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.
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
Source audit
We review your content sources, access rules, and target queries to define the retrieval plan.
Ingestion build
We implement parsing, chunking, metadata, and update workflows for your chosen sources.
Retrieval + generation
We wire hybrid retrieval, reranking, prompting, and response formatting with citations.
Evaluation
We create a query set and iterate on retrieval settings to hit accuracy and latency targets.
Launch
We deploy, monitor, and document how to maintain and evolve the knowledge base over time.
Tech Stack
Technologies we use
Core
Tools
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.
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Regional
Delivery considerations for your region
Data and risk discovery (Germany)
Privacy, security, residency, and regulatory requirements differ by workflow. We document the applicable data flows, roles, retention needs, and control owners before recommending an architecture.
The resulting proposal lists the controls and evidence that are actually in scope. It is not a generic compliance, certification, or legal-assurance promise.
- Map data sources, destinations, roles, and sensitive fields
- Record access, retention, logging, and deletion requirements
- Identify required security or procurement evidence before contracting
- Use an NDA or DPA only when the parties mutually execute it
Working model (Germany)
Exact live-overlap hours, response expectations, meeting windows, and escalation contacts are confirmed in the proposal for each engagement.
Written decisions, scoped milestones, and asynchronous updates reduce unnecessary meetings without implying an unagreed service level.
- Proposal-specific overlap and meeting windows
- Named owners for decisions and blockers
- Written scope, assumptions, and change decisions
- Milestone cadence agreed before kickoff
Commercial setup (Germany)
The contracting entity, proposal currency, invoicing cadence, payment terms, intellectual-property terms, and required vendor documents are agreed before work begins.
The Opportunity Sprint can establish the evidence needed to scope a production pilot; it does not pre-commit either party to a rollout.
- Contracting entity and currency confirmed in writing
- Milestones and acceptance criteria defined in the proposal
- Vendor-document requirements identified before signature
- Scope changes require an explicit written decision
Delivery controls (Germany)
Testing, observability, release, security, and handover controls are selected for the actual system risk rather than promised as a generic bundle.
Acceptance measures and production responsibilities are recorded before implementation so both teams know what evidence will support release.
- Risk-based testing and acceptance measures
- Release, rollback, and observability responsibilities
- Security controls tied to the agreed threat model
- Handover artifacts defined in the signed scope
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.