AI Pillar
RAG Consulting
Design and improve RAG systems that stay accurate—better retrieval, safer fallbacks, and measurable evaluation loops.
Problems
What’s slowing teams down
Common bottlenecks we see before AI workflows are implemented.
Answers aren’t grounded
Assistants lose trust when responses don’t match docs, policies, and latest product information.
Retrieval isn’t measurable
Without eval queries and scorecards, tuning becomes guesswork and regressions slip in.
Ingestion is brittle
Docs change often and pipelines break unless monitoring and ownership are defined.
Permission models are ignored
Knowledge systems must respect roles and tenants to be safe for internal teams.
Delivery
What we deliver
Implementation-ready modules designed for reliability, safety, and real operations.
Ingestion + chunking strategy
Design chunking and metadata so retrieval is consistent and debuggable across sources.
Hybrid retrieval + reranking
Use hybrid search and reranking when it improves real queries measurably, then lock it in with evals.
Grounded answers with fallbacks
Citations/excerpts and “don’t know” behavior when evidence is weak—so the system stays honest.
Evaluation loop
Test sets, monitoring, and iteration routines so quality improves over time, not just at launch.
Deliverables
What you’ll get
Concrete outputs designed for predictable handoff and measurable improvements.
RAG architecture plan (sources, access, refresh cadence)
Ingestion pipeline + chunking/metadata strategy
Retrieval tuning (hybrid/rerank as needed)
Eval queries + scorecards for measurement
Citations/excerpts + safe fallback behavior
Handoff notes for continuous improvement
Process
How we work
A pilot-first approach, with the quality and governance needed for production rollouts.
Audit
Review pipeline, sources, and failure modes.
Tune
Improve chunking, metadata, retrieval, and prompts.
Measure
Add eval sets and regression checks.
Ship
Deploy improvements and document routines.
Stack
Suggested implementation stack
A practical stack we can adapt to your constraints and existing systems.
Automations
Example automations
A few workflows that usually deliver ROI quickly.
Knowledge base chatbot grounded in docs and policies
Doc comparison and summarization workflows
Hybrid search upgrade for better relevance
Permission-aware internal assistant for teams
Start Small
Start small in 7 days
Three pilot-friendly options that reduce risk and ship value fast. Choose one, share access, and we deliver a production-ready baseline.
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.
Pricing
Typical pricing ranges
We confirm scope before starting. These ranges help you plan a pilot versus a full rollout.
RAG audit + tuning sprint: $900–$3,500
New RAG MVP: $2,500–$8,000
Hybrid search + reranking upgrade: $2,000–$6,500
Timelines
Delivery timelines
Common timelines for pilots and production hardening, depending on integrations and governance.
Audit + tuning: 1–2 weeks
RAG MVP rollout: 2–4 weeks
Risks
Risks & mitigation
The failure modes we design for so reliability and trust stay high.
Stale or inconsistent knowledge
We define refresh cadence and monitoring so the system stays current as docs change.
Low retrieval quality
We tune chunking and metadata, then introduce hybrid search/reranking when it improves real queries measurably.
Permission and compliance gaps
We design access-aware retrieval aligned with your auth model and document permissions.
AI Case Examples
Micro case studies (anonymous)
A few safe examples of outcomes we build for real operations—no client names, just results.
Policy Q&A With Grounded Answers
Problem: Models guessed when evidence was weak and users lost trust.
Solution: RAG grounding with citations and safe “don’t know” fallbacks.
Outcome: More consistent answers and fewer escalations due to hallucinations.
Hybrid Retrieval Upgrade
Problem: Keyword search couldn’t capture intent and results were irrelevant.
Solution: Hybrid retrieval + reranking with an eval set to measure improvement.
Outcome: Better relevance with a repeatable quality loop.
Relevant Gigs
Start with a fixed-scope gig
Pick a gig to launch a pilot quickly with clear deliverables and timeline.
Compare
Decision guides
Quick comparisons to help you choose the right approach before building.
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When you need more depth than a pilot, these services cover full delivery.
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More AI pages
Additional pillars and use cases to help you plan your roadmap.
FAQ
Frequently asked questions
Can RAG eliminate hallucinations completely?
No system can guarantee zero errors. RAG reduces hallucinations by grounding answers in retrieved sources and enforcing safe fallbacks.
Can you connect multiple document sources?
Yes. PDFs, help centers, Drive/Notion/Confluence, websites, and databases—based on access controls and formats.
Do you support citations or source links?
Yes. We can include citations/excerpts and links back to sources when it improves trust and debugging.
How do you measure retrieval quality?
We build eval queries and scorecards, then track retrieval hit rate and answer quality across real user intents.
Can you handle permission-aware retrieval?
Yes. We can design per-user/per-role access rules aligned with your auth model and document permissions.
Can we start with a small proof of concept?
Yes. A fixed-scope RAG pilot is a common starting point before expanding scope.
Want an AI pilot for your workflow?
Start with a fixed-scope gig or request a tailored implementation plan for your systems.