Softment

AI Development

RAG Development Services

We build Retrieval-Augmented Generation systems that answer from your content, not guesses. Expect clean ingestion, tuned retrieval, citations, and an architecture built for ongoing updates.

TimelineTypical: 3–7 weeks (scope-dependent)
Starting atCA$1.8k
Security-first AI integrations • Evals + logging + guardrails included

Overview

What this service is

We convert your sources—PDFs, wikis, help centers, tickets, and databases—into a searchable knowledge layer with metadata and versioning.

Retrieval is designed for trust: citations/excerpts in responses, permission-aware access, and fallback behavior when the system is uncertain.

We tune chunking, filters, hybrid search, and reranking against a test set so retrieval quality improves predictably as your content grows.

Benefits

What you get

Higher accuracy for customer and internal answers

RAG pulls relevant context so responses stay grounded in real, current content.

Trust-building citations

Users can verify sources and follow links to the exact excerpt behind an answer.

Faster onboarding and support resolution

Teams find answers quickly across large doc sets, reducing repetitive manual searching.

Permission-aware knowledge access

Access rules and role boundaries can be respected when needed for internal systems.

Measurable, testable improvements

Eval sets and regression checks make retrieval tuning safer and more repeatable.

Features

What we deliver

Ingestion pipeline

Parsers, normalization, and update workflows for PDFs, docs, web content, and structured sources.

Chunking + metadata strategy

Chunking tuned to your domain plus metadata that supports filters and access control.

Vector DB setup

Pinecone/Qdrant/Weaviate/pgvector schemas, indexes, and performance-ready configuration.

Hybrid search + reranking

Combine keyword + vector retrieval and rerank results for better relevance and fewer misses.

Citations and excerpts in answers

Answer formatting designed for trust, with consistent source attribution and links.

Monitoring + eval loop

Quality checks, retrieval diagnostics, and feedback signals to keep performance stable over time.

Process

How we work

1
2–4 days

Source audit

We review your content sources, access rules, and target queries to define the retrieval plan.

2
4–10 days

Ingestion build

We implement parsing, chunking, metadata, and update workflows for your chosen sources.

3
1–3 weeks

Retrieval + generation

We wire hybrid retrieval, reranking, prompting, and response formatting with citations.

4
3–7 days

Evaluation

We create a query set and iterate on retrieval settings to hit accuracy and latency targets.

5
1–3 days

Launch

We deploy, monitor, and document how to maintain and evolve the knowledge base over time.

Tech Stack

Technologies we use

Core

EmbeddingsPinecone / Qdrant / Weaviate / pgvectorHybrid search + rerankingLangChain / orchestration

Tools

OpenAI / AnthropicPostgreSQL + RedisTracing + eval datasets

Use Cases

Who this is for

Support knowledge assistant

Answer from docs and help center content with citations and clean escalation to humans.

Internal policy and SOP search

Find answers across handbooks, runbooks, and internal docs with role-aware access controls.

Product documentation copilot

Help users and engineers locate implementation details, examples, and API references quickly.

Sales enablement assistant

Answer product questions from approved collateral and generate structured summaries for follow-ups.

Document-heavy research workflows

Search across large PDF libraries with citations and versioning for consistent results.

FAQ

Frequently asked questions

PDFs, docs, web pages, help centers, wikis, tickets, and structured sources like databases or APIs. We tailor parsing and chunking per format.

Yes. We include citations and excerpts wherever possible so users can verify the source behind an answer.

Yes. We can implement permission-aware retrieval and filtering aligned to your RBAC model when your access rules are available.

We build update jobs and re-indexing workflows so new or changed documents are reflected reliably without manual effort.

Yes. We can embed RAG into your product via an API, a widget, or an internal tool experience depending on your stack.

Ready to start?

Need answers grounded in your own data?

Send sample docs + target workflows and we’ll recommend the right RAG stack, timeline, and rollout plan.

Citations + measurable quality checks included.