01
Retrieval grounding
Answers tied to your sources instead of model imagination.
[ Platform service ]
AI Chatbots & Assistants — built properly, handed over completely.
Customer and team assistants grounded in your knowledge base, with lead qualification and clear human handoff rules.
[ What you get ]
5 deliverables. Nothing implicit.
- Conversation map
- Intents, questions, and handoff points mapped before any assistant is built.You own: The conversation map document
- Knowledge base setup
- Your docs, FAQs, and policies structured into a source the assistant can ground on.You own: A grounded knowledge base in your systems
- Assistant build
- The assistant configured with guardrails that keep it on task and honest.You own: The live assistant in your account
- CRM routing
- Lead qualification and human handoff rules wired to your CRM.You own: Verified lead routing in your CRM
- Quality review
- Evaluation against expected answers before and after launch.You own: An evaluation report and a review cadence
- Done means
- Done means the assistant answers from your documented sources, hands edge cases to a human by rule rather than guesswork, and evaluation shows it staying on task.
- Not included
- Model and API usage costs are yours; we do not publish an assistant without guardrails, and we do not promise resolution rates nobody can guarantee.
$3k–$15k
Typical focused build, fixed price
3–6
Weeks from kickoff to handover, typical
100%
Owned by you at handover
[ How it works ]
The ai chatbots & assistants engagement, phase by phase.
- Discover & scope
- We map how ai chatbots & assistants fits into your current stack, then agree a written scope with the acceptance criteria attached before work begins.
- Plan & architect
- The implementation plan records the structure, boundaries, and integration points, so build decisions are documented rather than improvised.
- Build & integrate
- Implementation covers conversation map and knowledge base setup in reviewable increments against the agreed plan.
- Verify & hand over
- We verify the acceptance checks, close the engagement out with quality review, and hand over documentation your team can operate without us.
[ Capabilities ]
The surface area of a ai chatbots & assistants build.
The platform and discipline surface we work across — what an engagement can cover when the scope calls for it.
02
Guardrails
Topic boundaries and refusal rules written down and enforced.
03
Prompt & system design
System prompts versioned like code, not vibes.
04
Handoff rules
Human escalation criteria agreed with your team.
05
Lead qualification
Qualifying questions mapped to CRM fields.
06
Evaluation & evals
Response sets tested against expected outcomes before launch.
07
Channel deployment
Web, messaging-app, and internal surfaces where your users are.
08
Conversation analytics
Transcripts and drop-off points reviewed on a cadence.
09
Knowledge maintenance
An update routine so the assistant stays truthful as docs change.
[ Field notes ]
How we think about ai chatbots & assistants.
- What AI Chatbots and Assistants Deliver
- AI chatbot, agent, and Copilot builds provide customer and team assistants grounded in your knowledge base. The assistants answer from your own content rather than generic model knowledge. Every assistant ships with lead qualification and clear human handoff rules, so conversations resolve automatically when they can and escalate when they should.
- Who Should Use a Grounded Assistant
- This is for customer-facing teams that want fast, consistent answers built from their existing knowledge base, and for internal teams that want a Copilot for their own documentation. It suits organizations that want to qualify leads at scale and pass only ready conversations to human staff.
- The Deliverables Package
- Deliverables include a conversation map, knowledge setup, CRM routing, and quality review. The conversation map defines how a conversation starts, flows, and resolves. Knowledge setup connects your content to the assistant so grounded responses come from your sources.
- Discovery and the Conversation Map
- Discovery begins with the conversation map, laying out the user intents, the questions they will ask, and the responses the assistant should give. The map also captures where lead qualification begins and where a human takes over. From the map, we decide the right shape: a simple AI chatbot, a fuller agent, or a Copilot embedded in your team's tools.
- Grounding Responses in a Knowledge Base
- Grounded responses rely on a RAG pipeline: embeddings turn content into vectors, a vector store holds them, retrieval finds the relevant pieces, and grounded generation builds the answer. This keeps responses tied to your knowledge base. Knowledge setup prepares and organizes the content the retrieval step will search.
