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
Representative outputs for planning. The exact deliverables, ownership, and handoff commitments are defined in the signed scope.
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 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.
Engagement
A deliberate path from evidence to production
The sprint is the fixed entry offer. Pilot and rollout ranges are planning bands; exact scope, price, and commitments are confirmed in a signed proposal.
$3k–$5k Automation Opportunity Sprint
$15k–$25k production pilot after scope validation
$25k–$50k+ rollout and integration after pilot evidence
Timelines
Scope before committing the calendar
Only the opportunity sprint has a standard delivery window. Larger timelines depend on systems, data access, controls, and acceptance criteria.
Opportunity Sprint: 1–2 weeks
Pilot timeline: confirmed from integrations, risk, and acceptance criteria
Rollout timeline: confirmed after pilot evidence and stakeholder planning
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.
Implementation Patterns
How we frame common AI workflows
Illustrative patterns only—not client case studies, endorsements, or production-result claims.
Policy question-answering pattern
Challenge: Policy answers need source evidence and an explicit response when support is insufficient.
Approach: Use access-aware retrieval, citations, confidence rules, and a safe fallback.
Validation: Evaluate supported, unsupported, conflicting, and permission-restricted questions before launch.
Hybrid retrieval evaluation pattern
Challenge: Search quality needs a representative evaluation set before architecture choices are justified.
Approach: Compare keyword, vector, hybrid, and reranked retrieval using the same query set.
Validation: Record relevance, citation, latency, and cost results under documented test conditions.
First engagement
Start with the Automation Opportunity Sprint
One premium entry point keeps the decision focused on business value, operational risk, and a credible production path.
Compare
Decision guides
Quick comparisons to help you choose the right approach before building.
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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.
Ready to identify the right automation opportunity?
Start with the $3k–$5k Opportunity Sprint and leave with an evidence-backed implementation decision.