AI Development
RAG Development Services
We build Retrieval-Augmented Generation (RAG) systems that answer from your documents—not guesses. Expect clean ingestion, tuned retrieval, citations, and an evaluation loop that improves accuracy over time. Delivery aligned to Canada teams (PRO).
Overview
What this service is
RAG combines search with LLM responses: it retrieves relevant source passages and then generates an answer grounded in those sources.
We engineer the full stack—connectors, chunking, embeddings, hybrid search, reranking, and access control—so retrieval is reliable in real conditions.
You get monitoring and evaluation tests so your team can iterate without breaking quality as content and prompts evolve.
Start with evidence
Begin with a focused opportunity sprint
In 1–2 weeks, we map one consequential operation, test its automation case, and define a governed implementation plan before a larger build.
Standard
AI delivery standard
Quality and safety practices we ship with AI builds so the system stays measurable, maintainable, and production-ready.
Logging + tracing
Conversation and tool traces with request IDs, error visibility, and debug-friendly runbooks.
Guardrails + safety
Tool allowlists, PII-safe patterns, refusal behavior, and escalation routes for edge cases.
Evals + regression tests
Golden queries, scorecards, and regression checks so quality improves over time instead of drifting.
Cost + latency controls
Caching, prompt discipline, retrieval tuning, and routing so your app stays fast and predictable at scale.
Documentation + handoff
Architecture notes, environment setup, and next-step roadmap so your team can iterate safely after launch.
Security-first integration
Secrets isolation, role-based access, audit-friendly actions, and minimal data retention by design.
Benefits
What you get
Lower hallucinations with source-grounded answers
Better retrieval quality via hybrid search + reranking
Safer UX with citations and low-confidence fallbacks
Freshness via incremental indexing pipelines
Permission-aware access for internal/tenant data
Measurable accuracy via eval sets and regression tests
Features
What we deliver
Ingestion pipelines
Connect PDFs, docs, wikis, websites, help centers, tickets, and databases with clean normalization and metadata.
Chunking + metadata strategy
Right-sized chunks with stable IDs, versioning, and filters so retrieval stays accurate as content changes.
Vector database setup
Schema, indexing, backups, and performance tuning for Pinecone, Qdrant, Weaviate, or pgvector based on your constraints.
Hybrid search + reranking
Combine keyword + semantic retrieval and rerank results to improve precision for exact terms, error codes, and product names.
Citations + excerpt UX
Answers include sources and highlighted passages so users can verify and drill into the original content.
Evaluation + monitoring
Golden queries, regression checks, retrieval metrics, and dashboards so quality improves continuously after launch.
Process
How we work
Discovery
Sources, permissions, query goals, and evaluation criteria.
Ingestion
Connectors, parsing, normalization, and metadata.
Retrieval
Embeddings, vector DB, hybrid search, and reranking.
Answer UX
Citations, excerpt UI, and fallback behavior.
Evals + launch
Regression tests, monitoring, and rollout plan.
Tech Stack
Technologies we use
Core
Tools
Services
Use Cases
Who this is for
Support knowledge base assistant
Answer tickets from docs and policies with citations and escalation when evidence is weak.
Internal SOP / runbook search
Search across internal procedures and knowledge while respecting access rules and audit needs.
Product docs + developer assistant
Explain APIs, error codes, and configuration with precise citations and up-to-date versioning.
Compliance / policy Q&A
Ground answers in official policy docs, track sources, and provide safe “I can’t confirm” fallbacks.
Sales enablement assistant
Find the right case studies, pricing rules, and product positioning fast—without misinformation.
Implementation Patterns
How we frame common AI workflows
Illustrative patterns only—not client case studies, endorsements, or production-result claims.
Regulated mobile data workflow pattern
Challenge: Sensitive data workflows need explicit access boundaries, traceability, and documented operating responsibilities.
Approach: Threat-model the workflow, map authorization rules, select encryption controls, and define auditable state transitions.
Validation: Test access boundaries and recovery paths, record residual risk, and obtain any required independent compliance assessment.
Large knowledge-base retrieval pattern
Challenge: Long, mixed-format source collections need traceable retrieval and safe behavior when evidence is weak.
Approach: Evaluate hybrid retrieval, reranking, citations, structured outputs, and defined fallback or human-review paths.
Validation: Use a representative offline evaluation set and report citation quality, latency, and cost under documented test conditions.
Operations automation pattern
Challenge: Approval and system-sync workflows need deterministic controls around exceptions, retries, and ownership.
Approach: Model the workflow, add validation and approval gates, and use AI only for bounded classification or extraction tasks.
Validation: Baseline manual steps, test exception paths and audit logs, then compare pilot measurements before considering wider rollout.
Explore
Related solutions & technologies
Useful next pages if you’re planning an AI pilot or scaling this into a larger product.
Related solutions
FAQ
Frequently asked questions
It depends on your constraints. Pinecone is managed and operationally simple, Qdrant/Weaviate are great for self-hosting, and pgvector is strong if you’re already on Postgres. We recommend based on scale, cost, and infra preferences.
Yes. We can implement per-user or per-tenant filtering, source-level permissions, and auth integration so retrieval respects access rules.
We improve retrieval quality, require citations/excerpts, add low-confidence fallbacks, and test with evaluation queries that reflect real user needs.
We build incremental indexing and scheduled refresh pipelines so new or updated docs are reflected without full re-ingestion.
Yes. Hybrid retrieval and reranking are often the biggest accuracy unlocks for production RAG systems.
A first production RAG system often ships in 3–6 weeks depending on sources, permissions, and UX complexity.
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Regional
Delivery considerations for your region
Data and risk discovery (Canada)
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 (Canada)
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 (Canada)
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 (Canada)
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
Want help with RAG development?
Book a service call with Canada timezone overlap (North America overlap). proposal currency confirmed before contracting.
We’ll review the context and reply with a practical next step.