01
Layer of knowledge
Content ready to search and quote.
servicesPackagesFAQcase studies
AI and sales advice
AI sales and technical advisor with sources
Controlled advisor with approved knowledge, diagnosis, brief and human handoff.
Solution mapSmartCodeIT system view
Service CatalogPackages and pricingFAQ and training
Controlled AI advisor
Validation, logs and exception checking
RecommendationClient briefHandoff to man
What is blocking the process?
A generic chat answer does not qualify needs or cite approved sources.
How do we organize our work?
The advisor detects intent, asks for missing context and prepares a consultant brief.
What does this scenario mean for the company?
The AI advisor with sources, diagnosis and transfer to the consultant is a verified design of his own SmartCodeIT. The starting point was a specific problem: The first version of the advisor responded to a too rigid scheme and could not reliably match the service, sources and next step to the customer's situation. The performed range includes the working solution, tests and supporting materials described: full flow from the client's question to the diagnosis, brief and hand over to the consultant. A similar project for the client still requires a separate analysis of the process, data and limitations.
Decision context
When does such a system make sense?
This option makes sense when an advisor should not only respond, but also recognize the intention, explain the recommendation, build a brief and communicate a conversation with the full context.
Best match
Companies that want to use AI in sales or customer service without losing control of sources and decisions.
Signals to be analyzed
- bot answers are template or inconsistent
- lack of quoting sources and control of knowledge
- Lead goes to a consultant without diagnosis and history of conversation
Solution architecture
The system is divided into clear layers of responsibility.
Each layer has its own data, rules and control points. This allows you to develop the solution in stages without mixing user interface, process logic and integration.
02
AI Orchestration
Intentions, rags, memory and response rules.
routingretrievalguardrailsfallback
03
Tools
Functions only run within the permitted range.
briefleademailCRM
04
Control
Quality, logs, feedback and escalation.
citationsratingshandoffmonitoring
How can this be built technically?
Technically, such a system can be built as a combination of application layer, automation and data integration. In this scenario, the key elements are Next.js, Vercel AI Gateway, MAG, Postgres. Workflow includes: Client describes the problem -> The system recognizes intent and missing data -> RAG selects approved sources -> The Advisor prepares a recommendation and brief. Implementation requires field mapping, validation of data, handling of errors, history of operations, permissions and monitoring to make the process stable after production startup.
System elements
Next.jsVercel AI GatewayRAGPostgresResend
Scope of first implementation
Conversation widgetKnowledge Base and RAGDiagnosis engineBrief and handoffQuality panel and feedback
Operational flow
From input event to controlled result.
The process is designed so that each step has a status, owner, and predictable error handling.
- 01Stage 1Customer describes the problem
Conversation widget
- 02Stage 2System recognizes intent and missing data
Knowledge Base and RAG
- 03Stage 3MAG selects approved sources
Diagnosis engine
- 04Stage 4Advisor prepares recommendation and brief
Brief and handoff
- 05Stage 5The call may be forwarded to the consultant
Quality panel and feedback
Operational change
What should change after implementation?
These are target process changes, not a guarantee of business outcome. The actual impact depends on data, scale, integration and how the team works.
Current state
General talk scheme
Target state
Recommendation based on intentions and sources
Current state
Lead without context
Target state
Diagnosis, missing data and ready brief
Current state
No apparent escalation
Target state
Guardrails and handover to man
Control and security
Risks are part of the project, not a post-implementation note.
Automation should stop or escalate a case when data is incomplete, integration returns an error, or a decision requires human responsibility.
Risk
Hallucination or response from outside the offer
Control mechanism
Retrieval only from approved sources and openly quoted.
Risk
Automatic decision on high risk
Control mechanism
Human improvement and mandatory handoff for exceptions.
Risk
Fall in quality after change of model or content
Control mechanism
Evaluation dataset, feedback and regression response tests.
Recommended starting point
Pilotage of one advisory scenario
We measure the accuracy of intentions, the quality of sources, the completeness of the brief and the percentage of cases requiring man.
Approximate time2-4 weeks
One group of intentionsvalidated knowledge basecitationsbrief and handoff
Related paths
Services matching this scenario
Implementation FAQ
Questions worth asking before pricing.
The answers describe a safe technical option. The exact scope depends on your company's systems, data and exceptions.
Does AI's advisor answer exclusively from the SmartCodeIT database?
In this option, the offer answers are built on approved sources. A lack of information should lead to a clarification question or a referral to the consultant.
Can AI send an offer on his own?
It can prepare a sketch and collect data, but shipping, custom price or trade obligation should be subject to human rules and control.
How to measure the quality of such an advisor?
By accurate recognition of intentions, coverage with sources, assessment of answers, completeness of brief, handoff rate and conversion of conversations to consultations.
Related projects
See scenarios with similar architecture.
The selection is based on common services and system elements, thanks to which subsequent examples develop the topic rather than creating a random list.
Next step
Do you have a similar process, but different systems and exceptions?
Describe the current workflow, data sources, and where the process stops. The first conversation is used to assess whether the right start is an audit, integration, MVP or a ready-made tool.
Contact
Describe the process you want to improve.
A few concrete sentences are enough for us to suggest an audit, automation, an AI agent, a web application or a systems integration.
A reply with a recommended next stepThe option to begin with an audit or a focused MVPA solution designed for security and maintainability
Prefer a structured form? Book a free consultationkontakt@smartcodeit.pl882 121 238ul. Częstochowska 12/2, Gliwice