Savo SalesBot
Talks to every visitor the moment they arrive, answers product questions, scores intent and books meetings straight into your CRM.
We need a customer portal connected to our CRM.
Web applications · API integrations
Use the relevant information. Keep missing context visible.
Start with a web application and a scoped CRM integration. Confirm users, existing tools and the first workflow before estimating.
A consultant reviews scope and follows up.
A focused role, connected to your work.
SalesBot answers product questions from your approved knowledge base, scores intent against your ideal customer profile, and books qualified meetings directly into your reps' calendars. Unqualified traffic is nurtured instead of lost.
Try SalesBot. See what it picks up.
The existing demo uses a deterministic conversation engine - not a live LLM or CRM connection. Its extracted fields and sample handoff change as you talk.
Use example project information. Don’t enter confidential or personal details. This demo does not send an enquiry.
Savo SalesBot · live demo
deterministic engine · session new
Agent telemetry · live
Extracted slots
- Need
- awaiting input…
- Budget
- awaiting input…
- Timeline
- awaiting input…
- Services
- awaiting input…
Slots fill as the conversation progresses - watch them change with each message.
Handoff brief
When qualification completes, SalesBot assembles the structured brief a CRM receives on the live deployment: need, matched services, budget band, timeline and the lead score, with the full transcript attached.
Understand the demo’s working steps
SalesBot recognises intent, extracts requirements and gathers need, budget and timeline. It matches the service catalogue and prepares a structured handoff. The score is a demo rule, not a prediction of conversion. Production integrations, approvals and model behavior would be defined and tested separately.
A useful scope, not unlimited autonomy.
These capabilities define the starting conversation. Sources, integrations and permissions are agreed for your workflow.
- Trained on your product docs, pricing and FAQs
- Lead scoring against your ideal customer profile
- Calendar booking with routing to the right rep
- Full conversation transcripts synced to your CRM
Useful intelligence needs clear boundaries.
The model is only one part of the system. Explore the information, permissions and review paths around it.
Relevant information
Select useful sources, respect access and make the supporting context available. Missing or stale information should be visible, not silently filled in.
Bounded actions
Specify what the system may read, prepare or change. Sensitive actions need the right permission and, where appropriate, a person's approval.
Evaluation & fallback
Test routine questions, incomplete inputs and difficult cases. Define when to ask, stop, retry or hand off rather than assuming every answer is correct.
An operating plan
Agree who reviews usage, handles incidents and maintains prompts, data or integrations. Monitoring, cost controls and review records depend on the deployment.
AI is not always the answer.
Clear rules may be simpler. Thin information may need attention first. For sensitive decisions, AI can prepare context while a qualified person decides.
Explore AI strategyA clear path from idea to operation.
Start with a focused scope. Make the information, evaluation and operating responsibilities part of the work.
Scope the job
Define the workflow, the information it needs and the actions it is allowed to take.
Connect the knowledge
Prepare retrieval, integrations and access around your actual systems.
Test the boundaries
Evaluate useful answers, failure cases, approval steps and human handoff.
Launch with supervision
Start with agreed limits, review real usage and adjust the system with evidence.
Illustrative delivery stages. Scope and timing are agreed for your project.
Explore another role.
Different tasks need different information, tools and review paths.
A few things worth asking.
Practical answers about scope, information and what happens after the first build.
Timing depends on the workflow, the state of your information and the integrations involved. We define the first useful scope and the evaluation work before agreeing a delivery plan.
That depends on the agreed architecture and model providers. We review hosting, access, retention and provider terms with you. A private deployment may be appropriate, but data-location and confidentiality requirements need to be confirmed in the project scope.
We define permitted reads and actions, approval steps, escalation rules and review records. Sensitive actions can require a person to approve them. These boundaries are tested and revised with the team that operates the agent.
They can be connected within a workflow, with a defined responsibility and permission boundary for each part. For a straightforward task, one agent or a rule-based automation may be a better starting point.
No. When the rules are clear and the inputs are consistent, an integration or rule-based automation may be simpler. An agent is worth considering when a task needs language understanding, retrieval or tool selection, with a suitable review path.
The workflow should have an agreed fallback: ask for more information, stop an action, use a simpler route or hand over to a person. AI can still produce incorrect answers, so testing and ongoing review are part of the work.
AI project questions
Start with the work you want to change.
Tell us the task, the systems involved and where your team needs help. We’ll work out whether AI, automation or a simpler approach fits.