh and change detection
Vector store plus keyword index provisioned in your environment
Query API with permission aware filtering
Evaluation notebook and baseline scores you can rerun after any change
Admin view for reindexing, source health, and stale document alerts

Engagement

Typical duration3 to 6 weeks
Indicative investmentFrom $6,500
CategoryAutomate
Starts withFree written quote

Get a free quote

Opens the quote form with RAG Pipeline Development already selected.

Typical stack

pgvectorQdrantElasticsearchCohere RerankClaudeFastAPIUnstructured

Third party names and logos are shown for identification only and do not imply affiliation or endorsement.

Month one is refundable. If the first month does not land we return it. We would rather refund than carry a project neither side believes in.

The return

What this gives back, every month

Ranges, not promises. They come from published 2026 automation benchmarks and our own delivery data, and the audit re-runs them against your actual volumes before you commit anything.

40 to 70hours a monthstaff time no longer spent hunting through documents, tickets, and wikis

$1,500 to $2,700Staff time returned every month

12 to 18 weeksTime to break even

$6,500Your first year cost, all in

Why buy it

The case for doing this now

Your knowledge exists, nobody can find it

The documentation, the contracts, the resolved tickets are all there. The cost is the twenty minutes a person spends locating the right paragraph, several times a day, multiplied by the team.

Search that guesses is worse than no search

An answer with no citation cannot be checked, so it gets checked manually anyway and saves nothing. Citation enforcement is what turns retrieval into time saved rather than time moved.

It compounds with every other system

Once retrieval is grounded and permission aware, the support agent, the sales assistant, and the internal chat all draw on it. The pipeline is infrastructure, not a feature.

Compared to the alternatives

What the same outcome costs elsewhere

Every option below solves some version of this problem. Here is what each one actually costs over twelve months.

First year total cost, including the search time you keep paying if nothing changes

Do nothing for a year

$25,080

Enterprise search

$36,000

Typical agency

$18,000

deepaibots

$6,500

Figures are indicative market ranges for a team of 10 to 60 staff, not quotes from named vendors. Staff time is costed at $38 an hour loaded, meaning salary plus employment cost plus overhead.

Your options	Upfront	Ongoing	Time to value	What you get
Do nothing	None	$2,090/mo in search time	Never	Answers stay inconsistent between staff
Enterprise search license	Setup fee	$3,000/mo	8 to 12 weeks	Generic relevance, your data leaves your estate
Typical AI agency	$10,000 to $25,000	Retainer on top	8 to 14 weeks	Often a vector dump with no eval set
deepaibots	$6,500	Optional from $1,200/mo	3 to 6 weeks	Hybrid retrieval, citations, measured recall

What changed

Why this is worth buying in 2026 and was not in 2024

The scope of this service moved with the tooling. These are the shifts that make the engagement materially better than the same brief eighteen months ago.

01
Hybrid retrieval became the default. Keyword and vector search combined with a cross encoder reranker consistently beats pure vector search, which is what most 2024 era pipelines shipped.
02
Long context windows did not remove the need for retrieval, they changed the job: recall now matters more than aggressive chunking, and reranking carries the precision.
03
Retrieval evaluation tooling matured, so recall at k and answer faithfulness are measurable before launch rather than discovered in production.

FAQ

RAG Pipeline Development: your questions

How do you stop it inventing answers?

Retrieval confidence thresholds plus citation enforcement. Below threshold the system says it does not know and routes to a human. That behavior is tested in the eval suite.

Can it respect our access controls?

Yes. Permission metadata is attached at ingestion and filtered at query time, so a user never retrieves a chunk they cannot see in the source system.

How large a corpus can you handle?

We have shipped pipelines from 2,000 documents to several million chunks. Above roughly 500,000 chunks the architecture shifts toward sharded indexes and async ingestion.