AI Use Case
AI Knowledge Base Chatbot
Answer questions across policies, SOPs, and docs with grounded responses, citations, and permission-aware retrieval.
Problems
What’s slowing teams down
Common bottlenecks we see before AI workflows are implemented.
Slow knowledge retrieval
Teams waste time searching across scattered docs.
Inconsistent answers
Different people interpret SOPs and policies differently.
Permission requirements
Not all docs should be visible to all users.
No measurement loop
Without evals, you can’t reliably improve retrieval quality.
Delivery
What we deliver
Implementation-ready modules designed for reliability, safety, and real operations.
Grounded KB assistant
Answer from docs with citations and safe fallbacks.
Retrieval tuning
Chunking/metadata, hybrid search, and reranking when it helps.
Access-aware retrieval
Respect roles, tenants, and document-level permissions.
Evals + monitoring
Eval queries and KPIs to prevent drift.
Deliverables
What you’ll get
Concrete outputs designed for predictable handoff and measurable improvements.
Knowledge base ingestion pipeline
Retrieval tuning + metadata strategy
Chat UI + embed-ready delivery
Citations/excerpts and safe fallbacks
Optional permission-aware retrieval
Eval set + handoff documentation
Process
How we work
A pilot-first approach, with the quality and governance needed for production rollouts.
Ingest
Connect docs and build indexing.
Tune
Optimize retrieval for real queries.
Ship
Chat UX, citations, monitoring, rollout plan.
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.
Policy Q&A for support teams
SOP assistant for operations
Document comparison and summaries
Onboarding Q&A with permissions
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.
Pilot KB chatbot: $900–$3,500
Multi-source KB with permissions: $3,500–$12,000
Timelines
Delivery timelines
Common timelines for pilots and production hardening, depending on integrations and governance.
Pilot: 1–2 weeks
Multi-source rollout: 2–4 weeks
Risks
Risks & mitigation
The failure modes we design for so reliability and trust stay high.
Stale content
We define refresh cadence and monitor ingestion failures.
Weak grounding
We use citations/excerpts and safe fallbacks, plus eval sets for measurement.
AI Case Examples
Micro case studies (anonymous)
A few safe examples of outcomes we build for real operations—no client names, just results.
Secure Mobile Solution in Australian Defence Ecosystem
Problem: Secure data workflows were required in a regulated environment with strict access controls.
Solution: Hardened architecture with strict auth, encrypted storage, and audit-friendly engineering patterns.
Outcome: Deployed securely within a regulated ecosystem with clear handoff and operational guidance.
AI Knowledge Base Across 2,000+ Pages
Problem: Teams needed fast answers across long PDFs, but search was slow and results were inconsistent.
Solution: RAG with hybrid retrieval and reranking, plus grounded answers and safer fallback behavior.
Outcome: Reliable answers with <10s response times and measurable improvements on real queries.
Ops Automation with AI + n8n
Problem: Manual approvals and CRM syncing created delays and data inconsistencies across tools.
Solution: Event-driven automation with validation gates and AI-assisted classification where it improved routing.
Outcome: Reduced manual workload significantly with more reliable workflows and operator visibility.
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.
Related Services
Explore deeper implementations
When you need more depth than a pilot, these services cover full delivery.
Explore
More AI pages
Additional pillars and use cases to help you plan your roadmap.
FAQ
Frequently asked questions
Can we connect Notion/Confluence/Drive?
Often yes. We select connectors based on access controls and document formats.
Do you support citations?
Yes. We can include citations/excerpts and links back to sources for trust and debugging.
Can this respect permissions?
Yes. We can implement access-aware retrieval aligned with your auth model.
How do you improve accuracy over time?
We add eval queries, track failures, tune retrieval, and introduce reranking if it measurably helps.
Can we start with one doc set?
Yes. A small pilot is the best way to validate retrieval quality before expanding.
Will we own the code?
Yes. Source code and handoff notes are included.
Want an AI pilot for your workflow?
Start with a fixed-scope gig or request a tailored implementation plan for your systems.