Answers need approved sources
The assistant should answer from policies, docs, product data, CRM records, or approved knowledge, not model memory.
02 AI assistant development
AI assistants built around real handoff paths.
Assistants that answer, qualify, route, and support real work. We connect answers, lead capture, routing, CRM updates, and review queues into one production workflow.
SourcesApproved answers
24/7Coverage
CRMLead handoff
RepliesCommon questions
What we build
Assistant systems for repeat customer and staff questions.
Capabilities
What the assistant actually handles.
01
Website and product assistants grounded in approved content
02
Lead capture, scoring, routing, and CRM handoff
03
Internal knowledge bots for SOPs, policies, and onboarding
04
WhatsApp, Telegram, Slack, and Teams assistant workflows
05
Human handoff queues with transcript and source context
06
Conversation analytics for gaps, failed answers, and next actions
AI foundation
What keeps the assistant from becoming a risky chat widget.
01
Grounded Answers
Approved pages, docs, FAQs, and product records are indexed with source metadata so the assistant answers from real business content.
02
Action Routing
Qualified leads, support requests, bookings, and internal tasks move into the correct CRM, queue, inbox, or staff handoff path.
03
Review Controls
Blocked topics, escalation rules, transcript review, and confidence checks keep the assistant useful without giving up human control.
Before we build
What we need before the assistant goes live.
01
Question set
The repeated questions customers or staff ask today, plus the exact answers that are already approved.
02
Source content
The pages, docs, FAQs, product records, policies, or knowledge base the assistant is allowed to use.
03
Allowed actions
What the assistant can do after answering: capture a lead, book a call, create a ticket, send a summary, or update a CRM.
04
Handoff rules
When the assistant stops answering and sends the transcript, source, reason, and next step to a person.
05
Review owner
Who checks failed answers, lead quality, escalations, and conversation gaps after launch.
06
Success metric
What proves the assistant is useful: fewer repeated questions, faster response, better lead capture, or fewer missed handoffs.
Build choices
The product layer behind a useful assistant.
01
User interface
The assistant needs a place to live: website widget, internal panel, product screen, or team inbox. We keep the UI simple enough for users to ask, review, and escalate without training.
Next.jsWeb chatInternal panels
02
Business data
Answers come from approved sources: FAQs, policies, product records, documents, CRM fields, or support history. We define what the assistant can use before it answers customers or staff.
PostgreSQLVector searchSource metadata
03
Conversation logic
The model is only one part. We design intents, blocked topics, confidence checks, tool calls, and handoff rules so the assistant knows when to answer and when to route.
OpenAIClaudeGemini
04
System actions
Useful assistants do more than reply. They create CRM notes, route leads, book meetings, open tasks, send summaries, and update the systems your team already uses.
CRMCalendarWebhooks
05
Review and reporting
After launch, managers need to see unanswered questions, weak answers, escalations, lead quality, and repeated gaps. That feedback loop is how the assistant improves.
LogsReview queuesAnalytics
06
Deployment path
We ship the assistant with API keys, environment setup, monitoring, documentation, and ownership transfer so it does not stay trapped in a developer account.
VercelAWSHandoff
Shipped system
What makes an assistant safe enough to use.
We build assistants with grounded answers, CRM handoffs, human review, logs, and production controls so the workflow can be trusted after launch.
View service →Shipped system
What makes an assistant safe enough to use.
01
Answers grounded in approved pages, docs, and FAQs
02
Lead capture, qualification, and CRM handoff
03
Human takeover with transcript and page context
Workflow signals
How to tell if an assistant is the right first build.
Handoff is part of the product
Every uncertain request needs an owner, reason, transcript, and next step so staff can continue without losing context.
Lead capture needs structure
Useful assistants produce fields teams can use: intent, urgency, contact details, source, status, and routing notes.
Production means logs
Teams need unanswered questions, failed routes, escalation history, and conversation quality visible after launch.
Why us
Why teams bring us in for assistant builds.
01
Workflow-first assistant design, not generic chat widgets
02
Source-grounded answers with retrieval, metadata, and fallbacks
03
CRM, calendar, inbox, and dashboard integrations built into the flow
04
Human escalation paths for sensitive or uncertain conversations
05
Analytics for unanswered questions, lead quality, and staff handoff outcomes
06
Model flexibility across hosted and local deployments based on privacy and latency
Questions
Questions, answered clearly.
Short answers for teams deciding how a chatbot should answer, escalate, update systems, and stay controlled.
Q1
Can the assistant answer from our own content?
Yes. We connect it to your website, documents, FAQs, product data, or internal knowledge base, then define what it can answer and what should be escalated.
Q2
Will it replace our support or sales team?
No. The best version handles repeat questions, captures context, and routes the right work to people. The goal is less repetitive work, not less control.
Q3
Can it update our CRM?
Yes. We can log conversations, create contacts, update lead fields, trigger tasks, and push handoff notes into tools like HubSpot, Salesforce, Pipedrive, or a custom CRM.
Next step
Put one assistant workflow into production.
Send the repeated question, lead flow, or internal knowledge workflow. We will show the first useful assistant build.