- Prompt Design and Context Architecture
- Prompt design and context architecture control how the model is instructed and what context it sees for each conversation. Good architecture keeps prompts focused and responses on-topic. The context assembled for each turn is designed to fit within token budgets while carrying what the answer needs.
- Lead Qualification and CRM Routing
- Lead qualification turns conversation data into a qualification decision based on your criteria. Qualified leads are sent to the CRM through CRM routing rules. Unqualified or undecided conversations follow defined next steps rather than disappearing.
- Human Handoff Rules
- Clear human handoff rules define exactly when a conversation moves from the assistant to a person. The assistant hands over the conversation context so the human can continue without repeating questions. Handoff criteria are part of the conversation map, so escalation is predictable and consistent.
- Agentic Workflows, Tools, and Memory
- For more advanced builds, n8n's AI Agent node uses tools and memory to complete multi-step work. Current n8n behavior is centered on the Tools Agent model, so the workflow makes each tool call and handoff explicit instead of hiding action logic. MCP servers connect the assistant to external tools and data using the Model Context Protocol.
- Guardrails and Evaluation
- Guardrails keep the assistant on task, grounded in the knowledge base, and out of unsupported territory. Evaluation with evals checks responses against expected outcomes before launch. Quality review is part of the deliverables and runs continuously as the assistant is refined.
- Launch and Cost Control
- Launch goes through LLM APIs such as the OpenAI Responses API and the Anthropic Messages API. Token usage and cost controls keep running costs predictable as conversation volume grows. Prompt and context design, together with token budgeting, keep each conversation within plan.
- Ongoing Care and Related Services
- Ongoing care covers knowledge base refreshes, guardrail updates, and re-evaluation as your content changes. The assistant is re-grounded whenever the underlying knowledge base is updated. Related services include AI API integrations that connect models, business systems, webhooks, and operational workflows on top of this assistant foundation.
[ Common questions ]
Grounded responses come from a RAG pipeline where embeddings, a vector store, retrieval, and grounded generation work together. The assistant answers from your knowledge base, not generic model knowledge.
Builds work with LLM APIs such as the OpenAI Responses API and the Anthropic Messages API. The right API is chosen for your use case and cost constraints.
Lead qualification scores conversations against your criteria, and CRM routing sends qualified leads to the CRM. Human handoff rules define when a person takes over.
Guardrails keep the assistant grounded and on-task by restricting what it can answer and when it can act. Evaluation with evals verifies responses against expected outcomes.
Token usage and cost controls are designed into the prompt and context architecture. Budgets keep each conversation predictable as volume grows.
[ Industry examples ]
AI & Automation work by other teams.
Industry case studies in this practice area — the problem, the build, and the results, credited to the agency that delivered each one.
AI & AutomationARC Europe · 2026
LLM chatbot routes stranded drivers in 2-4 questions
2-4questions to reach the right service
By NetguruLLM chatbot
AI & AutomationMattioli Woods · 2026
Automated reconciliation saves 14,000+ staff hours a year
14,000+staff hours saved annually
By STX NextProcess automation
AI & AutomationDisasters Emergency Committee · 2026
An AI proofreader to speed up appeal content at the Disasters Emergency Committee
100%pilot participants who found the tool valuable
By TorchboxAI literacy and organisational readiness
[ Sibling platforms ]
Other ways we build in ai & automation.
01
n8n Automation Services
Self-hosted and cloud n8n workflows using nodes, webhooks, credentials, retries, and integrations for dependable operations.
02
AI API Integrations
AI API and data pipeline integrations that connect models, business systems, webhooks, and operational workflows, with fine-tuning considered only where the provider and account support it.
[ Field guides ]
Go deeper on ai chatbots & assistants.
01
AI Chatbot Buying Guide: 12 Questions to Ask
Twelve questions that separate a demo chatbot from an operable assistant: knowledge sources, escalation, guardrails, analytics, ownership, and pricing.
02
RAG Explained: AI Answers from Your Own Docs
RAG lets an AI answer from your documents with citations. Learn how chunking, retrieval, permissions, and freshness decide whether it works.
[ Next step ]
Start with a scoped proposal. Price attached.
contact@nexsumlabs.tech — we reply within one business dayBack to AI & Automation